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Udacity - Machine Learning Engineer Nanodegree nd009t v1 0 0
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Part 04-Module 03-Lesson 01_Feature Scaling (Size: 5.37 GB) (Files: 4260)
Part 04-Module 03-Lesson 01_Feature Scaling
04. Sarah's Height + Weight-p5p3OLARpmA.en.vtt
04. Sarah's Height + Weight-p5p3OLARpmA.pt-BR.vtt
04. Sarah's Height + Weight-p5p3OLARpmA.zh-CN.vtt
08. Feature Scaling Formula Quiz 2-vmIK4jpUtNo.en.vtt
04. Sarah's Height + Weight-p5p3OLARpmA.ar.vtt
08. Feature Scaling Formula Quiz 2-vmIK4jpUtNo.pt-BR.vtt
08. Feature Scaling Formula Quiz 2-vmIK4jpUtNo.zh-CN.vtt
08. Feature Scaling Formula Quiz 2-vmIK4jpUtNo.ar.vtt
02. A Metric for Chris-Thj7e55iSlA.en.vtt
03. Height + Weight for Cameron--dT9dztM-Lc.en.vtt
02. A Metric for Chris-Thj7e55iSlA.pt-BR.vtt
03. Height + Weight for Cameron--dT9dztM-Lc.pt-BR.vtt
04. Sarah's Height + Weight-OdsfV143AMc.en.vtt
08. Feature Scaling Formula Quiz 2-J6RyUyWxrM4.zh-CN.vtt
04. Sarah's Height + Weight-OdsfV143AMc.zh-CN.vtt
07. Feature Scaling Formula Quiz 1-sPqs7DoBkXQ.zh-CN.vtt
03. Height + Weight for Cameron--dT9dztM-Lc.ar.vtt
04. Sarah's Height + Weight-OdsfV143AMc.pt-BR.vtt
07. Feature Scaling Formula Quiz 1-sPqs7DoBkXQ.pt-BR.vtt
04. Sarah's Height + Weight-OdsfV143AMc.ar.vtt
07. Feature Scaling Formula Quiz 1-sPqs7DoBkXQ.en.vtt
03. Height + Weight for Cameron-MetxO9LDp-I.en.vtt
03. Height + Weight for Cameron-MetxO9LDp-I.zh-CN.vtt
02. A Metric for Chris-Thj7e55iSlA.ar.vtt
08. Feature Scaling Formula Quiz 2-J6RyUyWxrM4.en.vtt
03. Height + Weight for Cameron-MetxO9LDp-I.pt-BR.vtt
08. Feature Scaling Formula Quiz 2-J6RyUyWxrM4.pt-BR.vtt
03. Height + Weight for Cameron-MetxO9LDp-I.ar.vtt
07. Feature Scaling Formula Quiz 1-sPqs7DoBkXQ.ar.vtt
08. Feature Scaling Formula Quiz 2-J6RyUyWxrM4.ar.vtt
01. Chris's T-Shirt Size (Intuition)-l6YXxmCNtHk.zh-CN.vtt
05. Chris's Shirt Size by Our Metric-e83ZS4VqGZ0.zh-CN.vtt
10. MinMax Rescaler Coding Quiz-xTEkF0voyoM.zh-CN.vtt
05. Chris's Shirt Size by Our Metric-e83ZS4VqGZ0.en.vtt
10. MinMax Rescaler Coding Quiz-xTEkF0voyoM.en.vtt
01. Chris's T-Shirt Size (Intuition)-l6YXxmCNtHk.en.vtt
01. Chris's T-Shirt Size (Intuition)-l6YXxmCNtHk.pt-BR.vtt
05. Chris's Shirt Size by Our Metric-oWyt6md7P44.zh-CN.vtt
05. Chris's Shirt Size by Our Metric-e83ZS4VqGZ0.pt-BR.vtt
10. MinMax Rescaler Coding Quiz-xTEkF0voyoM.pt-BR.vtt
05. Chris's Shirt Size by Our Metric-oWyt6md7P44.en.vtt
09. Feature Scaling Formula Quiz 3-iY_sO4d23gY.zh-CN.vtt
01. Chris's T-Shirt Size (Intuition)-l6YXxmCNtHk.ar.vtt
05. Chris's Shirt Size by Our Metric-oWyt6md7P44.pt-BR.vtt
05. Chris's Shirt Size by Our Metric-e83ZS4VqGZ0.ar.vtt
09. Feature Scaling Formula Quiz 3-iY_sO4d23gY.en.vtt
10. MinMax Rescaler Coding Quiz-xTEkF0voyoM.ar.vtt
10. MinMax Rescaler Coding Quiz-ePXAzoGVviM.zh-CN.vtt
09. Feature Scaling Formula Quiz 3-iY_sO4d23gY.pt-BR.vtt
05. Chris's Shirt Size by Our Metric-oWyt6md7P44.ar.vtt
02. A Metric for Chris-O0bvLU4l0is.zh-CN.vtt
02. A Metric for Chris-O0bvLU4l0is.en.vtt
10. MinMax Rescaler Coding Quiz-ePXAzoGVviM.en.vtt
09. Feature Scaling Formula Quiz 3-iY_sO4d23gY.ar.vtt
10. MinMax Rescaler Coding Quiz-ePXAzoGVviM.pt-BR.vtt
02. A Metric for Chris-O0bvLU4l0is.pt-BR.vtt
02. A Metric for Chris-O0bvLU4l0is.ar.vtt
07. Feature Scaling Formula Quiz 1-jOxS1eJRsOk.zh-CN.vtt
12. Quiz on Algorithms Requiring Rescaling-ntRkOeSZutw.zh-CN.vtt
10. MinMax Rescaler Coding Quiz-ePXAzoGVviM.ar.vtt
09. Feature Scaling Formula Quiz 3-bY2fuRkH3iw.zh-CN.vtt
07. Feature Scaling Formula Quiz 1-jOxS1eJRsOk.en.vtt
07. Feature Scaling Formula Quiz 1-jOxS1eJRsOk.pt-BR.vtt
12. Quiz on Algorithms Requiring Rescaling-ntRkOeSZutw.en.vtt
12. Quiz on Algorithms Requiring Rescaling-ntRkOeSZutw.pt-BR.vtt
09. Feature Scaling Formula Quiz 3-bY2fuRkH3iw.en.vtt
09. Feature Scaling Formula Quiz 3-bY2fuRkH3iw.pt-BR.vtt
07. Feature Scaling Formula Quiz 1-jOxS1eJRsOk.ar.vtt
12. Quiz on Algorithms Requiring Rescaling-ntRkOeSZutw.ar.vtt
09. Feature Scaling Formula Quiz 3-bY2fuRkH3iw.ar.vtt
01. Chris's T-Shirt Size (Intuition)-oaqjLyiKOIA.zh-CN.vtt
01. Chris's T-Shirt Size (Intuition)-oaqjLyiKOIA.en.vtt
01. Chris's T-Shirt Size (Intuition)-oaqjLyiKOIA.pt-BR.vtt
06. Comparing Features with Different Scales-PRL8trOU7Rs.zh-CN.vtt
01. Chris's T-Shirt Size (Intuition)-oaqjLyiKOIA.ar.vtt
06. Comparing Features with Different Scales-PRL8trOU7Rs.en.vtt
06. Comparing Features with Different Scales-PRL8trOU7Rs.pt-BR.vtt
12. Quiz on Algorithms Requiring Rescaling-oEhevl5DWpk.zh-CN.vtt
12. Quiz on Algorithms Requiring Rescaling-oEhevl5DWpk.pt-BR.vtt
12. Quiz on Algorithms Requiring Rescaling-oEhevl5DWpk.en.vtt
06. Comparing Features with Different Scales-PRL8trOU7Rs.ar.vtt
index.html
12. Quiz on Algorithms Requiring Rescaling-oEhevl5DWpk.ar.vtt
11. MinMax Scaler in sklearn-lgoh5R05YM0.zh-CN.vtt
11. MinMax Scaler in sklearn-lgoh5R05YM0.pt-BR.vtt
11. MinMax Scaler in sklearn-lgoh5R05YM0.en.vtt
11. MinMax Scaler in sklearn.html
06. Comparing Features with Different Scales.html
02. A Metric for Chris.html
03. Height + Weight for Cameron.html
04. Sarah's Height + Weight.html
07. Feature Scaling Formula Quiz 1.html
08. Feature Scaling Formula Quiz 2.html
05. Chris's Shirt Size by Our Metric.html
01. Chris's T-Shirt Size (Intuition).html
09. Feature Scaling Formula Quiz 3.html
11. MinMax Scaler in sklearn-lgoh5R05YM0.ar.vtt
12. Quiz on Algorithms Requiring Rescaling.html
10. MinMax Rescaler Coding Quiz.html
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08. Feature Scaling Formula Quiz 2-J6RyUyWxrM4.mp4
08. Feature Scaling Formula Quiz 2-vmIK4jpUtNo.mp4
04. Sarah's Height + Weight-OdsfV143AMc.mp4
04. Sarah's Height + Weight-p5p3OLARpmA.mp4
03. Height + Weight for Cameron-MetxO9LDp-I.mp4
07. Feature Scaling Formula Quiz 1-sPqs7DoBkXQ.mp4
10. MinMax Rescaler Coding Quiz-xTEkF0voyoM.mp4
02. A Metric for Chris-Thj7e55iSlA.mp4
03. Height + Weight for Cameron--dT9dztM-Lc.mp4
01. Chris's T-Shirt Size (Intuition)-l6YXxmCNtHk.mp4
05. Chris's Shirt Size by Our Metric-e83ZS4VqGZ0.mp4
05. Chris's Shirt Size by Our Metric-oWyt6md7P44.mp4
10. MinMax Rescaler Coding Quiz-ePXAzoGVviM.mp4
09. Feature Scaling Formula Quiz 3-iY_sO4d23gY.mp4
12. Quiz on Algorithms Requiring Rescaling-ntRkOeSZutw.mp4
09. Feature Scaling Formula Quiz 3-bY2fuRkH3iw.mp4
07. Feature Scaling Formula Quiz 1-jOxS1eJRsOk.mp4
02. A Metric for Chris-O0bvLU4l0is.mp4
01. Chris's T-Shirt Size (Intuition)-oaqjLyiKOIA.mp4
06. Comparing Features with Different Scales-PRL8trOU7Rs.mp4
12. Quiz on Algorithms Requiring Rescaling-oEhevl5DWpk.mp4
11. MinMax Scaler in sklearn-lgoh5R05YM0.mp4
Part 04-Module 04-Lesson 01_PCA
10. Practice Finding Centers-FZVBF1HR4U0.pt-BR.vtt
10. Practice Finding Centers-FZVBF1HR4U0.en.vtt
10. Practice Finding Centers-FZVBF1HR4U0.zh-CN.vtt
10. Practice Finding Centers-FZVBF1HR4U0.ar.vtt
02. Trickier Data Dimensionality--dcNhrSPmoY.zh-CN.vtt
02. Trickier Data Dimensionality--dcNhrSPmoY.en.vtt
11. Practice Finding New Axes-th34aboBOO0.en.vtt
11. Practice Finding New Axes-th34aboBOO0.pt-BR.vtt
08. Principal Axis of New Coordinate System-qPr3Uj55eog.zh-CN.vtt
02. Trickier Data Dimensionality--dcNhrSPmoY.ar.vtt
02. Trickier Data Dimensionality--dcNhrSPmoY.pt-BR.vtt
11. Practice Finding New Axes-th34aboBOO0.ar.vtt
08. Principal Axis of New Coordinate System-qPr3Uj55eog.en.vtt
08. Principal Axis of New Coordinate System-qPr3Uj55eog.pt-BR.vtt
01. Data Dimensionality-bAZJT4xHiXM.zh-CN.vtt
24. Maximum Number of PCs Quiz-oOUx6NHppdQ.zh-CN.vtt
07. Center of a New Coordinate System-1ask5zHGQKM.zh-CN.vtt
07. Center of a New Coordinate System-1ask5zHGQKM.pt-BR.vtt
01. Data Dimensionality-bAZJT4xHiXM.en.vtt
03. One-Dimensional, or Two-QsncWsyboFk.zh-CN.vtt
03. One-Dimensional, or Two-QsncWsyboFk.pt-BR.vtt
03. One-Dimensional, or Two-QsncWsyboFk.en.vtt
07. Center of a New Coordinate System-1ask5zHGQKM.en.vtt
18. Maximal Variance-FpQm_dYA9LM.zh-CN.vtt
08. Principal Axis of New Coordinate System-qPr3Uj55eog.ar.vtt
01. Data Dimensionality-bAZJT4xHiXM.pt-BR.vtt
05. Trickiest Data Dimensionality-vIxDt0bNV9g.zh-CN.vtt
04. Slightly Less Perfect Data-g5yfjKWIKN4.zh-CN.vtt
24. Maximum Number of PCs Quiz-oOUx6NHppdQ.en.vtt
04. Slightly Less Perfect Data-g5yfjKWIKN4.en.vtt
05. Trickiest Data Dimensionality-vIxDt0bNV9g.en.vtt
24. Maximum Number of PCs Quiz-oOUx6NHppdQ.pt-BR.vtt
04. Slightly Less Perfect Data-g5yfjKWIKN4.pt-BR.vtt
20. Maximal Variance and Information Loss-DX_f02bUHT0.zh-CN.vtt
01. Data Dimensionality-bAZJT4xHiXM.ar.vtt
24. Maximum Number of PCs Quiz-oOUx6NHppdQ.ar.vtt
03. One-Dimensional, or Two-QsncWsyboFk.ar.vtt
02. Trickier Data Dimensionality-s24-ikl3ZAs.zh-CN.vtt
05. Trickiest Data Dimensionality-vIxDt0bNV9g.pt-BR.vtt
07. Center of a New Coordinate System-1ask5zHGQKM.ar.vtt
02. Trickier Data Dimensionality-s24-ikl3ZAs.en.vtt
18. Maximal Variance-FpQm_dYA9LM.pt-BR.vtt
18. Maximal Variance-FpQm_dYA9LM.en.vtt
13. When Does an Axis Dominate-4hJlaYRHdpA.zh-CN.vtt
02. Trickier Data Dimensionality-s24-ikl3ZAs.pt-BR.vtt
24. Maximum Number of PCs Quiz-q4c5n5W2aUc.zh-CN.vtt
20. Maximal Variance and Information Loss-DX_f02bUHT0.pt-BR.vtt
04. Slightly Less Perfect Data-g5yfjKWIKN4.ar.vtt
05. Trickiest Data Dimensionality-vIxDt0bNV9g.ar.vtt
09. Second Principal Component Of New System-cTjBlM2ATLQ.zh-CN.vtt
20. Maximal Variance and Information Loss-DX_f02bUHT0.en.vtt
13. When Does an Axis Dominate-4hJlaYRHdpA.en.vtt
24. Maximum Number of PCs Quiz-q4c5n5W2aUc.en.vtt
13. When Does an Axis Dominate-4hJlaYRHdpA.pt-BR.vtt
27. PCA on the Enron Finance Data-6ufIq2nrTwg.zh-CN.vtt
09. Second Principal Component Of New System-cTjBlM2ATLQ.en.vtt
18. Maximal Variance-FpQm_dYA9LM.ar.vtt
24. Maximum Number of PCs Quiz-q4c5n5W2aUc.pt-BR.vtt
17. Composite Features-0ZBp8oWySAc.zh-CN.vtt
14. Measurable vs. Latent Features Quiz-20QVVrTcp2A.zh-CN.vtt
02. Trickier Data Dimensionality-s24-ikl3ZAs.ar.vtt
09. Second Principal Component Of New System-cTjBlM2ATLQ.pt-BR.vtt
17. Composite Features-0ZBp8oWySAc.pt-BR.vtt
24. Maximum Number of PCs Quiz-q4c5n5W2aUc.ar.vtt
12. Which Data is Ready for PCA-JSVsHbGUuIE.zh-CN.vtt
20. Maximal Variance and Information Loss-DX_f02bUHT0.ar.vtt
27. PCA on the Enron Finance Data-6ufIq2nrTwg.pt-BR.vtt
14. Measurable vs. Latent Features Quiz-20QVVrTcp2A.en.vtt
27. PCA on the Enron Finance Data-6ufIq2nrTwg.en.vtt
22. Neighborhood Composite Feature-adXoa85rnPM.zh-CN.vtt
19. Advantages of Maximal Variance-TbT6a6qaj08.zh-CN.vtt
14. Measurable vs. Latent Features Quiz-UeSD19oit_w.zh-CN.vtt
17. Composite Features-0ZBp8oWySAc.en.vtt
13. When Does an Axis Dominate-4hJlaYRHdpA.ar.vtt
14. Measurable vs. Latent Features Quiz-20QVVrTcp2A.pt-BR.vtt
12. Which Data is Ready for PCA-JSVsHbGUuIE.en.vtt
22. Neighborhood Composite Feature-adXoa85rnPM.en.vtt
12. Which Data is Ready for PCA-JSVsHbGUuIE.pt-BR.vtt
08. Principal Axis of New Coordinate System-i6zv8vyZBk0.zh-CN.vtt
04. Slightly Less Perfect Data-9O7cJSP4C8w.zh-CN.vtt
19. Advantages of Maximal Variance-TbT6a6qaj08.en.vtt
22. Neighborhood Composite Feature-adXoa85rnPM.pt-BR.vtt
27. PCA on the Enron Finance Data-6ufIq2nrTwg.ar.vtt
10. Practice Finding Centers-PRjmvj6Vubs.zh-CN.vtt
14. Measurable vs. Latent Features Quiz-UeSD19oit_w.en.vtt
19. Advantages of Maximal Variance-TbT6a6qaj08.pt-BR.vtt
09. Second Principal Component Of New System-cTjBlM2ATLQ.ar.vtt
14. Measurable vs. Latent Features Quiz-UeSD19oit_w.pt-BR.vtt
22. Neighborhood Composite Feature-WxAWorS2SLg.zh-CN.vtt
08. Principal Axis of New Coordinate System-i6zv8vyZBk0.pt-BR.vtt
04. Slightly Less Perfect Data-9O7cJSP4C8w.pt-BR.vtt
14. Measurable vs. Latent Features Quiz-20QVVrTcp2A.ar.vtt
04. Slightly Less Perfect Data-9O7cJSP4C8w.en.vtt
12. Which Data is Ready for PCA-JSVsHbGUuIE.ar.vtt
08. Principal Axis of New Coordinate System-i6zv8vyZBk0.en.vtt
17. Composite Features-0ZBp8oWySAc.ar.vtt
22. Neighborhood Composite Feature-WxAWorS2SLg.en.vtt
15. From Four Features to Two-xJtmPbEfpFo.zh-CN.vtt
10. Practice Finding Centers-PRjmvj6Vubs.en.vtt
07. Center of a New Coordinate System-Kst3mlrqJnQ.zh-CN.vtt
03. One-Dimensional, or Two-yhzQ_HJcwn8.zh-CN.vtt
16. Compression While Preserving Information-_TJeoCTDykE.zh-CN.vtt
14. Measurable vs. Latent Features Quiz-UeSD19oit_w.ar.vtt
16. Compression While Preserving Information-_TJeoCTDykE.pt-BR.vtt
10. Practice Finding Centers-PRjmvj6Vubs.pt-BR.vtt
22. Neighborhood Composite Feature-adXoa85rnPM.ar.vtt
03. One-Dimensional, or Two-yhzQ_HJcwn8.en.vtt
13. When Does an Axis Dominate-5Uon6hUTl8Y.zh-CN.vtt
22. Neighborhood Composite Feature-WxAWorS2SLg.pt-BR.vtt
16. Compression While Preserving Information-_TJeoCTDykE.en.vtt
03. One-Dimensional, or Two-yhzQ_HJcwn8.pt-BR.vtt
19. Advantages of Maximal Variance-TbT6a6qaj08.ar.vtt
07. Center of a New Coordinate System-Kst3mlrqJnQ.en.vtt
07. Center of a New Coordinate System-Kst3mlrqJnQ.pt-BR.vtt
13. When Does an Axis Dominate-5Uon6hUTl8Y.pt-BR.vtt
13. When Does an Axis Dominate-5Uon6hUTl8Y.en.vtt
04. Slightly Less Perfect Data-9O7cJSP4C8w.ar.vtt
08. Principal Axis of New Coordinate System-i6zv8vyZBk0.ar.vtt
10. Practice Finding Centers-PRjmvj6Vubs.ar.vtt
15. From Four Features to Two-xJtmPbEfpFo.pt-BR.vtt
15. From Four Features to Two-xJtmPbEfpFo.en.vtt
22. Neighborhood Composite Feature-WxAWorS2SLg.ar.vtt
01. Data Dimensionality-gg7SAMMl4kM.zh-CN.vtt
09. Second Principal Component Of New System-PqtW_Ux2_nY.zh-CN.vtt
12. Which Data is Ready for PCA-Su7kIUVPu6w.zh-CN.vtt
16. Compression While Preserving Information-_TJeoCTDykE.ar.vtt
01. Data Dimensionality-gg7SAMMl4kM.en.vtt
05. Trickiest Data Dimensionality-mTcuS5jUeUE.zh-CN.vtt
13. When Does an Axis Dominate-5Uon6hUTl8Y.ar.vtt
09. Second Principal Component Of New System-PqtW_Ux2_nY.en.vtt
03. One-Dimensional, or Two-yhzQ_HJcwn8.ar.vtt
09. Second Principal Component Of New System-PqtW_Ux2_nY.pt-BR.vtt
27. PCA on the Enron Finance Data-w5XWkq_Y-rY.zh-CN.vtt
12. Which Data is Ready for PCA-Su7kIUVPu6w.en.vtt
01. Data Dimensionality-gg7SAMMl4kM.pt-BR.vtt
06. PCA for Data Transformation-nDuo5ECT1G4.zh-CN.vtt
12. Which Data is Ready for PCA-Su7kIUVPu6w.pt-BR.vtt
15. From Four Features to Two-xJtmPbEfpFo.ar.vtt
07. Center of a New Coordinate System-Kst3mlrqJnQ.ar.vtt
05. Trickiest Data Dimensionality-mTcuS5jUeUE.en.vtt
15. From Four Features to Two-MEtIAGKweXU.zh-CN.vtt
05. Trickiest Data Dimensionality-mTcuS5jUeUE.pt-BR.vtt
27. PCA on the Enron Finance Data-w5XWkq_Y-rY.en.vtt
27. PCA on the Enron Finance Data-w5XWkq_Y-rY.en-US.vtt
30. PCA for Facial Recognition-B_JKtLN-i5I.zh-CN.vtt
27. PCA on the Enron Finance Data-w5XWkq_Y-rY.pt-BR.vtt
06. PCA for Data Transformation-nDuo5ECT1G4.pt-BR.vtt
06. PCA for Data Transformation-nDuo5ECT1G4.en.vtt
01. Data Dimensionality-gg7SAMMl4kM.ar.vtt
09. Second Principal Component Of New System-PqtW_Ux2_nY.ar.vtt
30. PCA for Facial Recognition-B_JKtLN-i5I.en.vtt
30. PCA for Facial Recognition-B_JKtLN-i5I.pt-BR.vtt
05. Trickiest Data Dimensionality-mTcuS5jUeUE.ar.vtt
19. Advantages of Maximal Variance-jQaYAlZ1fp0.zh-CN.vtt
12. Which Data is Ready for PCA-Su7kIUVPu6w.ar.vtt
11. Practice Finding New Axes-aZqYc7v8BK4.zh-CN.vtt
15. From Four Features to Two-MEtIAGKweXU.pt-BR.vtt
15. From Four Features to Two-MEtIAGKweXU.en.vtt
30. PCA for Facial Recognition-WyoU2otqsd8.zh-CN.vtt
21. Info Loss and Principal Components-LTPV8lxQeZQ.zh-CN.vtt
19. Advantages of Maximal Variance-jQaYAlZ1fp0.pt-BR.vtt
11. Practice Finding New Axes-aZqYc7v8BK4.pt-BR.vtt
27. PCA on the Enron Finance Data-w5XWkq_Y-rY.ar.vtt
19. Advantages of Maximal Variance-jQaYAlZ1fp0.en.vtt
11. Practice Finding New Axes-aZqYc7v8BK4.en.vtt
06. PCA for Data Transformation-nDuo5ECT1G4.ar.vtt
21. Info Loss and Principal Components-LTPV8lxQeZQ.en.vtt
18. Maximal Variance-tfYAGBIR_Ws.zh-CN.vtt
21. Info Loss and Principal Components-LTPV8lxQeZQ.pt-BR.vtt
15. From Four Features to Two-MEtIAGKweXU.ar.vtt
30. PCA for Facial Recognition-B_JKtLN-i5I.ar.vtt
30. PCA for Facial Recognition-WyoU2otqsd8.en.vtt
20. Maximal Variance and Information Loss-hfmvk8DzTGA.zh-CN.vtt
30. PCA for Facial Recognition-WyoU2otqsd8.pt-BR.vtt
16. Compression While Preserving Information-NjuenhkC-44.zh-CN.vtt
18. Maximal Variance-tfYAGBIR_Ws.en.vtt
19. Advantages of Maximal Variance-jQaYAlZ1fp0.ar.vtt
25. ReviewDefinition of PCA-oFBGXUUuKyI.zh-CN.vtt
18. Maximal Variance-tfYAGBIR_Ws.pt-BR.vtt
20. Maximal Variance and Information Loss-hfmvk8DzTGA.en.vtt
20. Maximal Variance and Information Loss-hfmvk8DzTGA.pt-BR.vtt
16. Compression While Preserving Information-NjuenhkC-44.en.vtt
11. Practice Finding New Axes-aZqYc7v8BK4.ar.vtt
16. Compression While Preserving Information-NjuenhkC-44.pt-BR.vtt
21. Info Loss and Principal Components-LTPV8lxQeZQ.ar.vtt
30. PCA for Facial Recognition-WyoU2otqsd8.ar.vtt
25. ReviewDefinition of PCA-oFBGXUUuKyI.en.vtt
18. Maximal Variance-tfYAGBIR_Ws.ar.vtt
25. ReviewDefinition of PCA-oFBGXUUuKyI.pt-BR.vtt
23. PCA for Feature Transformation-8kUPRUEMCA8.zh-CN.vtt
17. Composite Features-spVqFnSvlIU.zh-CN.vtt
16. Compression While Preserving Information-NjuenhkC-44.ar.vtt
20. Maximal Variance and Information Loss-hfmvk8DzTGA.ar.vtt
23. PCA for Feature Transformation-8kUPRUEMCA8.en.vtt
17. Composite Features-spVqFnSvlIU.en.vtt
23. PCA for Feature Transformation-8kUPRUEMCA8.pt-BR.vtt
17. Composite Features-spVqFnSvlIU.pt-BR.vtt
25. ReviewDefinition of PCA-oFBGXUUuKyI.ar.vtt
28. PCA in sklearn-SBYdqlLgbGk.zh-CN.vtt
29. When to Use PCA-hJZHcmJBk1o.zh-CN.vtt
28. PCA in sklearn-SBYdqlLgbGk.en.vtt
28. PCA in sklearn-SBYdqlLgbGk.pt-BR.vtt
29. When to Use PCA-hJZHcmJBk1o.en.vtt
17. Composite Features-spVqFnSvlIU.ar.vtt
31. Eigenfaces Code-LgLYw-G4sLQ.zh-CN.vtt
29. When to Use PCA-hJZHcmJBk1o.pt-BR.vtt
23. PCA for Feature Transformation-8kUPRUEMCA8.ar.vtt
31. Eigenfaces Code-LgLYw-G4sLQ.en.vtt
index.html
31. Eigenfaces Code-LgLYw-G4sLQ.pt-BR.vtt
28. PCA in sklearn-SBYdqlLgbGk.ar.vtt
29. When to Use PCA-hJZHcmJBk1o.ar.vtt
29. When to Use PCA.html
31. Eigenfaces Code.html
25. ReviewDefinition of PCA.html
26. Applying PCA to Real Data.html
06. PCA for Data Transformation.html
23. PCA for Feature Transformation.html
21. Info Loss and Principal Components.html
31. Eigenfaces Code-LgLYw-G4sLQ.ar.vtt
28. PCA in sklearn.html
18. Maximal Variance.html
01. Data Dimensionality.html
11. Practice Finding New Axes.html
03. One-Dimensional, or Two.html
08. Principal Axis of New Coordinate System.html
04. Slightly Less Perfect Data.html
24. Maximum Number of PCs Quiz.html
02. Trickier Data Dimensionality.html
05. Trickiest Data Dimensionality.html
10. Practice Finding Centers.html
20. Maximal Variance and Information Loss.html
07. Center of a New Coordinate System.html
16. Compression While Preserving Information.html
19. Advantages of Maximal Variance.html
17. Composite Features.html
14. Measurable vs. Latent Features Quiz.html
27. PCA on the Enron Finance Data.html
30. PCA for Facial Recognition.html
22. Neighborhood Composite Feature.html
09. Second Principal Component of New System.html
15. From Four Features to Two.html
13. When Does an Axis Dominate.html
12. Which Data is Ready for PCA.html
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14. Measurable vs. Latent Features Quiz-20QVVrTcp2A.mp4
11. Practice Finding New Axes-th34aboBOO0.mp4
02. Trickier Data Dimensionality--dcNhrSPmoY.mp4
01. Data Dimensionality-bAZJT4xHiXM.mp4
10. Practice Finding Centers-FZVBF1HR4U0.mp4
08. Principal Axis of New Coordinate System-qPr3Uj55eog.mp4
03. One-Dimensional, or Two-QsncWsyboFk.mp4
09. Second Principal Component Of New System-cTjBlM2ATLQ.mp4
02. Trickier Data Dimensionality-s24-ikl3ZAs.mp4
04. Slightly Less Perfect Data-g5yfjKWIKN4.mp4
05. Trickiest Data Dimensionality-vIxDt0bNV9g.mp4
18. Maximal Variance-FpQm_dYA9LM.mp4
13. When Does an Axis Dominate-4hJlaYRHdpA.mp4
24. Maximum Number of PCs Quiz-oOUx6NHppdQ.mp4
07. Center of a New Coordinate System-1ask5zHGQKM.mp4
20. Maximal Variance and Information Loss-DX_f02bUHT0.mp4
24. Maximum Number of PCs Quiz-q4c5n5W2aUc.mp4
27. PCA on the Enron Finance Data-6ufIq2nrTwg.mp4
14. Measurable vs. Latent Features Quiz-UeSD19oit_w.mp4
22. Neighborhood Composite Feature-WxAWorS2SLg.mp4
19. Advantages of Maximal Variance-TbT6a6qaj08.mp4
22. Neighborhood Composite Feature-adXoa85rnPM.mp4
17. Composite Features-0ZBp8oWySAc.mp4
08. Principal Axis of New Coordinate System-i6zv8vyZBk0.mp4
16. Compression While Preserving Information-_TJeoCTDykE.mp4
12. Which Data is Ready for PCA-JSVsHbGUuIE.mp4
04. Slightly Less Perfect Data-9O7cJSP4C8w.mp4
10. Practice Finding Centers-PRjmvj6Vubs.mp4
15. From Four Features to Two-xJtmPbEfpFo.mp4
09. Second Principal Component Of New System-PqtW_Ux2_nY.mp4
03. One-Dimensional, or Two-yhzQ_HJcwn8.mp4
27. PCA on the Enron Finance Data-w5XWkq_Y-rY.mp4
07. Center of a New Coordinate System-Kst3mlrqJnQ.mp4
13. When Does an Axis Dominate-5Uon6hUTl8Y.mp4
01. Data Dimensionality-gg7SAMMl4kM.mp4
11. Practice Finding New Axes-aZqYc7v8BK4.mp4
12. Which Data is Ready for PCA-Su7kIUVPu6w.mp4
19. Advantages of Maximal Variance-jQaYAlZ1fp0.mp4
30. PCA for Facial Recognition-B_JKtLN-i5I.mp4
05. Trickiest Data Dimensionality-mTcuS5jUeUE.mp4
15. From Four Features to Two-MEtIAGKweXU.mp4
06. PCA for Data Transformation-nDuo5ECT1G4.mp4
21. Info Loss and Principal Components-LTPV8lxQeZQ.mp4
30. PCA for Facial Recognition-WyoU2otqsd8.mp4
18. Maximal Variance-tfYAGBIR_Ws.mp4
16. Compression While Preserving Information-NjuenhkC-44.mp4
25. ReviewDefinition of PCA-oFBGXUUuKyI.mp4
20. Maximal Variance and Information Loss-hfmvk8DzTGA.mp4
29. When to Use PCA-hJZHcmJBk1o.mp4
17. Composite Features-spVqFnSvlIU.mp4
23. PCA for Feature Transformation-8kUPRUEMCA8.mp4
28. PCA in sklearn-SBYdqlLgbGk.mp4
31. Eigenfaces Code-LgLYw-G4sLQ.mp4
Part 04-Module 02-Lesson 01_Clustering
09. Handoff to Katie-knrPsGtpyQY.pt-BR.vtt
09. Handoff to Katie-knrPsGtpyQY.zh-CN.vtt
09. Handoff to Katie-knrPsGtpyQY.en.vtt
09. Handoff to Katie-knrPsGtpyQY.ar.vtt
15. Limitations of K-Means-nvLhUSSUhiY.zh-CN.vtt
15. Limitations of K-Means-nvLhUSSUhiY.pt-BR.vtt
07. Moving Centers 2-uC1Xwc7warg.en.vtt
15. Limitations of K-Means-nvLhUSSUhiY.en.vtt
07. Moving Centers 2-uC1Xwc7warg.pt-BR.vtt
07. Moving Centers 2-uC1Xwc7warg.zh-CN.vtt
08. Match Points (again)-9J3IwQFXveI.zh-CN.vtt
08. Match Points (again)-9J3IwQFXveI.en.vtt
07. Moving Centers 2-FY0DXe0lfrI.zh-CN.vtt
07. Moving Centers 2-FY0DXe0lfrI.en.vtt
08. Match Points (again)-9J3IwQFXveI.pt-BR.vtt
07. Moving Centers 2-FY0DXe0lfrI.pt-BR.vtt
04. How Many Clusters-8Ygq5dRV0Kk.zh-CN.vtt
15. Limitations of K-Means-nvLhUSSUhiY.ar.vtt
10. K-Means Cluster Visualization-ZMfwPUrOFsE.zh-CN.vtt
04. How Many Clusters-8Ygq5dRV0Kk.pt-BR.vtt
07. Moving Centers 2-uC1Xwc7warg.ar.vtt
10. K-Means Cluster Visualization-ZMfwPUrOFsE.en.vtt
10. K-Means Cluster Visualization-ZMfwPUrOFsE.pt-BR.vtt
04. How Many Clusters-8Ygq5dRV0Kk.en.vtt
07. Moving Centers 2-FY0DXe0lfrI.ar.vtt
06. Optimizing Centers (Rubber Bands)-TN1rQMrx65c.zh-CN.vtt
08. Match Points (again)-9J3IwQFXveI.ar.vtt
04. How Many Clusters-8Ygq5dRV0Kk.ar.vtt
14. Some challenges of k-means-e2CdlG5P4WA.zh-CN.vtt
14. Some challenges of k-means-e2CdlG5P4WA.pt-BR.vtt
10. K-Means Cluster Visualization-ZMfwPUrOFsE.ar.vtt
05. Match Points with Clusters-wJV1cRjmIYY.zh-CN.vtt
06. Optimizing Centers (Rubber Bands)-TN1rQMrx65c.en.vtt
14. Some challenges of k-means-e2CdlG5P4WA.en.vtt
06. Optimizing Centers (Rubber Bands)-TN1rQMrx65c.pt-BR.vtt
05. Match Points with Clusters-wJV1cRjmIYY.en.vtt
17. Counterintuitive Clusters 2-xSQTzAeeoEc.zh-CN.vtt
17. Counterintuitive Clusters 2-xSQTzAeeoEc.en.vtt
05. Match Points with Clusters-wJV1cRjmIYY.pt-BR.vtt
17. Counterintuitive Clusters 2-xSQTzAeeoEc.pt-BR.vtt
05. Match Points with Clusters-lS5DfbsWH34.zh-CN.vtt
05. Match Points with Clusters-wJV1cRjmIYY.ar.vtt
04. How Many Clusters-R6oIvdBtsZw.zh-CN.vtt
04. How Many Clusters-R6oIvdBtsZw.en.vtt
05. Match Points with Clusters-lS5DfbsWH34.en.vtt
04. How Many Clusters-R6oIvdBtsZw.pt-BR.vtt
05. Match Points with Clusters-lS5DfbsWH34.pt-BR.vtt
15. Limitations of K-Means-4Fkfu37el_k.zh-CN.vtt
06. Optimizing Centers (Rubber Bands)-TN1rQMrx65c.ar.vtt
17. Counterintuitive Clusters 2-xSQTzAeeoEc.ar.vtt
15. Limitations of K-Means-4Fkfu37el_k.en.vtt
15. Limitations of K-Means-4Fkfu37el_k.pt-BR.vtt
14. Some challenges of k-means-e2CdlG5P4WA.ar.vtt
16. Counterintuitive Clusters-aveIz1JYeAg.zh-CN.vtt
16. Counterintuitive Clusters-aveIz1JYeAg.en.vtt
04. How Many Clusters-R6oIvdBtsZw.ar.vtt
08. Match Points (again)-5j6VZr8sHo8.zh-CN.vtt
16. Counterintuitive Clusters-aveIz1JYeAg.pt-BR.vtt
05. Match Points with Clusters-lS5DfbsWH34.ar.vtt
08. Match Points (again)-5j6VZr8sHo8.en.vtt
08. Match Points (again)-5j6VZr8sHo8.pt-BR.vtt
15. Limitations of K-Means-4Fkfu37el_k.ar.vtt
10. K-Means Cluster Visualization-iCTPBcowJRY.zh-CN.vtt
16. Counterintuitive Clusters-aveIz1JYeAg.ar.vtt
17. Counterintuitive Clusters 2-HyjBus7S2gY.zh-CN.vtt
06. Optimizing Centers (Rubber Bands)-nNR4hjhhGBc.zh-CN.vtt
10. K-Means Cluster Visualization-iCTPBcowJRY.pt-BR.vtt
17. Counterintuitive Clusters 2-HyjBus7S2gY.en.vtt
17. Counterintuitive Clusters 2-HyjBus7S2gY.pt-BR.vtt
10. K-Means Cluster Visualization-iCTPBcowJRY.en.vtt
06. Optimizing Centers (Rubber Bands)-nNR4hjhhGBc.pt-BR.vtt
08. Match Points (again)-5j6VZr8sHo8.ar.vtt
06. Optimizing Centers (Rubber Bands)-nNR4hjhhGBc.en.vtt
17. Counterintuitive Clusters 2-HyjBus7S2gY.ar.vtt
16. Counterintuitive Clusters-StmEUgT1XSY.zh-CN.vtt
10. K-Means Cluster Visualization-iCTPBcowJRY.ar.vtt
06. Optimizing Centers (Rubber Bands)-nNR4hjhhGBc.ar.vtt
16. Counterintuitive Clusters-StmEUgT1XSY.en.vtt
16. Counterintuitive Clusters-StmEUgT1XSY.pt-BR.vtt
12. K-Means Clustering Visualization 3-WfwX3B4d8_I.zh-CN.vtt
03. Clustering Movies-g8PKffm8IRY.zh-CN.vtt
12. K-Means Clustering Visualization 3-WfwX3B4d8_I.pt-BR.vtt
12. K-Means Clustering Visualization 3-WfwX3B4d8_I.en.vtt
02. Unsupervised Learning-Mx9f99bRB3Q.zh-CN.vtt
03. Clustering Movies-g8PKffm8IRY.en.vtt
03. Clustering Movies-g8PKffm8IRY.pt-BR.vtt
16. Counterintuitive Clusters-StmEUgT1XSY.ar.vtt
11. K-Means Clustering Visualization 2-fQXXa-CAoS0.zh-CN.vtt
02. Unsupervised Learning-Mx9f99bRB3Q.pt-BR.vtt
02. Unsupervised Learning-Mx9f99bRB3Q.en.vtt
11. K-Means Clustering Visualization 2-fQXXa-CAoS0.pt-BR.vtt
11. K-Means Clustering Visualization 2-fQXXa-CAoS0.en.vtt
12. K-Means Clustering Visualization 3-WfwX3B4d8_I.ar.vtt
03. Clustering Movies-g8PKffm8IRY.ar.vtt
02. Unsupervised Learning-Mx9f99bRB3Q.ar.vtt
index.html
11. K-Means Clustering Visualization 2-fQXXa-CAoS0.ar.vtt
13. Sklearn-3zHUAXcoZ7c.zh-CN.vtt
13. Sklearn.html
13. Sklearn-3zHUAXcoZ7c.pt-BR.vtt
09. Handoff to Katie.html
03. Clustering Movies.html
02. Unsupervised Learning.html
14. Some challenges of k-means.html
13. Sklearn-3zHUAXcoZ7c.en.vtt
12. K-Means Clustering Visualization 3.html
11. K-Means Clustering Visualization 2.html
01. Introduction.html
04. How Many Clusters.html
15. Limitations of K-Means.html
16. Counterintuitive Clusters.html
17. Counterintuitive Clusters 2.html
10. K-Means Cluster Visualization.html
07. Moving Centers 2.html
06. Optimizing Centers (Rubber Bands).html
13. Sklearn-3zHUAXcoZ7c.ar.vtt
08. Match Points (again).html
05. Match Points with Clusters.html
img
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09. Handoff to Katie-knrPsGtpyQY.mp4
07. Moving Centers 2-uC1Xwc7warg.mp4
15. Limitations of K-Means-nvLhUSSUhiY.mp4
08. Match Points (again)-9J3IwQFXveI.mp4
10. K-Means Cluster Visualization-ZMfwPUrOFsE.mp4
07. Moving Centers 2-FY0DXe0lfrI.mp4
04. How Many Clusters-8Ygq5dRV0Kk.mp4
05. Match Points with Clusters-lS5DfbsWH34.mp4
06. Optimizing Centers (Rubber Bands)-TN1rQMrx65c.mp4
17. Counterintuitive Clusters 2-xSQTzAeeoEc.mp4
05. Match Points with Clusters-wJV1cRjmIYY.mp4
04. How Many Clusters-R6oIvdBtsZw.mp4
14. Some challenges of k-means-e2CdlG5P4WA.mp4
10. K-Means Cluster Visualization-iCTPBcowJRY.mp4
16. Counterintuitive Clusters-aveIz1JYeAg.mp4
06. Optimizing Centers (Rubber Bands)-nNR4hjhhGBc.mp4
15. Limitations of K-Means-4Fkfu37el_k.mp4
08. Match Points (again)-5j6VZr8sHo8.mp4
12. K-Means Clustering Visualization 3-WfwX3B4d8_I.mp4
17. Counterintuitive Clusters 2-HyjBus7S2gY.mp4
03. Clustering Movies-g8PKffm8IRY.mp4
16. Counterintuitive Clusters-StmEUgT1XSY.mp4
11. K-Means Clustering Visualization 2-fQXXa-CAoS0.mp4
02. Unsupervised Learning-Mx9f99bRB3Q.mp4
13. Sklearn-3zHUAXcoZ7c.mp4
Part 11-Module 05-Lesson 01_Convolutional Neural Networks
01. Intro to CNNs-B61jxZ4rkMs.zh-CN.vtt
01. Intro to CNNs-B61jxZ4rkMs.en.vtt
01. Intro to CNNs-B61jxZ4rkMs.en-US.vtt
01. Intro to CNNs-B61jxZ4rkMs.pt-BR.vtt
01. Intro to CNNs-B61jxZ4rkMs.ja-JP.vtt
07. Feature-Map-Sizes-Question-lp1NrLZnCUM.zh-CN.vtt
07. Feature-Map-Sizes-Question-lp1NrLZnCUM.en.vtt
02. Color-Question-BdQccpMwk80.zh-CN.vtt
07. Feature-Map-Sizes-Question-lp1NrLZnCUM.pt-BR.vtt
02. Color-Question-BdQccpMwk80.pt-BR.vtt
02. Color-Question-BdQccpMwk80.en.vtt
08. Convolutions Cont.-utOv-BKI_vo.zh-CN.vtt
08. Convolutions Cont.-utOv-BKI_vo.pt-BR.vtt
07. Feature-Map-Sizes-Solution-W4xtf8LTz1c.zh-CN.vtt
08. Convolutions Cont.-utOv-BKI_vo.en.vtt
07. Feature-Map-Sizes-Solution-W4xtf8LTz1c.en.vtt
07. Feature-Map-Sizes-Solution-W4xtf8LTz1c.pt-BR.vtt
29. Inception Module-SlTm03bEOxA.zh-CN.vtt
28. 1x1 Convolutions-Zmzgerm6SjA.zh-CN.vtt
29. Inception Module-SlTm03bEOxA.pt-BR.vtt
29. Inception Module-SlTm03bEOxA.en.vtt
28. 1x1 Convolutions-Zmzgerm6SjA.en.vtt
28. 1x1 Convolutions-Zmzgerm6SjA.pt-BR.vtt
03. Statistical Invariance-0Hr5YwUUhr0.zh-CN.vtt
03. Statistical Invariance-0Hr5YwUUhr0.pt-BR.vtt
03. Statistical Invariance-0Hr5YwUUhr0.en.vtt
18. Explore the Design Space-FG7M9tWH2nQ.zh-CN.vtt
18. Explore the Design Space-FG7M9tWH2nQ.en.vtt
18. Explore the Design Space-FG7M9tWH2nQ.pt-BR.vtt
04. Convolutional Networks-ISHGyvsT0QY.zh-CN.vtt
04. Convolutional Networks-ISHGyvsT0QY.en.vtt
04. Convolutional Networks-ISHGyvsT0QY.pt-BR.vtt
img
diagonal-line-1.png
diagonal-line-2.png
neilsen-pic.png
screen-shot-2016-11-24-at-12.51.51-pm.png
screen-shot-2016-11-24-at-10.05.37-pm.png
screen-shot-2016-11-24-at-10.05.46-pm.png
screen-shot-2016-11-24-at-12.51.47-pm.png
max-pooling.png
grid-layer-1.png
maxpool.jpeg
layer-1-grid.png
heirarchy-diagram.jpg
screen-shot-2016-11-24-at-12.49.08-pm.png
convolution-schematic.gif
screen-shot-2016-11-24-at-12.50.54-pm.png
filter-depth.png
screen-shot-2016-11-24-at-12.49.43-pm.png
dog-1210559-1280.jpg
vlcsnap-2016-11-24-15h52m47s438.png
teeth-whiskers-tongue.png
vlcsnap-2016-11-24-16h01m35s262.png
retriever-patch.png
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convolutionalnetworksquiz.png
arch.png
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screen-shot-2016-11-24-at-12.09.24-pm.png
index.html
25. Solution Pooling Practice.html
27. Solution Average Pooling.html
29. Inception Module.html
28. 1x1 Convolutions.html
13. Solution Number of Parameters.html
03. Statistical Invariance.html
04. Convolutional Networks.html
18. Explore The Design Space.html
15. Solution Parameter Sharing.html
01. Intro To CNNs.html
23. Solution Pooling Mechanics.html
08. Convolutions continued.html
21. Solution Pooling Intuition.html
26. Quiz Average Pooling.html
35. CNNs - Additional Resources.html
24. Quiz Pooling Practice.html
02. Color.html
20. Quiz Pooling Intuition.html
34. Solution TensorFlow Pooling Layer.html
12. Quiz Number of Parameters.html
10. Quiz Convolution Output Shape.html
14. Quiz Parameter Sharing.html
22. Quiz Pooling Mechanics.html
32. Solution TensorFlow Convolution Layer.html
11. Solution Convolution Output Shape.html
33. TensorFlow Pooling Layer.html
17. TensorFlow Convolution Layer.html
19. TensorFlow Max Pooling.html
07. Feature Map Sizes.html
31. TensorFlow Convolution Layer.html
09. Parameters.html
05. Intuition.html
06. Filters.html
16. Visualizing CNNs.html
30. Convolutional Network in TensorFlow.html
07. Feature-Map-Sizes-Question-lp1NrLZnCUM.mp4
02. Color-Question-BdQccpMwk80.mp4
07. Feature-Map-Sizes-Solution-W4xtf8LTz1c.mp4
01. Intro to CNNs-B61jxZ4rkMs.mp4
08. Convolutions Cont.-utOv-BKI_vo.mp4
03. Statistical Invariance-0Hr5YwUUhr0.mp4
29. Inception Module-SlTm03bEOxA.mp4
28. 1x1 Convolutions-Zmzgerm6SjA.mp4
18. Explore the Design Space-FG7M9tWH2nQ.mp4
04. Convolutional Networks-ISHGyvsT0QY.mp4
Part 09-Module 02-Lesson 01_GitHub Review
05. Identify fixes for example “bad†profile-AF07y1oAim0.zh-CN.vtt
05. Identify fixes for example “bad†profile-AF07y1oAim0.en.vtt
15. Starring interesting repositories-U3FUxkm1MxI.zh-CN.vtt
07. Quick Fixes #2-It6AEuSDQw0.zh-CN.vtt
15. Starring interesting repositories-U3FUxkm1MxI.en.vtt
07. Quick Fixes #2-It6AEuSDQw0.en.vtt
12. Participating in open source projects-OxL-gMTizUA.zh-CN.vtt
07. Quick Fixes #2-It6AEuSDQw0.pt-BR.vtt
05. Identify fixes for example “bad†profile-AF07y1oAim0.pt-BR.vtt
15. Starring interesting repositories-U3FUxkm1MxI.pt-BR.vtt
11. Reflect on your commit messages-_0AHmKkfjTo.zh-CN.vtt
12. Participating in open source projects-OxL-gMTizUA.en.vtt
05. Identify fixes for example “bad†profile-AF07y1oAim0.ar.vtt
11. Reflect on your commit messages-_0AHmKkfjTo.en.vtt
11. Reflect on your commit messages-_0AHmKkfjTo.pt-BR.vtt
15. Starring interesting repositories-U3FUxkm1MxI.ar.vtt
12. Participating in open source projects-OxL-gMTizUA.pt-BR.vtt
15. Starring interesting repositories-ZwMY5rAAd7Q.zh-CN.vtt
07. Quick Fixes #2-It6AEuSDQw0.ar.vtt
15. Starring interesting repositories-ZwMY5rAAd7Q.en.vtt
11. Reflect on your commit messages-_0AHmKkfjTo.ar.vtt
15. Starring interesting repositories-ZwMY5rAAd7Q.pt-BR.vtt
16. Outro-dps7Ti6Lado.zh-CN.vtt
12. Participating in open source projects-OxL-gMTizUA.ar.vtt
16. Outro-dps7Ti6Lado.en.vtt
15. Starring interesting repositories-ZwMY5rAAd7Q.ar.vtt
16. Outro-dps7Ti6Lado.pt-BR.vtt
08. Writing READMEs with Walter-DQEfT2Zq5_o.zh-CN.vtt
08. Writing READMEs with Walter-DQEfT2Zq5_o.pt-BR.vtt
14. Participating in open source projects 2-elZCLxVvJrY.zh-CN.vtt
05. Identify fixes for example “bad†profile-ncFtwW5urHk.zh-CN.vtt
08. Writing READMEs with Walter-DQEfT2Zq5_o.en.vtt
05. Identify fixes for example “bad†profile-ncFtwW5urHk.en.vtt
14. Participating in open source projects 2-elZCLxVvJrY.en.vtt
05. Identify fixes for example “bad†profile-ncFtwW5urHk.pt-BR.vtt
08. Writing READMEs with Walter-DQEfT2Zq5_o.ar.vtt
01. Introduction-Vnj2VNQROtI.en.vtt
01. Introduction-Vnj2VNQROtI.zh-CN.vtt
14. Participating in open source projects 2-elZCLxVvJrY.pt-BR.vtt
01. Introduction-Vnj2VNQROtI.pt-BR.vtt
06. Quick Fixes-Lb9e2KemR6I.zh-CN.vtt
06. Quick Fixes-Lb9e2KemR6I.en.vtt
03. Good GitHub repository-qBi8Q1EJdfQ.zh-CN.vtt
03. Good GitHub repository-qBi8Q1EJdfQ.en.vtt
05. Identify fixes for example “bad†profile-ncFtwW5urHk.ar.vtt
06. Quick Fixes-Lb9e2KemR6I.pt-BR.vtt
03. Good GitHub repository-qBi8Q1EJdfQ.pt-BR.vtt
09. Interview with Art - Part 2-Vvzl2J5K7-Y.zh-CN.vtt
09. Interview with Art - Part 2-Vvzl2J5K7-Y.en.vtt
14. Participating in open source projects 2-elZCLxVvJrY.ar.vtt
01. Introduction-Vnj2VNQROtI.ar.vtt
09. Interview with Art - Part 2-Vvzl2J5K7-Y.pt-BR.vtt
03. Good GitHub repository-qBi8Q1EJdfQ.ar.vtt
06. Quick Fixes-Lb9e2KemR6I.ar.vtt
02. GitHub profile important items-prvPVTjVkwQ.zh-CN.vtt
09. Interview with Art - Part 2-Vvzl2J5K7-Y.ar.vtt
02. GitHub profile important items-prvPVTjVkwQ.en.vtt
02. GitHub profile important items-prvPVTjVkwQ.pt-BR.vtt
04. Interview with Art - Part 1-ClLYamtaO-Q.zh-CN.vtt
13. Interview with Art - Part 3-M6PKr3S1rPg.zh-CN.vtt
04. Interview with Art - Part 1-ClLYamtaO-Q.en.vtt
02. GitHub profile important items-prvPVTjVkwQ.ar.vtt
04. Interview with Art - Part 1-ClLYamtaO-Q.pt-BR.vtt
13. Interview with Art - Part 3-M6PKr3S1rPg.en.vtt
13. Interview with Art - Part 3-M6PKr3S1rPg.pt-BR.vtt
04. Interview with Art - Part 1-ClLYamtaO-Q.ar.vtt
index.html
13. Interview with Art - Part 3-M6PKr3S1rPg.ar.vtt
06. Quick Fixes #1.html
03. Good GitHub repository.html
13. Interview with Art - Part 3.html
09. Interview with Art - Part 2.html
04. Interview with Art - Part 1.html
16. Outro.html
14. Participating in open source projects 2.html
08. Writing READMEs with Walter.html
01. Introduction.html
02. GitHub profile important items.html
12. Participating in open source projects.html
11. Reflect on your commit messages.html
07. Quick Fixes #2.html
17. Resources in Your Career Portal.html
Project Description - Optimize Your GitHub Profile.html
15. Starring interesting repositories.html
Project Rubric - Optimize Your GitHub Profile.html
10. Commit messages best practices.html
05. Identify fixes for example “bad†profile.html
img
career-portal-sidebar.png
6551597473.gif
6499079068.gif
6485174133.gif
6509638772.gif
screen-shot-2017-10-31-at-1.06.42-pm.png
05. Identify fixes for example “bad†profile-AF07y1oAim0.mp4
05. Identify fixes for example “bad†profile-ncFtwW5urHk.mp4
07. Quick Fixes #2-It6AEuSDQw0.mp4
15. Starring interesting repositories-U3FUxkm1MxI.mp4
12. Participating in open source projects-OxL-gMTizUA.mp4
11. Reflect on your commit messages-_0AHmKkfjTo.mp4
14. Participating in open source projects 2-elZCLxVvJrY.mp4
02. GitHub profile important items-prvPVTjVkwQ.mp4
15. Starring interesting repositories-ZwMY5rAAd7Q.mp4
03. Good GitHub repository-qBi8Q1EJdfQ.mp4
06. Quick Fixes-Lb9e2KemR6I.mp4
16. Outro-dps7Ti6Lado.mp4
08. Writing READMEs with Walter-DQEfT2Zq5_o.mp4
01. Introduction-Vnj2VNQROtI.mp4
09. Interview with Art - Part 2-Vvzl2J5K7-Y.mp4
04. Interview with Art - Part 1-ClLYamtaO-Q.mp4
13. Interview with Art - Part 3-M6PKr3S1rPg.mp4
Part 05-Module 01-Lesson 01_Neural Networks
10. DL 10 S Perceptron Algorithm-fATmrG2hQzI.pt-BR.vtt
10. DL 10 S Perceptron Algorithm-fATmrG2hQzI.zh-CN.vtt
10. DL 10 S Perceptron Algorithm-fATmrG2hQzI.en.vtt
15. Discrete vs Continuous-rdP-RPDFkl0.zh-CN.vtt
16. Quiz - Softmax-NNoezNnAMTY.en.vtt
16. Quiz - Softmax-NNoezNnAMTY.pt-BR.vtt
21. Formula For Cross 1-qvr_ego_d6w.zh-CN.vtt
16. Quiz - Softmax-NNoezNnAMTY.zh-CN.vtt
15. Discrete vs Continuous-rdP-RPDFkl0.en.vtt
15. Discrete vs Continuous-rdP-RPDFkl0.pt-BR.vtt
21. Formula For Cross 1-qvr_ego_d6w.en.vtt
21. Formula For Cross 1-qvr_ego_d6w.pt-BR.vtt
13. Error Functions-YfUUunxWIJw.zh-CN.vtt
13. Error Functions-YfUUunxWIJw.en.vtt
13. Error Functions-YfUUunxWIJw.pt-BR.vtt
19. Quiz - Cross 1--xxrisIvD0E.zh-CN.vtt
19. Quiz - Cross 1--xxrisIvD0E.en.vtt
img
codecogseqn-58.gif
codecogseqn-49.gif
codecogseqn-43.gif
codecogseqn-60-2.png
points.png
perceptronquiz.png
xor-quiz.png
meme.png
xor.png
and-quiz.png
or-quiz.png
and-to-or.png
student-quiz.png
19. Quiz - Cross 1--xxrisIvD0E.pt-BR.vtt
08. XOR Perceptron-TF83GfjYLdw.zh-CN.vtt
08. XOR Perceptron-TF83GfjYLdw.pt-BR.vtt
08. XOR Perceptron-TF83GfjYLdw.en.vtt
09. Why Neural Networks-zAkzOZntK6Y.zh-CN.vtt
09. Why Neural Networks-zAkzOZntK6Y.pt-BR.vtt
09. Why Neural Networks-zAkzOZntK6Y.en.vtt
23. DL 29 Logistic Regression-Minimizing The Error Function-KayqiYijlzc.pt-BR.vtt
18. Maximum Likelihood 1-1yJx-QtlvNI.zh-CN.vtt
23. DL 29 Logistic Regression-Minimizing The Error Function-KayqiYijlzc.zh-CN.vtt
12. Non-Linear Regions-B8UrWnHh1Wc.pt-BR.vtt
12. Non-Linear Regions-B8UrWnHh1Wc.zh-CN.vtt
04. Classification Example-46PywnGa_cQ.pt-BR.vtt
18. Maximum Likelihood 1-1yJx-QtlvNI.pt-BR.vtt
23. DL 29 Logistic Regression-Minimizing The Error Function-KayqiYijlzc.en.vtt
18. Maximum Likelihood 1-1yJx-QtlvNI.en.vtt
04. Classification Example-46PywnGa_cQ.zh-CN.vtt
04. Classification Example-46PywnGa_cQ.en.vtt
12. Non-Linear Regions-B8UrWnHh1Wc.en.vtt
17. One-Hot Encoding-AePvjhyvsBo.zh-CN.vtt
17. One-Hot Encoding-AePvjhyvsBo.pt-BR.vtt
19. Quiz Cross Entropy-njq6bYrPqSU.zh-CN.vtt
25. Gradient Descent Algorithm-snxmBgi_GeU.zh-CN.vtt
17. One-Hot Encoding-AePvjhyvsBo.en.vtt
19. Quiz Cross Entropy-njq6bYrPqSU.pt-BR.vtt
19. Quiz Cross Entropy-njq6bYrPqSU.en.vtt
16. DL 18 S Softmax-n8S-v_LCTms.zh-CN.vtt
10. Perceptron Algorithm--zhTROHtscQ.zh-CN.vtt
03. Classsification Example-Dh625piH7Z0.zh-CN.vtt
06. 09 Higher Dimensions-eBHunImDmWw.zh-CN.vtt
10. Perceptron Algorithm--zhTROHtscQ.pt-BR.vtt
08. AND And OR Perceptrons-45K5N0P9wJk.zh-CN.vtt
03. Classsification Example-Dh625piH7Z0.pt-BR.vtt
16. DL 18 S Softmax-n8S-v_LCTms.pt-BR.vtt
25. Gradient Descent Algorithm-snxmBgi_GeU.en.vtt
16. DL 18 S Softmax-n8S-v_LCTms.en.vtt
25. Gradient Descent Algorithm-snxmBgi_GeU.pt-BR.vtt
10. Perceptron Algorithm--zhTROHtscQ.en.vtt
06. 09 Higher Dimensions-eBHunImDmWw.pt-BR.vtt
03. Classsification Example-Dh625piH7Z0.en.vtt
02. Introduction-tn-CrUTkCUc.zh-CN.vtt
06. 09 Higher Dimensions-eBHunImDmWw.en.vtt
08. AND And OR Perceptrons-45K5N0P9wJk.en.vtt
02. Introduction-tn-CrUTkCUc.pt-BR.vtt
08. AND And OR Perceptrons-45K5N0P9wJk.pt-BR.vtt
11. Perceptron Agorithm Pseudocode-p8Q3yu9YqYk.pt-BR.vtt
02. Introduction-tn-CrUTkCUc.en.vtt
05. Linear Boundaries-X-uMlsBi07k.zh-CN.vtt
11. Perceptron Agorithm Pseudocode-p8Q3yu9YqYk.en.vtt
10. 07 Perceptron Algorithm Trick-lif_qPmXvWA.zh-CN.vtt
28. Gradient Descent Vs Perceptron Algorithm-uL5LuRPivTA.zh-CN.vtt
05. Linear Boundaries-X-uMlsBi07k.pt-BR.vtt
18. Maximum Likelihood 2-6nUUeQ9AeUA.zh-CN.vtt
24. Gradient Descent-rhVIF-nigrY.en.vtt
05. Linear Boundaries-X-uMlsBi07k.en.vtt
24. Gradient Descent-rhVIF-nigrY.pt-BR.vtt
22. DL 27 Multi-Class Cross Entropy 2 Fix-keDswcqkees.zh-CN.vtt
20. Cross Entropy 1-iREoPUrpXvE.zh-CN.vtt
10. 07 Perceptron Algorithm Trick-lif_qPmXvWA.en.vtt
23. Error Function-V5kkHldUlVU.zh-CN.vtt
10. 07 Perceptron Algorithm Trick-lif_qPmXvWA.pt-BR.vtt
28. Gradient Descent Vs Perceptron Algorithm-uL5LuRPivTA.pt-BR.vtt
28. Gradient Descent Vs Perceptron Algorithm-uL5LuRPivTA.en.vtt
16. DL 18 Q Softmax V2-RC_A9Tu99y4.zh-CN.vtt
18. Maximum Likelihood 2-6nUUeQ9AeUA.en.vtt
18. Maximum Likelihood 2-6nUUeQ9AeUA.pt-BR.vtt
22. DL 27 Multi-Class Cross Entropy 2 Fix-keDswcqkees.pt-BR.vtt
15. Discrete vs. Continuous-Rm2KxFaPiJg.zh-CN.vtt
22. DL 27 Multi-Class Cross Entropy 2 Fix-keDswcqkees.en.vtt
20. Cross Entropy 1-iREoPUrpXvE.en.vtt
23. Error Function-V5kkHldUlVU.en.vtt
07. DL 06 Perceptron Definition Fix V2-hImSxZyRiOw.zh-CN.vtt
20. Cross Entropy 1-iREoPUrpXvE.pt-BR.vtt
16. DL 18 Q Softmax V2-RC_A9Tu99y4.pt-BR.vtt
23. Error Function-V5kkHldUlVU.pt-BR.vtt
16. DL 18 Q Softmax V2-RC_A9Tu99y4.en.vtt
07. DL 06 Perceptron Definition Fix V2-hImSxZyRiOw.pt-BR.vtt
index.html
15. Discrete vs. Continuous-Rm2KxFaPiJg.pt-BR.vtt
15. Discrete vs. Continuous-Rm2KxFaPiJg.en.vtt
07. DL 06 Perceptron Definition Fix V2-hImSxZyRiOw.en.vtt
21. CrossEntropy V1-1BnhC6e0TFw.zh-CN.vtt
29. Outro.html
02. Introduction.html
13. Error Functions.html
17. One-Hot Encoding.html
12. Non-Linear Regions.html
09. Why Neural Networks.html
04. Classification Problems 2.html
25. Logistic Regression Algorithm.html
01. Announcement.html
20. Cross-Entropy 1.html
21. CrossEntropy V1-1BnhC6e0TFw.pt-BR.vtt
28. Perceptron vs Gradient Descent.html
27. Notebook Gradient Descent.html
21. CrossEntropy V1-1BnhC6e0TFw.en.vtt
05. Linear Boundaries.html
14. Error Functions-jfKShxGAbok.zh-CN.vtt
07. Perceptrons.html
22. Multi-Class Cross Entropy.html
06. Higher Dimensions.html
03. Classification Problems 1.html
14. Error Functions-jfKShxGAbok.pt-BR.vtt
14. Log-loss Error Function.html
23. Logistic Regression.html
26. Pre-Lab Gradient Descent.html
19. Maximizing Probabilities.html
14. Error Functions-jfKShxGAbok.en.vtt
15. Discrete vs Continuous.html
18. Maximum Likelihood.html
21. Cross-Entropy 2.html
10. Perceptron Trick.html
16. Softmax.html
11. Perceptron Algorithm.html
24. Gradient Descent.html
08. Perceptrons as Logical Operators.html
10. DL 10 S Perceptron Algorithm-fATmrG2hQzI.mp4
08. XOR Perceptron-TF83GfjYLdw.mp4
09. Why Neural Networks-zAkzOZntK6Y.mp4
12. Non-Linear Regions-B8UrWnHh1Wc.mp4
23. DL 29 Logistic Regression-Minimizing The Error Function-KayqiYijlzc.mp4
04. Classification Example-46PywnGa_cQ.mp4
17. One-Hot Encoding-AePvjhyvsBo.mp4
16. Quiz - Softmax-NNoezNnAMTY.mp4
19. Quiz Cross Entropy-njq6bYrPqSU.mp4
10. Perceptron Algorithm--zhTROHtscQ.mp4
16. DL 18 S Softmax-n8S-v_LCTms.mp4
25. Gradient Descent Algorithm-snxmBgi_GeU.mp4
03. Classsification Example-Dh625piH7Z0.mp4
21. Formula For Cross 1-qvr_ego_d6w.mp4
15. Discrete vs Continuous-rdP-RPDFkl0.mp4
06. 09 Higher Dimensions-eBHunImDmWw.mp4
08. AND And OR Perceptrons-45K5N0P9wJk.mp4
11. Perceptron Agorithm Pseudocode-p8Q3yu9YqYk.mp4
19. Quiz - Cross 1--xxrisIvD0E.mp4
28. Gradient Descent Vs Perceptron Algorithm-uL5LuRPivTA.mp4
29. Neural Networks Outro V2-pwA5shUkRVc.mp4
13. Error Functions-YfUUunxWIJw.mp4
10. 07 Perceptron Algorithm Trick-lif_qPmXvWA.mp4
24. Gradient Descent-rhVIF-nigrY.mp4
18. Maximum Likelihood 2-6nUUeQ9AeUA.mp4
05. Linear Boundaries-X-uMlsBi07k.mp4
16. DL 18 Q Softmax V2-RC_A9Tu99y4.mp4
22. DL 27 Multi-Class Cross Entropy 2 Fix-keDswcqkees.mp4
20. Cross Entropy 1-iREoPUrpXvE.mp4
23. Error Function-V5kkHldUlVU.mp4
07. DL 06 Perceptron Definition Fix V2-hImSxZyRiOw.mp4
15. Discrete vs. Continuous-Rm2KxFaPiJg.mp4
18. Maximum Likelihood 1-1yJx-QtlvNI.mp4
21. CrossEntropy V1-1BnhC6e0TFw.mp4
14. Error Functions-jfKShxGAbok.mp4
02. Introduction-tn-CrUTkCUc.mp4
Part 03-Module 01-Lesson 02_Perceptron Algorithm
08. DL 10 S Perceptron Algorithm-fATmrG2hQzI.pt-BR.vtt
08. DL 10 S Perceptron Algorithm-fATmrG2hQzI.zh-CN.vtt
08. DL 10 S Perceptron Algorithm-fATmrG2hQzI.en.vtt
01. Perception Algorithm V2-ebIlG6Pqwas.zh-CN.vtt
01. Perception Algorithm V2-ebIlG6Pqwas.pt-BR.vtt
07. XOR Perceptron-TF83GfjYLdw.zh-CN.vtt
07. XOR Perceptron-TF83GfjYLdw.pt-BR.vtt
07. XOR Perceptron-TF83GfjYLdw.en.vtt
01. Perception Algorithm V2-ebIlG6Pqwas.en.vtt
03. Classification Example-46PywnGa_cQ.pt-BR.vtt
03. Classification Example-46PywnGa_cQ.zh-CN.vtt
03. Classification Example-46PywnGa_cQ.en.vtt
08. Perceptron Algorithm--zhTROHtscQ.zh-CN.vtt
02. Classsification Example-Dh625piH7Z0.zh-CN.vtt
05. 09 Higher Dimensions-eBHunImDmWw.zh-CN.vtt
08. Perceptron Algorithm--zhTROHtscQ.pt-BR.vtt
07. AND And OR Perceptrons-45K5N0P9wJk.zh-CN.vtt
02. Classsification Example-Dh625piH7Z0.pt-BR.vtt
08. Perceptron Algorithm--zhTROHtscQ.en.vtt
05. 09 Higher Dimensions-eBHunImDmWw.pt-BR.vtt
02. Classsification Example-Dh625piH7Z0.en.vtt
05. 09 Higher Dimensions-eBHunImDmWw.en.vtt
07. AND And OR Perceptrons-45K5N0P9wJk.en.vtt
07. AND And OR Perceptrons-45K5N0P9wJk.pt-BR.vtt
09. Perceptron Agorithm Pseudocode-p8Q3yu9YqYk.pt-BR.vtt
04. Linear Boundaries-X-uMlsBi07k.zh-CN.vtt
09. Perceptron Agorithm Pseudocode-p8Q3yu9YqYk.en.vtt
08. 07 Perceptron Algorithm Trick-lif_qPmXvWA.zh-CN.vtt
04. Linear Boundaries-X-uMlsBi07k.pt-BR.vtt
04. Linear Boundaries-X-uMlsBi07k.en.vtt
08. 07 Perceptron Algorithm Trick-lif_qPmXvWA.en.vtt
index.html
08. 07 Perceptron Algorithm Trick-lif_qPmXvWA.pt-BR.vtt
06. DL 06 Perceptron Definition Fix V2-hImSxZyRiOw.zh-CN.vtt
10. Outro.html
01. Intro.html
03. Classification Problems 2.html
06. DL 06 Perceptron Definition Fix V2-hImSxZyRiOw.pt-BR.vtt
06. DL 06 Perceptron Definition Fix V2-hImSxZyRiOw.en.vtt
04. Linear Boundaries.html
06. Perceptrons.html
05. Higher Dimensions.html
02. Classification Problems 1.html
08. Perceptron Trick.html
09. Perceptron Algorithm.html
07. Perceptrons as Logical Operators.html
img
points.png
perceptronquiz.png
xor-quiz.png
meme.png
xor.png
and-quiz.png
or-quiz.png
and-to-or.png
student-quiz.png
08. DL 10 S Perceptron Algorithm-fATmrG2hQzI.mp4
07. XOR Perceptron-TF83GfjYLdw.mp4
03. Classification Example-46PywnGa_cQ.mp4
08. Perceptron Algorithm--zhTROHtscQ.mp4
02. Classsification Example-Dh625piH7Z0.mp4
05. 09 Higher Dimensions-eBHunImDmWw.mp4
07. AND And OR Perceptrons-45K5N0P9wJk.mp4
09. Perceptron Agorithm Pseudocode-p8Q3yu9YqYk.mp4
08. 07 Perceptron Algorithm Trick-lif_qPmXvWA.mp4
04. Linear Boundaries-X-uMlsBi07k.mp4
06. DL 06 Perceptron Definition Fix V2-hImSxZyRiOw.mp4
01. Perception Algorithm V2-ebIlG6Pqwas.mp4
Part 05-Module 01-Lesson 03_Deep Neural Networks
20. Random Restart-idyBBCzXiqg.zh-CN.vtt
20. Random Restart-idyBBCzXiqg.en.vtt
20. Random Restart-idyBBCzXiqg.pt-BR.vtt
26. Keras Lab-a50un22BsLI.zh-CN.vtt
26. Keras Lab-a50un22BsLI.pt-BR.vtt
26. Keras Lab-a50un22BsLI.en.vtt
01. Non-Linear Data-F7ZiE8PQiSc.pt-BR.vtt
01. Non-Linear Data-F7ZiE8PQiSc.zh-CN.vtt
01. Non-Linear Data-F7ZiE8PQiSc.en.vtt
29. Conclusion-wOiUQDgGD9E.zh-CN.vtt
29. Conclusion-wOiUQDgGD9E.en.vtt
10. Training Optimization-UiGKhx9pUYc.en.vtt
10. Training Optimization-UiGKhx9pUYc.zh-CN.vtt
10. Training Optimization-UiGKhx9pUYc.pt-BR.vtt
19. Learning Rate-TwJ8aSZoh2U.zh-CN.vtt
15. Local Minima-gF_sW_nY-xw.zh-CN.vtt
12. DL 53 Q Regularization-KxROxcRsHL8.zh-CN.vtt
29. Conclusion-wOiUQDgGD9E.pt-BR.vtt
15. Local Minima-gF_sW_nY-xw.pt-BR.vtt
23. Error Functions Around the World-34AAcTECu2A.zh-CN.vtt
23. Error Functions Around the World-34AAcTECu2A.pt-BR.vtt
19. Learning Rate-TwJ8aSZoh2U.en.vtt
03. Non-Linear Models-HWuBKCZsCo8.zh-CN.vtt
15. Local Minima-gF_sW_nY-xw.en.vtt
12. DL 53 Q Regularization-KxROxcRsHL8.en.vtt
02. Continuous Perceptrons-07-JJ-aGEfM.zh-CN.vtt
12. DL 53 Q Regularization-KxROxcRsHL8.pt-BR.vtt
23. Error Functions Around the World-34AAcTECu2A.en.vtt
16. Vanishing Gradient-W_JJm_5syFw.zh-CN.vtt
19. Learning Rate-TwJ8aSZoh2U.pt-BR.vtt
03. Non-Linear Models-HWuBKCZsCo8.en.vtt
02. Continuous Perceptrons-07-JJ-aGEfM.pt-BR.vtt
02. Continuous Perceptrons-07-JJ-aGEfM.en.vtt
03. Non-Linear Models-HWuBKCZsCo8.pt-BR.vtt
06. Chain Rule-YAhIBOnbt54.zh-CN.vtt
16. Vanishing Gradient-W_JJm_5syFw.en.vtt
16. Vanishing Gradient-W_JJm_5syFw.pt-BR.vtt
06. Chain Rule-YAhIBOnbt54.en.vtt
05. DL 42 Neural Network Error Function (1)-SC1wEW7TtKs.zh-CN.vtt
06. Chain Rule-YAhIBOnbt54.pt-BR.vtt
04. Multiclass Classification-uNTtvxwfox0.zh-CN.vtt
05. DL 42 Neural Network Error Function (1)-SC1wEW7TtKs.en.vtt
04. Multiclass Classification-uNTtvxwfox0.en.vtt
img
sigmoid-derivative.gif
student-acceptance.png
data.png
regularization-quiz.png
summary.png
nn.png
all-ranks.png
04. Multiclass Classification-uNTtvxwfox0.pt-BR.vtt
05. DL 42 Neural Network Error Function (1)-SC1wEW7TtKs.pt-BR.vtt
21. Momentum-r-rYz_PEWC8.zh-CN.vtt
17. Other Activation Functions-kA-1vUt6cvQ.zh-CN.vtt
21. Momentum-r-rYz_PEWC8.en.vtt
17. Other Activation Functions-kA-1vUt6cvQ.pt-BR.vtt
17. Other Activation Functions-kA-1vUt6cvQ.en.vtt
21. Momentum-r-rYz_PEWC8.pt-BR.vtt
04. 29 Neural Network Architecture 2-FWN3Sw5fFoM.zh-CN.vtt
06. Calculating The Gradient 1 -tVuZDbUrzzI.zh-CN.vtt
04. 29 Neural Network Architecture 2-FWN3Sw5fFoM.en.vtt
04. Layers-pg99FkXYK0M.zh-CN.vtt
04. Layers-pg99FkXYK0M.pt-BR.vtt
04. 29 Neural Network Architecture 2-FWN3Sw5fFoM.pt-BR.vtt
04. Layers-pg99FkXYK0M.en.vtt
06. Calculating The Gradient 1 -tVuZDbUrzzI.en.vtt
06. Calculating The Gradient 1 -tVuZDbUrzzI.pt-BR.vtt
24. Neural Network Regression-aUJCBqBfEnI.pt-BR.vtt
14. Dropout-Ty6K6YiGdBs.zh-CN.vtt
18. Batch vs Stochastic Gradient Descent-2p58rVgqsgo.zh-CN.vtt
04. Combinando modelos-Boy3zHVrWB4.zh-CN.vtt
18. Batch vs Stochastic Gradient Descent-2p58rVgqsgo.pt-BR.vtt
18. Batch vs Stochastic Gradient Descent-2p58rVgqsgo.en.vtt
11. Model Complexity Graph-NnS0FJyVcDQ.zh-CN.vtt
14. Dropout-Ty6K6YiGdBs.pt-BR.vtt
14. Dropout-Ty6K6YiGdBs.en.vtt
06. DL 46 Calculating The Gradient 2 V2 (2)-7lidiTGIlN4.zh-CN.vtt
04. Combinando modelos-Boy3zHVrWB4.en.vtt
04. Combinando modelos-Boy3zHVrWB4.pt-BR.vtt
11. Model Complexity Graph-NnS0FJyVcDQ.en.vtt
05. DL 41 Feedforward FIX V2-hVCuvMGOfyY.zh-CN.vtt
11. Model Complexity Graph-NnS0FJyVcDQ.pt-BR.vtt
index.html
06. DL 46 Calculating The Gradient 2 V2 (2)-7lidiTGIlN4.en.vtt
05. DL 41 Feedforward FIX V2-hVCuvMGOfyY.en.vtt
06. Backpropagation V2-1SmY3TZTyUk.zh-CN.vtt
06. DL 46 Calculating The Gradient 2 V2 (2)-7lidiTGIlN4.pt-BR.vtt
05. DL 41 Feedforward FIX V2-hVCuvMGOfyY.pt-BR.vtt
13. Regularization-ndYnUrx8xvs.zh-CN.vtt
06. Backpropagation V2-1SmY3TZTyUk.pt-BR.vtt
06. Backpropagation V2-1SmY3TZTyUk.en.vtt
24. Neural Network Regression.html
14. Dropout.html
21. Momentum.html
29. Outro.html
26. Mini Project Intro.html
15. Local Minima.html
20. Random Restart.html
13. Regularization 2.html
19. Learning Rate Decay.html
01. Non-linear Data.html
03. Non-Linear Models.html
16. Vanishing Gradient.html
11. Early Stopping.html
10. Training Optimization.html
02. Continuous Perceptrons.html
23. Error Functions Around the World.html
18. Batch vs Stochastic Gradient Descent.html
17. Other Activation Functions.html
28. Lab IMDB Data in Keras.html
09. Lab Student Admissions in Keras.html
25. Neural Networks Playground.html
13. Regularization-ndYnUrx8xvs.en.vtt
22. Optimizers in Keras.html
12. Regularization.html
13. Regularization-ndYnUrx8xvs.pt-BR.vtt
05. Feedforward.html
06. Backpropagation.html
27. Pre-Lab IMDB Data in Keras.html
04. Neural Network Architecture.html
08. Pre-Lab Student Admissions in Keras.html
07. Keras.html
20. Random Restart-idyBBCzXiqg.mp4
15. Local Minima-gF_sW_nY-xw.mp4
19. Learning Rate-TwJ8aSZoh2U.mp4
12. DL 53 Q Regularization-KxROxcRsHL8.mp4
03. Non-Linear Models-HWuBKCZsCo8.mp4
02. Continuous Perceptrons-07-JJ-aGEfM.mp4
16. Vanishing Gradient-W_JJm_5syFw.mp4
06. Chain Rule-YAhIBOnbt54.mp4
05. DL 42 Neural Network Error Function (1)-SC1wEW7TtKs.mp4
23. Error Functions Around the World-34AAcTECu2A.mp4
04. Multiclass Classification-uNTtvxwfox0.mp4
01. Non-Linear Data-F7ZiE8PQiSc.mp4
21. Momentum-r-rYz_PEWC8.mp4
26. Keras Lab-a50un22BsLI.mp4
17. Other Activation Functions-kA-1vUt6cvQ.mp4
29. Conclusion-wOiUQDgGD9E.mp4
04. 29 Neural Network Architecture 2-FWN3Sw5fFoM.mp4
10. Training Optimization-UiGKhx9pUYc.mp4
04. Layers-pg99FkXYK0M.mp4
06. Calculating The Gradient 1 -tVuZDbUrzzI.mp4
24. Neural Network Regression-aUJCBqBfEnI.mp4
18. Batch vs Stochastic Gradient Descent-2p58rVgqsgo.mp4
14. Dropout-Ty6K6YiGdBs.mp4
04. Combinando modelos-Boy3zHVrWB4.mp4
11. Model Complexity Graph-NnS0FJyVcDQ.mp4
05. DL 41 Feedforward FIX V2-hVCuvMGOfyY.mp4
06. DL 46 Calculating The Gradient 2 V2 (2)-7lidiTGIlN4.mp4
06. Backpropagation V2-1SmY3TZTyUk.mp4
13. Regularization-ndYnUrx8xvs.mp4
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun
13. 13 Quiz Sensitivity And Specificty V3-O17MnhWBmKA.pt-BR.vtt
16. 15 Quiz Diagnosing Cancer V3-4UzkwecBJro.zh-CN.vtt
07. 07 Quiz Data Challenges V1-F8yc7BlV93c.zh-CN.vtt
16. 15 Quiz Diagnosing Cancer V3-4UzkwecBJro.pt-BR.vtt
28. Mini Project Introduction-Rgf3YVFWl-M.zh-CN.vtt
07. 07 Quiz Data Challenges V1-F8yc7BlV93c.pt-BR.vtt
13. 13 Quiz Sensitivity And Specificty V3-O17MnhWBmKA.zh-CN.vtt
13. 13 Quiz Sensitivity And Specificty V3-O17MnhWBmKA.en.vtt
16. 15 Quiz Diagnosing Cancer V3-4UzkwecBJro.en.vtt
28. Mini Project Introduction-Rgf3YVFWl-M.en.vtt
07. 07 Quiz Data Challenges V1-F8yc7BlV93c.en.vtt
28. Mini Project Introduction-Rgf3YVFWl-M.pt-BR.vtt
20. Solution ROC Curve-sdUUf6RRmXI.pt-BR.vtt
19. 17 Quiz ROC Curve 1 PT2 V1-Xv3v59_CfEU.pt-BR.vtt
19. 17 Quiz ROC Curve 1 PT2 V1-Xv3v59_CfEU.zh-CN.vtt
08. Solution Data Challenges-1z3o4niQuNg.pt-BR.vtt
10. 10 Quiz Random Vs Preinitiliazed Weights V3-DRC1e4XGl2M.zh-CN.vtt
10. 10 Quiz Random Vs Preinitiliazed Weights V3-DRC1e4XGl2M.pt-BR.vtt
14. Solution Sensitivty And Specificity-GBZjyeMjKxc.zh-CN.vtt
14. Solution Sensitivty And Specificity-GBZjyeMjKxc.pt-BR.vtt
19. 17 Quiz ROC Curve 1 PT2 V1-Xv3v59_CfEU.en.vtt
08. Solution Data Challenges-1z3o4niQuNg.zh-CN.vtt
01. Introduction-ZCpXvVdIdnY.zh-CN.vtt
20. Solution ROC Curve-sdUUf6RRmXI.zh-CN.vtt
14. Solution Sensitivty And Specificity-GBZjyeMjKxc.en.vtt
10. 10 Quiz Random Vs Preinitiliazed Weights V3-DRC1e4XGl2M.en.vtt
01. Introduction-ZCpXvVdIdnY.pt-BR.vtt
08. Solution Data Challenges-1z3o4niQuNg.en.vtt
01. Introduction-ZCpXvVdIdnY.en.vtt
06. 06 Image Challenge V3-Efnoj1KNPHw.zh-CN.vtt
06. 06 Image Challenge V3-Efnoj1KNPHw.pt-BR.vtt
20. Solution ROC Curve-sdUUf6RRmXI.en.vtt
11. Solution Random Vs Preinitialized Thoughts-sOuoRZRKDzs.zh-CN.vtt
09. Training The Neural Network-HwiI-UXUx-M.pt-BR.vtt
09. Training The Neural Network-HwiI-UXUx-M.zh-CN.vtt
03. Survival Rate-QPlp3NeGuSk.zh-CN.vtt
11. Solution Random Vs Preinitialized Thoughts-sOuoRZRKDzs.pt-BR.vtt
06. 06 Image Challenge V3-Efnoj1KNPHw.en.vtt
03. Survival Rate-QPlp3NeGuSk.pt-BR.vtt
09. Training The Neural Network-HwiI-UXUx-M.en.vtt
11. Solution Random Vs Preinitialized Thoughts-sOuoRZRKDzs.en.vtt
04. Medical Classification-RCOSP60dV7U.zh-CN.vtt
04. Medical Classification-RCOSP60dV7U.pt-BR.vtt
03. Survival Rate-QPlp3NeGuSk.en.vtt
17. 16 Solution Diagnosing Cancer V3-IJYvt2ssUFk.zh-CN.vtt
17. 16 Solution Diagnosing Cancer V3-IJYvt2ssUFk.pt-BR.vtt
04. Medical Classification-RCOSP60dV7U.en.vtt
25. Confusion Matrix-3rpN-YYlfes.zh-CN.vtt
17. 16 Solution Diagnosing Cancer V3-IJYvt2ssUFk.en.vtt
22. Visualization-aGIGB4Ta3_A.zh-CN.vtt
25. Confusion Matrix-3rpN-YYlfes.pt-BR.vtt
05. The Data-2RLbbV7MQNA.zh-CN.vtt
25. Confusion Matrix-3rpN-YYlfes.en.vtt
02. 02 Skin Cancer V4-70jGZeiTNgk.zh-CN.vtt
05. The Data-2RLbbV7MQNA.pt-BR.vtt
23. What Is The Neural Network Looking At-qN-rvoxPbBw.pt-BR.vtt
02. 02 Skin Cancer V4-70jGZeiTNgk.pt-BR.vtt
22. Visualization-aGIGB4Ta3_A.pt-BR.vtt
22. Visualization-aGIGB4Ta3_A.en.vtt
02. 02 Skin Cancer V4-70jGZeiTNgk.en.vtt
23. What Is The Neural Network Looking At-qN-rvoxPbBw.zh-CN.vtt
05. The Data-2RLbbV7MQNA.en.vtt
23. What Is The Neural Network Looking At-qN-rvoxPbBw.en.vtt
12. Validating The Training-Oxm9ofvov3I.pt-BR.vtt
12. Validating The Training-Oxm9ofvov3I.zh-CN.vtt
21. ROC Curve-fWwe_JlpnlQ.zh-CN.vtt
21. ROC Curve-fWwe_JlpnlQ.pt-BR.vtt
12. Validating The Training-Oxm9ofvov3I.en.vtt
21. ROC Curve-fWwe_JlpnlQ.en.vtt
26. Conclusion-WhpE_8sTt-0.zh-CN.vtt
26. Conclusion-WhpE_8sTt-0.pt-BR.vtt
26. Conclusion-WhpE_8sTt-0.en.vtt
24. Confusion Matrix-Question 1-9GLNjmMUB_4.pt-BR.vtt
24. Confusion Matrix-Question 1-9GLNjmMUB_4.zh-CN.vtt
24. Confusion Matrix-Question 1-9GLNjmMUB_4.en-US.vtt
24. Confusion Matrix-Question 1-9GLNjmMUB_4.en.vtt
index.html
18. ROC Curve-2Iw5TiGzJI4.zh-CN.vtt
05. The data.html
26. Conclusion.html
01. Intro.html
22. Visualization.html
25. Confusion Matrix.html
21. Comparing our Results with Doctors.html
20. Solution ROC Curve.html
03. Survival Probability of Skin Cancer.html
04. Medical Classification.html
12. Validating the Training.html
08. Solution Data Challenges.html
28. Mini Project Introduction.html
09. Training the Neural Network.html
23. What is the network looking at.html
14. Solution Sensitivity and Specificity.html
11. Solution Random vs Pre-initialized Weight.html
18. ROC Curve-2Iw5TiGzJI4.pt-BR.vtt
06. Image Challenges.html
18. ROC Curve-2Iw5TiGzJI4.en.vtt
16. Quiz Diagnosing Cancer.html
10. Quiz Random vs Pre-initialized Weights.html
27. Useful Resources.html
19. Quiz ROC Curve.html
07. Quiz Data Challenges.html
13. Quiz Sensitivity and Specificity.html
02. Skin Cancer.html
17. Solution Diagnosing Cancer.html
15. More on Sensitivity and Specificity.html
18. Refresh on ROC Curves.html
24. Refresh on Confusion Matrices.html
29. Mini Project Dermatologist AI.html
img
roc-curve.png
sample-roc-curve.png
roc.png
sample-confusion-matrix.png
roc-curves.png
sensitivity-specificity.png
precision-recall.png
new-confusion-matrix.png
cat-1.jpeg
cat-2.jpeg
confusion-matrix.png
threshold.png
cat-3.png
nature.png
lesions.png
skin-disease-classes.png
media
monkey-doctor.png
18. Images-1GdiN5Wc8LA.mp4
07. 07 Quiz Data Challenges V1-F8yc7BlV93c.mp4
13. 13 Quiz Sensitivity And Specificty V3-O17MnhWBmKA.mp4
19. 17 Quiz ROC Curve 1 PT2 V1-Xv3v59_CfEU.mp4
20. Solution ROC Curve-sdUUf6RRmXI.mp4
16. 15 Quiz Diagnosing Cancer V3-4UzkwecBJro.mp4
28. Mini Project Introduction-Rgf3YVFWl-M.mp4
09. Training The Neural Network-HwiI-UXUx-M.mp4
06. 06 Image Challenge V3-Efnoj1KNPHw.mp4
08. Solution Data Challenges-1z3o4niQuNg.mp4
03. Survival Rate-QPlp3NeGuSk.mp4
01. Introduction-ZCpXvVdIdnY.mp4
14. Solution Sensitivty And Specificity-GBZjyeMjKxc.mp4
10. 10 Quiz Random Vs Preinitiliazed Weights V3-DRC1e4XGl2M.mp4
04. Medical Classification-RCOSP60dV7U.mp4
17. 16 Solution Diagnosing Cancer V3-IJYvt2ssUFk.mp4
22. Visualization-aGIGB4Ta3_A.mp4
21. ROC Curve-fWwe_JlpnlQ.mp4
11. Solution Random Vs Preinitialized Thoughts-sOuoRZRKDzs.mp4
05. The Data-2RLbbV7MQNA.mp4
25. Confusion Matrix-3rpN-YYlfes.mp4
23. What Is The Neural Network Looking At-qN-rvoxPbBw.mp4
02. 02 Skin Cancer V4-70jGZeiTNgk.mp4
24. Confusion Matrix-Question 1-9GLNjmMUB_4.mp4
12. Validating The Training-Oxm9ofvov3I.mp4
18. ROC Curve-2Iw5TiGzJI4.mp4
26. Conclusion-WhpE_8sTt-0.mp4
Part 03-Module 01-Lesson 05_Support Vector Machines
01. Support Vector Machine V2-LBmM6pZCrI0.zh-CN.vtt
09. SVM 07 Error Function V1-A1wbrcSYc1c.pt-BR.vtt
09. SVM 07 Error Function V1-A1wbrcSYc1c.zh-CN.vtt
01. Support Vector Machine V2-LBmM6pZCrI0.en.vtt
09. SVM 07 Error Function V1-A1wbrcSYc1c.en.vtt
01. Support Vector Machine V2-LBmM6pZCrI0.pt-BR.vtt
02. SVM 01 Which Line Is Better V1-NCml_NCvd1I.zh-CN.vtt
02. SVM 01 Which Line Is Better V1-NCml_NCvd1I.pt-BR.vtt
02. SVM 01 Which Line Is Better V1-NCml_NCvd1I.en.vtt
03. SVM 02 Minimizing Distances V1-mNKk2dBsNGA.pt-BR.vtt
15. SVM 13 RBF Kernel 2 V1-ozl9UWVP0MI.zh-CN.vtt
15. SVM 13 RBF Kernel 2 V1-ozl9UWVP0MI.pt-BR.vtt
03. SVM 02 Minimizing Distances V1-mNKk2dBsNGA.zh-CN.vtt
15. SVM 13 RBF Kernel 2 V1-ozl9UWVP0MI.en.vtt
03. SVM 02 Minimizing Distances V1-mNKk2dBsNGA.en.vtt
04. SVM 03 Error Function V1-l-ahImxoi-U.zh-CN.vtt
04. SVM 03 Error Function V1-l-ahImxoi-U.pt-BR.vtt
10. SVM 08 The C Parameter V2-6CxPhVo0hRw.zh-CN.vtt
10. SVM 08 The C Parameter V2-6CxPhVo0hRw.pt-BR.vtt
11. SVM 09 Polynomial Kernel 1 V1-8t2tVDHNBnk.pt-BR.vtt
04. SVM 03 Error Function V1-l-ahImxoi-U.en.vtt
16. SVM 14 RBF Kernel 3 V1-DctkE8kaWPY.pt-BR.vtt
11. SVM 09 Polynomial Kernel 1 V1-8t2tVDHNBnk.zh-CN.vtt
10. SVM 08 The C Parameter V2-6CxPhVo0hRw.en.vtt
06. SVM 05 Classification Error V1-nWGVAGXwvGE.zh-CN.vtt
11. SVM 09 Polynomial Kernel 1 V1-8t2tVDHNBnk.en.vtt
16. SVM 14 RBF Kernel 3 V1-DctkE8kaWPY.zh-CN.vtt
06. SVM 05 Classification Error V1-nWGVAGXwvGE.pt-BR.vtt
12. SVM 10 Polynomial Kernel 2 V2-9RfFvZ9DIRg.pt-BR.vtt
16. SVM 14 RBF Kernel 3 V1-DctkE8kaWPY.en.vtt
06. SVM 05 Classification Error V1-nWGVAGXwvGE.en.vtt
12. SVM 10 Polynomial Kernel 2 V2-9RfFvZ9DIRg.zh-CN.vtt
05. SVM 04 Perceptron Algorithm V1-IIlQHBOrD6Q.zh-CN.vtt
05. SVM 04 Perceptron Algorithm V1-IIlQHBOrD6Q.pt-BR.vtt
12. SVM 10 Polynomial Kernel 2 V2-9RfFvZ9DIRg.en.vtt
05. SVM 04 Perceptron Algorithm V1-IIlQHBOrD6Q.en.vtt
index.html
07. SVM 06 Margin Error V2-dSac8Gfgbok.pt-BR.vtt
07. SVM 06 Margin Error V2-dSac8Gfgbok.zh-CN.vtt
14. SVM 12 RBF Kernel 1 V3-xdkIulxXWfQ.pt-BR.vtt
18. Outro.html
16. RBF Kernel 3.html
14. RBF Kernel 1.html
15. RBF Kernel 2.html
07. Margin Error.html
01. Intro.html
09. Error Function.html
10. The C Parameter.html
04. Error Function Intuition.html
11. Polynomial Kernel 1.html
13. Polynomial Kernel 3.html
03. Minimizing Distances.html
06. Classification Error.html
14. SVM 12 RBF Kernel 1 V3-xdkIulxXWfQ.zh-CN.vtt
07. SVM 06 Margin Error V2-dSac8Gfgbok.en.vtt
05. Perceptron Algorithm.html
02. Which line is better.html
14. SVM 12 RBF Kernel 1 V3-xdkIulxXWfQ.en.vtt
12. Polynomial Kernel 2.html
13. SVM 11 Polynomial Kernel 3 V1-XmbK8OjbX5U.pt-BR.vtt
13. SVM 11 Polynomial Kernel 3 V1-XmbK8OjbX5U.zh-CN.vtt
13. SVM 11 Polynomial Kernel 3 V1-XmbK8OjbX5U.en.vtt
08. (Optional) Margin Error Calculation.html
17. SVMs in sklearn.html
img
screen-shot-2018-01-06-at-8.13.20-pm.png
polynomial-kernel-2-quiz.png
screen-shot-2017-08-09-at-7.09.54-pm.png
margin-geometry-images.002.jpeg
margin-geometry-images.001.jpeg
margin-geometry-images.003.jpeg
margin-geometry-images.004.jpeg
margin-geometry-images.005.jpeg
screen-shot-2018-01-06-at-10.44.48-pm.png
margin-geometry-images.008.jpeg
02. SVM 01 Which Line Is Better V1-NCml_NCvd1I.mp4
09. SVM 07 Error Function V1-A1wbrcSYc1c.mp4
01. Support Vector Machine V2-LBmM6pZCrI0.mp4
03. SVM 02 Minimizing Distances V1-mNKk2dBsNGA.mp4
15. SVM 13 RBF Kernel 2 V1-ozl9UWVP0MI.mp4
04. SVM 03 Error Function V1-l-ahImxoi-U.mp4
10. SVM 08 The C Parameter V2-6CxPhVo0hRw.mp4
11. SVM 09 Polynomial Kernel 1 V1-8t2tVDHNBnk.mp4
16. SVM 14 RBF Kernel 3 V1-DctkE8kaWPY.mp4
12. SVM 10 Polynomial Kernel 2 V2-9RfFvZ9DIRg.mp4
06. SVM 05 Classification Error V1-nWGVAGXwvGE.mp4
05. SVM 04 Perceptron Algorithm V1-IIlQHBOrD6Q.mp4
14. SVM 12 RBF Kernel 1 V3-xdkIulxXWfQ.mp4
07. SVM 06 Margin Error V2-dSac8Gfgbok.mp4
13. SVM 11 Polynomial Kernel 3 V1-XmbK8OjbX5U.mp4
Part 02-Module 03-Lesson 01_Model Selection
13. MLND Outro-sFvMBncQjr8.zh-CN.vtt
13. MLND Outro-sFvMBncQjr8.en.vtt
13. MLND Outro-sFvMBncQjr8.pt-BR.vtt
12. Outro SC V1-YD1grQje9fw.en.vtt
12. Outro SC V1-YD1grQje9fw.pt-BR.vtt
04. KFold Cross Validation V3 V1-9W6o6eWGi-0.pt-BR.vtt
02. Model Complexity Graph-Question-YS5OQCA5cLY.zh-CN.vtt
02. Model Complexity Graph-Question-YS5OQCA5cLY.pt-BR.vtt
02. Model Complexity Graph-Question-YS5OQCA5cLY.en-US.vtt
08. Grid Search SC V1-zDw-ZGiHW5I.en.vtt
08. Grid Search SC V1-zDw-ZGiHW5I.pt-BR.vtt
index.html
03. Model-Complexity-Graph Solution 2-5pWHGkNyRhA.zh-CN.vtt
03. Model-Complexity-Graph Solution 2-5pWHGkNyRhA.pt-BR.vtt
04. K-Fold Cross Validation.html
12. Summary.html
13. Outro.html
01. Types of Errors.html
01. 04 L Types Of Errors-Twf1qnPZeSY.zh-CN.vtt
03. Cross Validation.html
05. Learning Curves.html
03. Model-Complexity-Graph Solution 2-5pWHGkNyRhA.en-US.vtt
08. Grid Search.html
01. 04 L Types Of Errors-Twf1qnPZeSY.pt-BR.vtt
10. Grid Search Lab.html
11. [Solution] Grid Search Lab.html
01. 04 L Types Of Errors-Twf1qnPZeSY.en-US.vtt
02. Model Complexity Graph.html
09. Grid Search in sklearn.html
05. Learning Curves SC V1-ZNhnNVKl8NM.en.vtt
05. Learning Curves SC V1-ZNhnNVKl8NM.pt-BR.vtt
07. Solution Detecting Overfitting and Underfitting.html
06. Detecting Overfitting and Underfitting with Learning Curves.html
img
circle-data.png
complexity.png
learning-curves.png
models.png
12. Outro SC V1-YD1grQje9fw.mp4
04. KFold Cross Validation V3 V1-9W6o6eWGi-0.mp4
13. MLND Outro-sFvMBncQjr8.mp4
08. Grid Search SC V1-zDw-ZGiHW5I.mp4
02. Model Complexity Graph-Question-YS5OQCA5cLY.mp4
05. Learning Curves SC V1-ZNhnNVKl8NM.mp4
01. 04 L Types Of Errors-Twf1qnPZeSY.mp4
03. Model-Complexity-Graph Solution 2-5pWHGkNyRhA.mp4
README.txt
Part 03-Module 01-Lesson 06_Ensemble Methods
11. Supervised Learning Outro V2-7X2SDqzGrdU.zh-CN.vtt
11. Supervised Learning Outro V2-7X2SDqzGrdU.en.vtt
05. MLND SL EM 05 Weighting The Models MAIN V1-wn6K536dPLc.pt-BR.vtt
05. MLND SL EM 05 Weighting The Models MAIN V1-wn6K536dPLc.en.vtt
08. MLND SL EM 08 Combining The Models V1 MAIN V1-1GxscvKU2Ic.pt-BR.vtt
08. MLND SL EM 08 Combining The Models V1 MAIN V1-1GxscvKU2Ic.en.vtt
04. MLND SL EM 04 Weighting The Data MAIN V1 V2-O-hh_x0iYW8.en.vtt
03. MLND SL EM 03 AdaBoost V1 MAIN V1-HD6SRBWKGUE.pt-BR.vtt
04. MLND SL EM 04 Weighting The Data MAIN V1 V2-O-hh_x0iYW8.pt-BR.vtt
03. MLND SL EM 03 AdaBoost V1 MAIN V1-HD6SRBWKGUE.en.vtt
02. MLND SL EM 02 Bagging V1 MAIN V1-9L_B0Jcio3c.pt-BR.vtt
02. MLND SL EM 02 Bagging V1 MAIN V1-9L_B0Jcio3c.en.vtt
07. MLND SL EM 07 Weighting The Models 3 V1 MAIN V1-fecp5nmetws.en.vtt
07. MLND SL EM 07 Weighting The Models 3 V1 MAIN V1-fecp5nmetws.pt-BR.vtt
06. MLND SL EM 06 Weighting The Models MAIN V2-unCJ_ifVquU.en.vtt
01. MLND SL EM 01 Intro V1 MAIN V2-5v9KqIo6CFE.en.vtt
01. MLND SL EM 01 Intro V1 MAIN V2-5v9KqIo6CFE.pt-BR.vtt
06. MLND SL EM 06 Weighting The Models MAIN V2-unCJ_ifVquU.pt-BR.vtt
index.html
11. Outro.html
01. Intro.html
02. Bagging.html
03. AdaBoost.html
04. Weighting the Data.html
08. Combining the Models.html
07. Weighting the Models 3.html
10. Resources.html
09. AdaBoost in sklearn.html
05. Weighting the Models 1.html
06. Weighting the Models 2.html
img
screen-shot-2018-01-03-at-2.23.38-pm.png
screen-shot-2018-01-03-at-2.20.30-pm.png
05. MLND SL EM 05 Weighting The Models MAIN V1-wn6K536dPLc.mp4
03. MLND SL EM 03 AdaBoost V1 MAIN V1-HD6SRBWKGUE.mp4
11. Supervised Learning Outro V2-7X2SDqzGrdU.mp4
02. MLND SL EM 02 Bagging V1 MAIN V1-9L_B0Jcio3c.mp4
04. MLND SL EM 04 Weighting The Data MAIN V1 V2-O-hh_x0iYW8.mp4
08. MLND SL EM 08 Combining The Models V1 MAIN V1-1GxscvKU2Ic.mp4
07. MLND SL EM 07 Weighting The Models 3 V1 MAIN V1-fecp5nmetws.mp4
01. MLND SL EM 01 Intro V1 MAIN V2-5v9KqIo6CFE.mp4
06. MLND SL EM 06 Weighting The Models MAIN V2-unCJ_ifVquU.mp4
Part 01-Module 01-Lesson 02_What is Machine Learning
13. SVM Question-Fwnjx0s_AIw.pt-BR.vtt
13. SVM Question-Fwnjx0s_AIw.zh-CN.vtt
13. SVM Question-Fwnjx0s_AIw.en.vtt
02. Decision Trees Question-1RonLycEJ34.zh-CN.vtt
02. Decision Trees Question-1RonLycEJ34.pt-BR.vtt
02. Decision Trees Question-1RonLycEJ34.en.vtt
20. Recap and Challenge-ecREasTrKu4.zh-CN.vtt
08. Gradient Descent-BEC0uH1fuGU.zh-CN.vtt
20. Recap and Challenge-ecREasTrKu4.pt-BR.vtt
09. Linear Regression Question-sf51L0RN6zc.zh-CN.vtt
20. Recap and Challenge-ecREasTrKu4.en.vtt
09. Linear Regression Question-sf51L0RN6zc.pt-BR.vtt
09. Linear Regression Question-sf51L0RN6zc.en.vtt
08. Gradient Descent-BEC0uH1fuGU.pt-BR.vtt
07. Naive Bayes Answer-YKN-fjuZ1VU.zh-CN.vtt
08. Gradient Descent-BEC0uH1fuGU.en.vtt
05. Naive Bayes Quiz-jsLkVYXmr3E.zh-CN.vtt
07. Naive Bayes Answer-YKN-fjuZ1VU.pt-BR.vtt
07. Naive Bayes Answer-YKN-fjuZ1VU.en.vtt
23. Conclusion-hJEuaOUu2yA.zh-CN.vtt
05. Naive Bayes Quiz-jsLkVYXmr3E.pt-BR.vtt
05. Naive Bayes Quiz-jsLkVYXmr3E.en.vtt
23. Conclusion-hJEuaOUu2yA.en.vtt
11. Logistic Regression Question-wQXKdeVHTmc.zh-CN.vtt
23. Conclusion-hJEuaOUu2yA.pt-BR.vtt
01. Introduction-bYeteZQrUcE.zh-CN.vtt
11. Logistic Regression Question-wQXKdeVHTmc.pt-BR.vtt
22. Hierarchical Clustering-1PldDT8AwMA.pt-BR.vtt
22. Hierarchical Clustering-1PldDT8AwMA.zh-CN.vtt
22. Hierarchical Clustering-1PldDT8AwMA.en.vtt
11. Logistic Regression Question-wQXKdeVHTmc.en.vtt
01. Introduction-bYeteZQrUcE.en.vtt
01. Introduction-bYeteZQrUcE.pt-BR.vtt
04. Decision Trees Answer-h8zH47iFhCo.zh-CN.vtt
04. Decision Trees Answer-h8zH47iFhCo.en.vtt
04. Decision Trees Answer-h8zH47iFhCo.pt-BR.vtt
17. Kernel Method Quiz-x0JqH6-Dhvw.pt-BR.vtt
17. Kernel Method Quiz-x0JqH6-Dhvw.zh-CN.vtt
15. SVM Answer-JrUtTwfnsfM.pt-BR.vtt
15. SVM Answer-JrUtTwfnsfM.zh-CN.vtt
17. Kernel Method Quiz-x0JqH6-Dhvw.en.vtt
15. SVM Answer-JrUtTwfnsfM.en.vtt
21. K-means Clustering-pv_i08zjpQw.pt-BR.vtt
21. K-means Clustering-pv_i08zjpQw.zh-CN.vtt
21. K-means Clustering-pv_i08zjpQw.en.vtt
19. Kernel Method Answer-dRFd6HaAXys.zh-CN.vtt
10. Linear Regression Answer-L5QBqYDNJn0.zh-CN.vtt
19. Kernel Method Answer-dRFd6HaAXys.en.vtt
10. Linear Regression Answer-L5QBqYDNJn0.pt-BR.vtt
10. Linear Regression Answer-L5QBqYDNJn0.en.vtt
index.html
19. Kernel Method Answer-dRFd6HaAXys.pt-BR.vtt
12. Logistic Regression Answer-JuAJd9Qvs6U.zh-CN.vtt
12. Logistic Regression Answer-JuAJd9Qvs6U.en.vtt
12. Logistic Regression Answer-JuAJd9Qvs6U.pt-BR.vtt
23. Summary.html
05. Naive Bayes.html
16. Neural Networks.html
13. Support Vector Machines.html
01. What Is Machine Learning.html
15. Support Vector Machines Answer.html
08. Gradient Descent.html
17. Kernel Method.html
21. K-means Clustering.html
07. Naive Bayes Answer.html
20. Recap and Challenge.html
19. Kernel Method Answer.html
02. Decision Trees.html
04. Decision Trees Answer.html
22. Hierarchical Clustering.html
10. Linear Regression Answer.html
12. Logistic Regression Answer.html
14. Support Vector Machines Quiz.html
03. Decision Trees Quiz.html
18. Kernel Method Quiz.html
06. Naive Bayes Quiz.html
11. Logistic Regression Quiz.html
09. Linear Regression Quiz.html
16. Neural Networks-xFu1_2K2D2U.zh-CN.vtt
16. Neural Networks-xFu1_2K2D2U.pt-BR.vtt
16. Neural Networks-xFu1_2K2D2U.en.vtt
img
svm-image.png
kernel-trick.png
decision-trees.png
naive-bayes-quiz.png
13. SVM Question-Fwnjx0s_AIw.mp4
02. Decision Trees Question-1RonLycEJ34.mp4
09. Linear Regression Question-sf51L0RN6zc.mp4
07. Naive Bayes Answer-YKN-fjuZ1VU.mp4
20. Recap and Challenge-ecREasTrKu4.mp4
08. Gradient Descent-BEC0uH1fuGU.mp4
05. Naive Bayes Quiz-jsLkVYXmr3E.mp4
11. Logistic Regression Question-wQXKdeVHTmc.mp4
17. Kernel Method Quiz-x0JqH6-Dhvw.mp4
15. SVM Answer-JrUtTwfnsfM.mp4
04. Decision Trees Answer-h8zH47iFhCo.mp4
10. Linear Regression Answer-L5QBqYDNJn0.mp4
12. Logistic Regression Answer-JuAJd9Qvs6U.mp4
19. Kernel Method Answer-dRFd6HaAXys.mp4
01. Introduction-bYeteZQrUcE.mp4
22. Hierarchical Clustering-1PldDT8AwMA.mp4
23. Conclusion-hJEuaOUu2yA.mp4
16. Neural Networks-xFu1_2K2D2U.mp4
21. K-means Clustering-pv_i08zjpQw.mp4
Part 02-Module 02-Lesson 01_Evaluation Metrics
04. Accuracy 2-ueYCLfd_aNQ.zh-CN.vtt
04. Accuracy 2-ueYCLfd_aNQ.pt-BR.vtt
04. Accuracy 2-ueYCLfd_aNQ.pt.vtt
04. Accuracy 2-ueYCLfd_aNQ.en.vtt
04. Accuracy 2-ueYCLfd_aNQ.en-US.vtt
02. Confusion-Matrix-Solution-ywwSzyU9rYs.pt-BR.vtt
02. Confusion-Matrix-Solution-ywwSzyU9rYs.zh-CN.vtt
02. Confusion-Matrix-Solution-ywwSzyU9rYs.en.vtt
02. Confusion-Matrix-Solution-ywwSzyU9rYs.en-US.vtt
03. Accuracy-s6SfhPTNOHA.zh-CN.vtt
03. Accuracy-s6SfhPTNOHA.en.vtt
03. Accuracy-s6SfhPTNOHA.pt-BR.vtt
03. Accuracy-s6SfhPTNOHA.en-US.vtt
08. 06 Precision SC V1-q2wVorBfefU.pt-BR.vtt
06. 04 Quiz False Negatives And Positives SC V1-_ytP9zIkziw.pt-BR.vtt
08. 06 Precision SC V1-q2wVorBfefU.en.vtt
09. 07 Recall SC V1-0n5wUZiefkQ.pt-BR.vtt
05. When Accuracy Wont Work-r0-O-gIDXZ0.pt-BR.vtt
05. When Accuracy Wont Work-r0-O-gIDXZ0.en.vtt
07. Answer False Negatives And Positives-KOytJL1lvgg.en.vtt
07. Answer False Negatives And Positives-KOytJL1lvgg.pt-BR.vtt
06. 04 Quiz False Negatives And Positives SC V1-_ytP9zIkziw.en.vtt
09. 07 Recall SC V1-0n5wUZiefkQ.en.vtt
13. Regression-Metrics-906P4BPnl9A.zh-CN.vtt
11. 09 Quiz Fbeta Score SC V1-KSswld4_9bY.pt-BR.vtt
13. Regression-Metrics-906P4BPnl9A.pt-BR.vtt
11. 09 Quiz Fbeta Score SC V1-KSswld4_9bY.en.vtt
13. Regression-Metrics-906P4BPnl9A.en-US.vtt
index.html
01. Confusion Matrix-Question 1-9GLNjmMUB_4.pt-BR.vtt
01. Confusion Matrix-Question 1-9GLNjmMUB_4.zh-CN.vtt
01. Confusion Matrix-Question 1-9GLNjmMUB_4.en-US.vtt
05. When accuracy won't work.html
01. Confusion Matrix-Question 1-9GLNjmMUB_4.en.vtt
12. ROC Curve.html
13. Regression Metrics.html
02. Confusion Matrix 2.html
04. Accuracy 2.html
07. Precision and Recall.html
10. F1 Score.html
03. Accuracy.html
12. ROC Curve-2Iw5TiGzJI4.zh-CN.vtt
08. Precision.html
10. 08 F1 Score SC V1-TRzBeL07fSg.pt-BR.vtt
09. Recall.html
10. 08 F1 Score SC V1-TRzBeL07fSg.en.vtt
06. False Negatives and Positives.html
12. ROC Curve-2Iw5TiGzJI4.pt-BR.vtt
01. Confusion Matrix.html
12. ROC Curve-2Iw5TiGzJI4.en.vtt
11. F-beta Score.html
img
accuracy-quiz.png
email.png
medical.png
confusion.png
recall-quiz.png
precision-quiz.png
fbeta.png
04. Accuracy 2-ueYCLfd_aNQ.mp4
02. Confusion-Matrix-Solution-ywwSzyU9rYs.mp4
09. 07 Recall SC V1-0n5wUZiefkQ.mp4
05. When Accuracy Wont Work-r0-O-gIDXZ0.mp4
06. 04 Quiz False Negatives And Positives SC V1-_ytP9zIkziw.mp4
07. Answer False Negatives And Positives-KOytJL1lvgg.mp4
08. 06 Precision SC V1-q2wVorBfefU.mp4
03. Accuracy-s6SfhPTNOHA.mp4
11. 09 Quiz Fbeta Score SC V1-KSswld4_9bY.mp4
13. Regression-Metrics-906P4BPnl9A.mp4
01. Confusion Matrix-Question 1-9GLNjmMUB_4.mp4
10. 08 F1 Score SC V1-TRzBeL07fSg.mp4
12. ROC Curve-2Iw5TiGzJI4.mp4
Part 11-Module 03-Lesson 01_Intro to Neural Networks
01. Introducing Luis-nto-stLuN6M.zh-CN.vtt
01. Introducing Luis-nto-stLuN6M.pt-BR.vtt
01. Introducing Luis-nto-stLuN6M.en-US.vtt
14. Multilayer perceptrons-Rs9petvTBLk.zh-CN.vtt
10. Gradient Descent-29PmNG7fuuM.zh-CN.vtt
10. Gradient Descent-29PmNG7fuuM.pt-BR.vtt
10. Gradient Descent-29PmNG7fuuM.en.vtt
14. Multilayer perceptrons-Rs9petvTBLk.en-US.vtt
img
backprop-weight-update.gif
hidden-layer-weights.gif
backprop-general.gif
codecogseqn-2.png
hidden-errors.gif
weight-label-reference.gif
backprop-error.gif
mse.png
heaviside-step-function-2.gif
inputs-matrix.png
perceptron-formula.gif
perceptron-equation-2.gif
backprop-network.png
heaviside-step-graph-2.png
sigmoid.png
example-before-bias.png
local-minima.png
hq-new-xor-table.png
multilayer-diagram-weights.png
simple-neuron.png
input-times-weights.png
network-with-labeled-nodes.png
derivative-example.png
network-with-labeled-weights.png
example-after-bias.png
and-table.png
gradient-descent.png
matrix-mult-3.png
example-data.png
hq-new-and-or-percep-fixed.png
legend.png
hq-perceptron.png
admissions-data.png
mat-headshot.png
hq-new-plot-perceptron-combine-v2.png
hq-new-plot-perceptron-combine.png
perceptron-graphics.001.jpeg
a-b-c-fill-nn.png
logistic-regression-quiz.png
14. Multilayer perceptrons-Rs9petvTBLk.pt-BR.vtt
02. Logistic Regression - Question-kSs6O3R7JUI.zh-CN.vtt
02. Logistic Regression - Question-kSs6O3R7JUI.pt-BR.vtt
15. Backpropagation-MZL97-2joxQ.zh-CN.vtt
02. Logistic Regression - Question-kSs6O3R7JUI.en-US.vtt
15. Backpropagation-MZL97-2joxQ.pt-BR.vtt
15. Backpropagation-MZL97-2joxQ.en-US.vtt
index.html
03. Logistic Regression - Solution-1iNylA3fJDs.zh-CN.vtt
03. Logistic Regression - Solution-1iNylA3fJDs.en-US.vtt
03. Logistic Regression - Solution-1iNylA3fJDs.pt-BR.vtt
04. Neural Networks.html
01. Introducing Luis.html
03. Logistic Regression Answer.html
17. Further Reading.html
11. Gradient Descent The Math.html
02. Logistic Regression Quiz.html
04. Neural Networks-Mqogpnp1lrU.zh-CN.vtt
11. Gradient Descent-Math-7sxA5Ap8AWM.zh-CN.vtt
04. Neural Networks-Mqogpnp1lrU.pt-BR.vtt
04. Neural Networks-Mqogpnp1lrU.en.vtt
06. AND Perceptron Quiz.html
08. XOR Perceptron Quiz.html
11. Gradient Descent-Math-7sxA5Ap8AWM.en.vtt
07. OR & NOT Perceptron Quiz.html
11. Gradient Descent-Math-7sxA5Ap8AWM.pt-BR.vtt
12. Gradient Descent The Code.html
09. The Simplest Neural Network.html
10. Gradient Descent.html
15. Backpropagation.html
05. Perceptron.html
14. Multilayer Perceptrons.html
16. Implementing Backpropagation.html
13. Implementing Gradient Descent.html
10. Gradient Descent-29PmNG7fuuM.mp4
14. Multilayer perceptrons-Rs9petvTBLk.mp4
15. Backpropagation-MZL97-2joxQ.mp4
02. Logistic Regression - Question-kSs6O3R7JUI.mp4
01. Introducing Luis-nto-stLuN6M.mp4
03. Logistic Regression - Solution-1iNylA3fJDs.mp4
11. Gradient Descent-Math-7sxA5Ap8AWM.mp4
04. Neural Networks-Mqogpnp1lrU.mp4
Part 11-Module 04-Lesson 01_Deep Neural Networks
10. Regularization-Quiz-E0eEW6V0_sA.zh-CN.vtt
10. Regularization-Quiz-E0eEW6V0_sA.en-US.vtt
10. Regularization-Quiz-E0eEW6V0_sA.pt-BR.vtt
12. Dropout Pt. 2-8nG8zzJMbZw. 2 RENDER-8nG8zzJMbZw.pt-BR.vtt
12. Dropout Pt. 2-8nG8zzJMbZw. 2 RENDER-8nG8zzJMbZw.zh-CN.vtt
12. Dropout Pt. 2-8nG8zzJMbZw. 2 RENDER-8nG8zzJMbZw.en-US.vtt
09. Regularization-QcJBhbuCl5g.zh-CN.vtt
08. Regularization Intro-pECnr-5F3_Q.pt-BR.vtt
08. Regularization Intro-pECnr-5F3_Q.zh-CN.vtt
01. Mat HS-9P7UPWFu8w8.zh-CN.vtt
08. Regularization Intro-pECnr-5F3_Q.en.vtt
08. Regularization Intro-pECnr-5F3_Q.en-US.vtt
09. Regularization-QcJBhbuCl5g.en.vtt
09. Regularization-QcJBhbuCl5g.pt-BR.vtt
01. Mat HS-9P7UPWFu8w8.en-US.vtt
08. Regularization Intro-pECnr-5F3_Q.ja-JP.vtt
05. Training a Deep Learning Network-CsB7yUtMJyk.zh-CN.vtt
05. Training a Deep Learning Network-CsB7yUtMJyk.pt-BR.vtt
05. Training a Deep Learning Network-CsB7yUtMJyk.en.vtt
11. Dropout RENDER-6DcImJS8uV8.zh-CN.vtt
11. Dropout RENDER-6DcImJS8uV8.en-US.vtt
11. Dropout RENDER-6DcImJS8uV8.pt-BR.vtt
index.html
01. Intro to Deep Neural Networks.html
11. Dropout.html
09. Regularization.html
12. Dropout Pt. 2.html
05. Training a Deep Learning Network.html
08. Regularization Intro.html
02. Two-Layer Neural Network.html
10. Regularization Quiz.html
03. Quiz TensorFlow ReLUs.html
07. Finetuning.html
04. Deep Neural Network in TensorFlow.html
13. Quiz TensorFlow Dropout.html
06. Save and Restore TensorFlow Models.html
img
two-layer-network.png
relu-network.png
dropout-node.jpeg
multi-layer.png
layers.png
regularization-quiz.png
10. Regularization-Quiz-E0eEW6V0_sA.mp4
12. Dropout Pt. 2-8nG8zzJMbZw.mp4
09. Regularization-QcJBhbuCl5g.mp4
05. Training a Deep Learning Network-CsB7yUtMJyk.mp4
11. Dropout RENDER-6DcImJS8uV8.mp4
08. Regularization Intro-pECnr-5F3_Q.mp4
01. Mat HS-9P7UPWFu8w8.mp4
Part 03-Module 01-Lesson 01_Linear Regression
23. Conclusion-pyeojf0NniQ.en.vtt
23. Conclusion-pyeojf0NniQ.pt-BR.vtt
14. Absolute Vs Squared Error-csvdjaqt1GM.pt-BR.vtt
14. Absolute Vs Squared Error-csvdjaqt1GM.en.vtt
03. Solution Housing Prices-uhdTulw9-Nc.en.vtt
14. DLND REG 12 Absolute Vs Squared Error 2 V1 (1)-7El1OH17Oi4.pt-BR.vtt
14. DLND REG 13 Absolute Vs Squared Error 3 V1 (1)-bIVGf_dDkrY.pt-BR.vtt
14. DLND REG 12 Absolute Vs Squared Error 2 V1 (1)-7El1OH17Oi4.en.vtt
03. Solution Housing Prices-uhdTulw9-Nc.pt-BR.vtt
14. DLND REG 13 Absolute Vs Squared Error 3 V1 (1)-bIVGf_dDkrY.en.vtt
img
gif-1.gif
f4.gif
e.gif
codecogseqn-62.gif
y.gif
f6.gif
f2.gif
f1.gif
codecogseqn-61.gif
m.gif
quadraticlinearregression.png
just-a-simple-lin-reg.png
lin-reg-w-outliers.png
lin-reg-no-outliers.png
just-a-2d-reg.png
minibatch.png
quiz.jpg
batch-stochastic.png
house.png
05. Moving A Line-8EIHFyL2Log.pt-BR.vtt
01. Welcome To Linear Regression-zxZkTkM34BY.en.vtt
05. Moving A Line-8EIHFyL2Log.en.vtt
21. Polynomial Regression-DBhWG-PagEQ.pt-BR.vtt
01. Welcome To Linear Regression-zxZkTkM34BY.pt-BR.vtt
02. DLND REG 01 Quiz Housing Prices V2-8CSBiVKu35Q.zh-CN.vtt
21. Polynomial Regression-DBhWG-PagEQ.en.vtt
02. DLND REG 01 Quiz Housing Prices V2-8CSBiVKu35Q.en.vtt
04. Fitting A Line-gkdoknEEcaI.en.vtt
04. Fitting A Line-gkdoknEEcaI.pt-BR.vtt
02. DLND REG 01 Quiz Housing Prices V2-8CSBiVKu35Q.pt-BR.vtt
10. Mean Squared Error-MRyxmZDngI4.pt-BR.vtt
10. Mean Squared Error-MRyxmZDngI4.en.vtt
16. Higher Dimensions--UvpQV1qmiE.pt-BR.vtt
16. Higher Dimensions--UvpQV1qmiE.en.vtt
09. Mean Absolute Error-vLKiY0Ehors.pt-BR.vtt
18. Closed Form Solution-G3fRVgLa5gI.pt-BR.vtt
09. Mean Absolute Error-vLKiY0Ehors.en.vtt
18. Closed Form Solution-G3fRVgLa5gI.en.vtt
07. Square Trick-AGZEq-yQgRM.pt-BR.vtt
07. Square Trick-AGZEq-yQgRM.en.vtt
11. Minimizing Error Functions-RbT2TXN_6tY.en.vtt
11. Minimizing Error Functions-RbT2TXN_6tY.pt-BR.vtt
08. Gradient Descent-4s4x9h6AN5Y.pt-BR.vtt
index.html
08. Gradient Descent-4s4x9h6AN5Y.en.vtt
06. Absolute Trick-DJWjBAqSkZw.pt-BR.vtt
06. Absolute Trick-DJWjBAqSkZw.en.vtt
23. Outro.html
07. Square Trick.html
05. Moving a Line.html
06. Absolute Trick.html
22. Regularization.html
08. Gradient Descent.html
16. Higher Dimensions.html
10. Mean Squared Error.html
04. Fitting a Line Through Data.html
09. Mean Absolute Error.html
01. Intro.html
21. Polynomial Regression.html
03. Solution Housing Prices.html
18. Closed Form Solution.html
02. Quiz Housing Prices.html
12. Mean vs Total Error.html
13. Mini-batch Gradient Descent.html
20. Linear Regression Warnings.html
11. Minimizing Error Functions.html
22. Regularization-PyFNIcsNma0.pt-BR.vtt
14. Absolute Error vs Squared Error.html
22. Regularization-PyFNIcsNma0.en.vtt
17. Multiple Linear Regression.html
19. (Optional) Closed form Solution Math.html
15. Linear Regression in scikit-learn.html
14. Absolute Vs Squared Error-csvdjaqt1GM.mp4
14. DLND REG 12 Absolute Vs Squared Error 2 V1 (1)-7El1OH17Oi4.mp4
14. DLND REG 13 Absolute Vs Squared Error 3 V1 (1)-bIVGf_dDkrY.mp4
05. Moving A Line-8EIHFyL2Log.mp4
21. Polynomial Regression-DBhWG-PagEQ.mp4
03. Solution Housing Prices-uhdTulw9-Nc.mp4
04. Fitting A Line-gkdoknEEcaI.mp4
02. DLND REG 01 Quiz Housing Prices V2-8CSBiVKu35Q.mp4
23. Conclusion-pyeojf0NniQ.mp4
10. Mean Squared Error-MRyxmZDngI4.mp4
09. Mean Absolute Error-vLKiY0Ehors.mp4
16. Higher Dimensions--UvpQV1qmiE.mp4
18. Closed Form Solution-G3fRVgLa5gI.mp4
07. Square Trick-AGZEq-yQgRM.mp4
11. Minimizing Error Functions-RbT2TXN_6tY.mp4
01. Welcome To Linear Regression-zxZkTkM34BY.mp4
08. Gradient Descent-4s4x9h6AN5Y.mp4
06. Absolute Trick-DJWjBAqSkZw.mp4
22. Regularization-PyFNIcsNma0.mp4
Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation
13. MLND - Unsupervised Learning - L3 13 GMM Implementation MAIN V1 V2-zWrC_2Npy9E.zh-CN.vtt
13. MLND - Unsupervised Learning - L3 13 GMM Implementation MAIN V1 V2-zWrC_2Npy9E.en.vtt
13. MLND - Unsupervised Learning - L3 13 GMM Implementation MAIN V1 V2-zWrC_2Npy9E.pt-BR.vtt
02. MLND - Unsupervised Learning - L3 2 Gaussian Mixture Model Clustering MAIN V1 V2-Y_methsXoFA.zh-CN.vtt
02. MLND - Unsupervised Learning - L3 2 Gaussian Mixture Model Clustering MAIN V1 V2-Y_methsXoFA.en.vtt
06. MLND - Unsupervised Learning - L3 06 GMM In 2D MAIN Sfx V1 V1-GsNWVHmRRG4.zh-CN.vtt
02. MLND - Unsupervised Learning - L3 2 Gaussian Mixture Model Clustering MAIN V1 V2-Y_methsXoFA.pt-BR.vtt
06. MLND - Unsupervised Learning - L3 06 GMM In 2D MAIN Sfx V1 V1-GsNWVHmRRG4.en.vtt
08. MLND - Unsupervised Learning - L3 08 Overview Of The Expectation Maximization Algorithm MAIN V1 V1-XdQfFnnj5Xo.zh-CN.vtt
06. MLND - Unsupervised Learning - L3 06 GMM In 2D MAIN Sfx V1 V1-GsNWVHmRRG4.pt-BR.vtt
05. MLND - Unsupervised Learning - L3 05 Gaussian Distribution In 2D MAIN V1 V2-Ne-qRjO38qQ.zh-CN.vtt
08. MLND - Unsupervised Learning - L3 08 Overview Of The Expectation Maximization Algorithm MAIN V1 V1-XdQfFnnj5Xo.en.vtt
16. MLND - Unsupervised Learning - L3 17 Cluster Validation MAINv1 V1-N13ML_GUuZQ.zh-CN.vtt
05. MLND - Unsupervised Learning - L3 05 Gaussian Distribution In 2D MAIN V1 V2-Ne-qRjO38qQ.en.vtt
08. MLND - Unsupervised Learning - L3 08 Overview Of The Expectation Maximization Algorithm MAIN V1 V1-XdQfFnnj5Xo.pt-BR.vtt
01. MLND - Unsupervised Learning - L3 01 Gaussian Mixture Model MAINv1 V3-SLdZrt0CvOk.zh-CN.vtt
05. MLND - Unsupervised Learning - L3 05 Gaussian Distribution In 2D MAIN V1 V2-Ne-qRjO38qQ.pt-BR.vtt
03. MLND - Unsupervised Learning - L3 3 Gaussian Distribution In 1D MAINv1 V1-uDPFrZwsKKQ.zh-CN.vtt
16. MLND - Unsupervised Learning - L3 17 Cluster Validation MAINv1 V1-N13ML_GUuZQ.en.vtt
16. MLND - Unsupervised Learning - L3 17 Cluster Validation MAINv1 V1-N13ML_GUuZQ.pt-BR.vtt
01. MLND - Unsupervised Learning - L3 01 Gaussian Mixture Model MAINv1 V3-SLdZrt0CvOk.en.vtt
03. MLND - Unsupervised Learning - L3 3 Gaussian Distribution In 1D MAINv1 V1-uDPFrZwsKKQ.pt-BR.vtt
01. MLND - Unsupervised Learning - L3 01 Gaussian Mixture Model MAINv1 V3-SLdZrt0CvOk.pt-BR.vtt
03. MLND - Unsupervised Learning - L3 3 Gaussian Distribution In 1D MAINv1 V1-uDPFrZwsKKQ.en.vtt
04. MLND - Unsupervised Learning - L3 04 GMM Clustering In 1D MAIN V1 V1-JkRQIGqkqA4.zh-CN.vtt
11. MLND - Unsupervised Learning - L3 11 Visual Example Of EM Progress MAIN V1 V1-9x3d_eVJrJE.zh-CN.vtt
11. MLND - Unsupervised Learning - L3 11 Visual Example Of EM Progress MAIN V1 V1-9x3d_eVJrJE.pt-BR.vtt
04. MLND - Unsupervised Learning - L3 04 GMM Clustering In 1D MAIN V1 V1-JkRQIGqkqA4.en.vtt
11. MLND - Unsupervised Learning - L3 11 Visual Example Of EM Progress MAIN V1 V1-9x3d_eVJrJE.en.vtt
04. MLND - Unsupervised Learning - L3 04 GMM Clustering In 1D MAIN V1 V1-JkRQIGqkqA4.pt-BR.vtt
15. MLND - Unsupervised Learning - L3 16 Cluster Analysis Process MAIN V1 V1-aI2wW4fcU1I.zh-CN.vtt
15. MLND - Unsupervised Learning - L3 16 Cluster Analysis Process MAIN V1 V1-aI2wW4fcU1I.en.vtt
15. MLND - Unsupervised Learning - L3 16 Cluster Analysis Process MAIN V1 V1-aI2wW4fcU1I.pt-BR.vtt
17. MLND - Unsupervised Learning - L3 18 External Validation Indices MAIN V1 V2-rXZM5X2-5D0.zh-CN.vtt
index.html
14. MLND - Unsupervised Learning - L3 15 GMM Examples And Applications MAIN V2 V1-FRoxeLp81Bg.zh-CN.vtt
17. MLND - Unsupervised Learning - L3 18 External Validation Indices MAIN V1 V2-rXZM5X2-5D0.pt-BR.vtt
10. MLND - Unsupervised Learning - L3 10 Expectation Maximization Pt 2 MAIN V1 V2-B_xXd0mFUm4.zh-CN.vtt
17. MLND - Unsupervised Learning - L3 18 External Validation Indices MAIN V1 V2-rXZM5X2-5D0.en.vtt
14. MLND - Unsupervised Learning - L3 15 GMM Examples And Applications MAIN V2 V1-FRoxeLp81Bg.pt-BR.vtt
14. MLND - Unsupervised Learning - L3 15 GMM Examples And Applications MAIN V2 V1-FRoxeLp81Bg.en.vtt
09. MLND - Unsupervised Learning - L3 09 Expectation Maximization Pt 1 V1 MAIN 1 V2-cf-RLKn5ubA.zh-CN.vtt
10. MLND - Unsupervised Learning - L3 10 Expectation Maximization Pt 2 MAIN V1 V2-B_xXd0mFUm4.pt-BR.vtt
10. MLND - Unsupervised Learning - L3 10 Expectation Maximization Pt 2 MAIN V1 V2-B_xXd0mFUm4.en.vtt
06. GMM in 2D.html
20. Quiz Silhouette Coefficient .html
01. Intro.html
16. Cluster Validation.html
13. GMM Implementation.html
12. Quiz Expectation Maximization.html
04. GMM Clustering in One Dimension.html
15. Cluster Analysis Process.html
05. Gaussian Distribution in 2D.html
03. Gaussian Distribution in One Dimension.html
11. Visual Example of EM Progress.html
10. Expectation Maximization Part 2.html
09. Expectation Maximization Part 1.html
02. Gaussian Mixture Model (GMM) Clustering.html
21. GMM & Cluster Validation Lab.html
22. GMM & Cluster Validation Lab Solution.html
08. Overview of The Expectation Maximization (EM) Algorithm.html
17. External Validation Indices.html
09. MLND - Unsupervised Learning - L3 09 Expectation Maximization Pt 1 V1 MAIN 1 V2-cf-RLKn5ubA.en.vtt
19. Internal Validation Indices.html
09. MLND - Unsupervised Learning - L3 09 Expectation Maximization Pt 1 V1 MAIN 1 V2-cf-RLKn5ubA.pt-BR.vtt
18. Quiz Adjusted Rand Index.html
14. GMM Examples & Applications.html
19. MLND - Unsupervised Learning - L3 20 Internal Validation Indices MAIN V1 V2-39JruOTptKI.zh-CN.vtt
19. MLND - Unsupervised Learning - L3 20 Internal Validation Indices MAIN V1 V2-39JruOTptKI.pt-BR.vtt
19. MLND - Unsupervised Learning - L3 20 Internal Validation Indices MAIN V1 V2-39JruOTptKI.en.vtt
07. Quiz Gaussian Mixtures.html
img
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13. MLND - Unsupervised Learning - L3 13 GMM Implementation MAIN V1 V2-zWrC_2Npy9E.mp4
02. MLND - Unsupervised Learning - L3 2 Gaussian Mixture Model Clustering MAIN V1 V2-Y_methsXoFA.mp4
16. MLND - Unsupervised Learning - L3 17 Cluster Validation MAINv1 V1-N13ML_GUuZQ.mp4
06. MLND - Unsupervised Learning - L3 06 GMM In 2D MAIN Sfx V1 V1-GsNWVHmRRG4.mp4
08. MLND - Unsupervised Learning - L3 08 Overview Of The Expectation Maximization Algorithm MAIN V1 V1-XdQfFnnj5Xo.mp4
05. MLND - Unsupervised Learning - L3 05 Gaussian Distribution In 2D MAIN V1 V2-Ne-qRjO38qQ.mp4
01. MLND - Unsupervised Learning - L3 01 Gaussian Mixture Model MAINv1 V3-SLdZrt0CvOk.mp4
03. MLND - Unsupervised Learning - L3 3 Gaussian Distribution In 1D MAINv1 V1-uDPFrZwsKKQ.mp4
04. MLND - Unsupervised Learning - L3 04 GMM Clustering In 1D MAIN V1 V1-JkRQIGqkqA4.mp4
15. MLND - Unsupervised Learning - L3 16 Cluster Analysis Process MAIN V1 V1-aI2wW4fcU1I.mp4
11. MLND - Unsupervised Learning - L3 11 Visual Example Of EM Progress MAIN V1 V1-9x3d_eVJrJE.mp4
17. MLND - Unsupervised Learning - L3 18 External Validation Indices MAIN V1 V2-rXZM5X2-5D0.mp4
10. MLND - Unsupervised Learning - L3 10 Expectation Maximization Pt 2 MAIN V1 V2-B_xXd0mFUm4.mp4
14. MLND - Unsupervised Learning - L3 15 GMM Examples And Applications MAIN V2 V1-FRoxeLp81Bg.mp4
09. MLND - Unsupervised Learning - L3 09 Expectation Maximization Pt 1 V1 MAIN 1 V2-cf-RLKn5ubA.mp4
19. MLND - Unsupervised Learning - L3 20 Internal Validation Indices MAIN V1 V2-39JruOTptKI.mp4
Part 10-Module 01-Lesson 05_Interview Practice
01. Machine Learning Interview-y0yKRmgDKY4.zh-CN.vtt
01. Machine Learning Interview-y0yKRmgDKY4.en.vtt
04. Q1 - Predict Rain-2HY0Yr5FRn0.zh-CN.vtt
04. Q1 - Predict Rain-2HY0Yr5FRn0.en.vtt
09. Q6 - Explain How SVMs Work-pMjG1IJRSb8.zh-CN.vtt
02. Mindset and Skills-OvjI0rveWnM.zh-CN.vtt
09. Q6 - Explain How SVMs Work-pMjG1IJRSb8.en.vtt
02. Mindset and Skills-OvjI0rveWnM.en.vtt
07. Q4 - Reduce Data Dimensionality-sbB-0qV33uM.zh-CN.vtt
05. Q2 - Identify Fish-lKAZqlhLBxc.zh-CN.vtt
07. Q4 - Reduce Data Dimensionality-sbB-0qV33uM.en.vtt
05. Q2 - Identify Fish-lKAZqlhLBxc.en.vtt
06. Q3 - Detect Plagiarism-sunl9foctXg.zh-CN.vtt
06. Q3 - Detect Plagiarism-sunl9foctXg.en.vtt
10. Conclusion-mnQ2n026Y2o.zh-CN.vtt
10. Conclusion-mnQ2n026Y2o.en.vtt
08. Q5 - Describe Your ML Project-r7g0Z-54vg0.en.vtt
08. Q5 - Describe Your ML Project-jjdbGD4CBGk.en.vtt
index.html
09. Q6 - Explain How SVMs Work-RyThtU8GcT0.zh-CN.vtt
06. Q3 - Detect Plagiarism-B3w_msqHP68.zh-CN.vtt
06. Q3 - Detect Plagiarism-B3w_msqHP68.en.vtt
09. Q6 - Explain How SVMs Work-RyThtU8GcT0.en.vtt
07. Q4 - Reduce Data Dimensionality-NzzpasA9GsM.zh-CN.vtt
10. Arpan's Analysis of the Interview.html
01. Introduction.html
03. Analyzing an Interview.html
02. Mindset and Skills.html
04. Q1 - Predict Rain-ooqFCXMdxys.zh-CN.vtt
07. Q4 - Reduce Data Dimensionality-NzzpasA9GsM.en.vtt
12. Resources in Your Career Portal.html
05. Q2 - Identify Fish-bXpONCq5ePE.zh-CN.vtt
04. Q1 - Predict Rain-ooqFCXMdxys.en.vtt
11. Keep Practicing!.html
08. Q5 - Describe Your ML Project.html
04. Q1 - Predict Rain.html
05. Q2 - Identify Fish.html
06. Q3 - Detect Plagiarism.html
09. Q6 - Explain How SVMs Work.html
07. Q4 - Reduce Data Dimensionality.html
05. Q2 - Identify Fish-bXpONCq5ePE.en.vtt
Project Rubric - ML Interview Practice.html
Project Description - ML Interview Practice.html
img
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01. Machine Learning Interview-y0yKRmgDKY4.mp4
04. Q1 - Predict Rain-2HY0Yr5FRn0.mp4
09. Q6 - Explain How SVMs Work-pMjG1IJRSb8.mp4
02. Mindset and Skills-OvjI0rveWnM.mp4
05. Q2 - Identify Fish-lKAZqlhLBxc.mp4
07. Q4 - Reduce Data Dimensionality-sbB-0qV33uM.mp4
10. Conclusion-mnQ2n026Y2o.mp4
08. Q5 - Describe Your ML Project-r7g0Z-54vg0.mp4
06. Q3 - Detect Plagiarism-sunl9foctXg.mp4
08. Q5 - Describe Your ML Project-jjdbGD4CBGk.mp4
06. Q3 - Detect Plagiarism-B3w_msqHP68.mp4
09. Q6 - Explain How SVMs Work-RyThtU8GcT0.mp4
07. Q4 - Reduce Data Dimensionality-NzzpasA9GsM.mp4
04. Q1 - Predict Rain-ooqFCXMdxys.mp4
05. Q2 - Identify Fish-bXpONCq5ePE.mp4
Part 05-Module 01-Lesson 04_Convolutional Neural Networks
01. Introducing Alexis-38ExGpdyvJI.pt-BR.vtt
01. Introducing Alexis-38ExGpdyvJI.zh-CN.vtt
01. Introducing Alexis-38ExGpdyvJI.en.vtt
07. When do MLPs (not) work well-deMeuLdZN3Q.zh-CN.vtt
23. Visualizing CNNs-mnqS_EhEZVg.zh-CN.vtt
04. MLPs For Image Classification-TIFStebu530.zh-CN.vtt
22. Groundbreaking CNN Architectures-ddrB-mhMfkY.zh-CN.vtt
07. When do MLPs (not) work well-deMeuLdZN3Q.en.vtt
12. Stride and Padding-0r9o8hprDXQ.zh-CN.vtt
04. MLPs For Image Classification-TIFStebu530.en.vtt
23. Visualizing CNNs-mnqS_EhEZVg.pt-BR.vtt
07. When do MLPs (not) work well-deMeuLdZN3Q.pt-BR.vtt
23. Visualizing CNNs-mnqS_EhEZVg.en.vtt
22. Groundbreaking CNN Architectures-ddrB-mhMfkY.en.vtt
04. MLPs For Image Classification-TIFStebu530.pt-BR.vtt
05. Categorical Cross-Entropy-3sDYifgjFck.zh-CN.vtt
22. Groundbreaking CNN Architectures-ddrB-mhMfkY.pt-BR.vtt
12. Stride and Padding-0r9o8hprDXQ.en.vtt
12. Stride and Padding-0r9o8hprDXQ.pt-BR.vtt
15. Pooling Layers-OkkIZNs7Cyc.zh-CN.vtt
02. Applications of CNNs-HrYNL_1SV2Y.zh-CN.vtt
06. Model Validation in Keras-002jNXSM6CU.zh-CN.vtt
18. CNNs in Keras Practical Example-faFvmGDwXX0.zh-CN.vtt
05. Categorical Cross-Entropy-3sDYifgjFck.en.vtt
03. How Computers Interpret Images-V4f6p6uRhu8.zh-CN.vtt
05. Categorical Cross-Entropy-3sDYifgjFck.pt-BR.vtt
02. Applications of CNNs-HrYNL_1SV2Y.en.vtt
25. Transfer Learning-LHG5FltaR6I.zh-CN.vtt
18. CNNs in Keras Practical Example-faFvmGDwXX0.en.vtt
15. Pooling Layers-OkkIZNs7Cyc.en.vtt
06. Model Validation in Keras-002jNXSM6CU.en.vtt
03. How Computers Interpret Images-V4f6p6uRhu8.en.vtt
02. Applications of CNNs-HrYNL_1SV2Y.pt-BR.vtt
26. Transfer Learning in Keras-HsIAznMM1LA.zh-CN.vtt
img
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02-guide-how-transfer-learning-v3-06.png
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index.html
15. Pooling Layers-OkkIZNs7Cyc.pt-BR.vtt
03. How Computers Interpret Images-V4f6p6uRhu8.pt-BR.vtt
25. Transfer Learning-LHG5FltaR6I.en.vtt
06. Model Validation in Keras-002jNXSM6CU.pt-BR.vtt
10. Convolutional Layers-h5R_JvdUrUI.zh-CN.vtt
26. Transfer Learning in Keras-HsIAznMM1LA.en.vtt
18. CNNs in Keras Practical Example-faFvmGDwXX0.pt-BR.vtt
25. Transfer Learning-LHG5FltaR6I.pt-BR.vtt
26. Transfer Learning in Keras-HsIAznMM1LA.pt-BR.vtt
20. Image Augmentation in Keras-odStujZq3GY.zh-CN.vtt
10. Convolutional Layers-h5R_JvdUrUI.en.vtt
10. Convolutional Layers-h5R_JvdUrUI.pt-BR.vtt
12. Stride and Padding.html
09. Local Connectivity.html
10. Convolutional Layers (Part 1).html
09. Local Connectivity-z9wiDg0w-Dc.zh-CN.vtt
01. Introducing Alexis.html
15. Pooling Layers.html
07. When do MLPs (not) work well .html
20. Image Augmentation in Keras-odStujZq3GY.en.vtt
04. MLPs for Image Classification.html
06. Model Validation in Keras.html
11. Convolutional Layers (Part 2).html
19. Mini project CNNs in Keras.html
20. Image Augmentation in Keras-odStujZq3GY.pt-BR.vtt
21. Mini project Image Augmentation in Keras.html
11. Convolutional Layers-RnM1D-XI--8.zh-CN.vtt
22. Groundbreaking CNN Architectures.html
09. Local Connectivity-z9wiDg0w-Dc.en.vtt
03. How Computers Interpret Images.html
26. Transfer Learning in Keras.html
05. Categorical Cross-Entropy.html
09. Local Connectivity-z9wiDg0w-Dc.pt-BR.vtt
18. CNNs in Keras Practical Example.html
20. Image Augmentation in Keras.html
17. CNNs For Image Classification-l9vg_1YUlzg.zh-CN.vtt
23. Visualizing CNNs (Part 1).html
11. Convolutional Layers-RnM1D-XI--8.en.vtt
17. CNNs for Image Classification.html
16. Max Pooling Layers in Keras.html
08. Mini project Training an MLP on MNIST.html
11. Convolutional Layers-RnM1D-XI--8.pt-BR.vtt
17. CNNs For Image Classification-l9vg_1YUlzg.en.vtt
13. Convolutional Layers in Keras.html
17. CNNs For Image Classification-l9vg_1YUlzg.pt-BR.vtt
02. Applications of CNNs.html
24. Visualizing CNNs (Part 2).html
14. Quiz Dimensionality.html
25. Transfer Learning.html
01. Introducing Alexis-38ExGpdyvJI.mp4
04. MLPs For Image Classification-TIFStebu530.mp4
06. Model Validation in Keras-002jNXSM6CU.mp4
05. Categorical Cross-Entropy-3sDYifgjFck.mp4
07. When do MLPs (not) work well-deMeuLdZN3Q.mp4
15. Pooling Layers-OkkIZNs7Cyc.mp4
03. How Computers Interpret Images-V4f6p6uRhu8.mp4
12. Stride and Padding-0r9o8hprDXQ.mp4
10. Convolutional Layers-h5R_JvdUrUI.mp4
22. Groundbreaking CNN Architectures-ddrB-mhMfkY.mp4
18. CNNs in Keras Practical Example-faFvmGDwXX0.mp4
23. Visualizing CNNs-mnqS_EhEZVg.mp4
20. Image Augmentation in Keras-odStujZq3GY.mp4
26. Transfer Learning in Keras-HsIAznMM1LA.mp4
09. Local Connectivity-z9wiDg0w-Dc.mp4
25. Transfer Learning-LHG5FltaR6I.mp4
02. Applications of CNNs-HrYNL_1SV2Y.mp4
17. CNNs For Image Classification-l9vg_1YUlzg.mp4
11. Convolutional Layers-RnM1D-XI--8.mp4
Part 11-Module 02-Lesson 01_Intro to TensorFlow
12. 13 L One Hot Encoding-phYsxqlilUk.zh-CN.vtt
12. 13 L One Hot Encoding-phYsxqlilUk.pt-BR.vtt
17. Numerical Stability-_SbGcOS-jcQ.zh-CN.vtt
12. 13 L One Hot Encoding-phYsxqlilUk.en.vtt
16. 17 L Transition Into Practical Aspects Of Learning-bKqkRFOOKoA.pt-BR.vtt
16. 17 L Transition Into Practical Aspects Of Learning-bKqkRFOOKoA.zh-CN.vtt
17. Numerical Stability-_SbGcOS-jcQ.en-US.vtt
20. 29 L Optimizing A Logistic Classifier-U_7nO1dm2tY.pt-BR.vtt
20. 29 L Optimizing A Logistic Classifier-U_7nO1dm2tY.zh-CN.vtt
16. 17 L Transition Into Practical Aspects Of Learning-bKqkRFOOKoA.en-US.vtt
17. Numerical Stability-_SbGcOS-jcQ.pt-BR.vtt
20. 29 L Optimizing A Logistic Classifier-U_7nO1dm2tY.en-US.vtt
07. Supervised Classification-XTGsutypAPE.zh-CN.vtt
07. Supervised Classification-XTGsutypAPE.en.vtt
07. Supervised Classification-XTGsutypAPE.pt-BR.vtt
03. Let'S Get Started-ySIDqaXLhHw.zh-CN.vtt
img
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z93yz2vrgdaacqjowbaabie8yaaackcwmaaadshdeaaabpwhgaaia0yqwaaecamayaacbngamaajamjaeaaegtxgaaakqjywaaankemqaaagncgaaagdrhdaaaqjowbgaaie0yawaakcamaqaasbpgaaaapaljaaaa0oqxaaaaaciyaacangemaabamjagaaagtrgdaacqj
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03. Let'S Get Started-ySIDqaXLhHw.en.vtt
03. Let'S Get Started-ySIDqaXLhHw.pt-BR.vtt
01. What Is Deep Learning-INt1nULYPak.pt-BR.vtt
01. What Is Deep Learning-INt1nULYPak.zh-CN.vtt
01. What Is Deep Learning-INt1nULYPak.en.vtt
22. 31 L Momentum And Learning Rate Decay-O3QYdmQjXds.zh-CN.vtt
23. 32 L Parameter Hyperspace!-5a3-iIhdguc.zh-CN.vtt
08. Training Your Logistic Classifier-WQsdr1EJgz8.zh-CN.vtt
02. Solving Problems - Big And Small-WHcRQMGSbqg.zh-CN.vtt
23. 32 L Parameter Hyperspace!-5a3-iIhdguc.en.vtt
22. 31 L Momentum And Learning Rate Decay-O3QYdmQjXds.en.vtt
02. Solving Problems - Big And Small-WHcRQMGSbqg.pt-BR.vtt
22. 31 L Momentum And Learning Rate Decay-O3QYdmQjXds.pt-BR.vtt
08. Training Your Logistic Classifier-WQsdr1EJgz8.pt-BR.vtt
08. Training Your Logistic Classifier-WQsdr1EJgz8.en.vtt
02. Solving Problems - Big And Small-WHcRQMGSbqg.en.vtt
23. 32 L Parameter Hyperspace!-5a3-iIhdguc.pt-BR.vtt
14. 16 L Minimizing Cross-Entropy-YrDMXFhvh9E.zh-CN.vtt
14. 16 L Minimizing Cross-Entropy-YrDMXFhvh9E.en-US.vtt
14. 16 L Minimizing Cross-Entropy-YrDMXFhvh9E.pt-BR.vtt
18. Normalized Inputs And Initial Weights-WaHQ9-UXIIg.zh-CN.vtt
21. 30 L Stochastic Gradient Descent-U9iEGUd9kJ0.zh-CN.vtt
21. 30 L Stochastic Gradient Descent-U9iEGUd9kJ0.pt-BR.vtt
21. 30 L Stochastic Gradient Descent-U9iEGUd9kJ0.en.vtt
18. Normalized Inputs And Initial Weights-WaHQ9-UXIIg.en.vtt
18. Normalized Inputs And Initial Weights-WaHQ9-UXIIg.pt-BR.vtt
19. 21 L Measuring Performance-byP0DJImOSk.zh-CN.vtt
index.html
19. 21 L Measuring Performance-byP0DJImOSk.en-US.vtt
19. 21 L Measuring Performance-byP0DJImOSk.pt-BR.vtt
03. Let's Get Started .html
01. What is Deep Learning .html
23. Parameter Hyperspace .html
07. Supervised Classification.html
19. Measuring Performance .html
14. Minimizing Cross Entropy.html
21. Stochastic Gradient Descent.html
02. Solving Problems - Big and Small .html
08. Training Your Logistic Classifier .html
22. Momentum and Learning Rate Decay.html
20. Optimizing a Logistic Classifier.html
18. Normalized Inputs and Initial Weights .html
16. Practical Aspects of Learning.html
17. Quiz Numerical Stability.html
12. One-Hot Encoding.html
06. Transition to Classification.html
04. Installing TensorFlow.html
05. Hello, Tensor World!.html
10. Quiz TensorFlow Softmax.html
13. Quiz TensorFlow Cross Entropy.html
15. Categorical Cross-Entropy.html
25. Epochs.html
11. ReLU and Softmax Activation Functions.html
09. Quiz TensorFlow Linear Function.html
24. Quiz Mini-batch.html
17. Numerical Stability-_SbGcOS-jcQ.mp4
12. 13 L One Hot Encoding-phYsxqlilUk.mp4
07. Supervised Classification-XTGsutypAPE.mp4
03. Let'S Get Started-ySIDqaXLhHw.mp4
23. 32 L Parameter Hyperspace!-5a3-iIhdguc.mp4
22. 31 L Momentum And Learning Rate Decay-O3QYdmQjXds.mp4
08. Training Your Logistic Classifier-WQsdr1EJgz8.mp4
14. 16 L Minimizing Cross-Entropy-YrDMXFhvh9E.mp4
20. 29 L Optimizing A Logistic Classifier-U_7nO1dm2tY.mp4
16. 17 L Transition Into Practical Aspects Of Learning-bKqkRFOOKoA.mp4
21. 30 L Stochastic Gradient Descent-U9iEGUd9kJ0.mp4
18. Normalized Inputs And Initial Weights-WaHQ9-UXIIg.mp4
19. 21 L Measuring Performance-byP0DJImOSk.mp4
01. What Is Deep Learning-INt1nULYPak.mp4
02. Solving Problems - Big And Small-WHcRQMGSbqg.mp4
Part 03-Module 01-Lesson 04_Naive Bayes
01. Naive Bayes Intro V2-vNOiQXghgRY.zh-CN.vtt
01. Naive Bayes Intro V2-vNOiQXghgRY.pt-BR.vtt
01. Naive Bayes Intro V2-vNOiQXghgRY.en.vtt
03. SL NB 02 Known And Inferred V1 V2-DrYfZXiDLQI.zh-CN.vtt
03. SL NB 02 Known And Inferred V1 V2-DrYfZXiDLQI.pt-BR.vtt
03. SL NB 02 Known And Inferred V1 V2-DrYfZXiDLQI.en.vtt
09. SL NB 08 S Bayesian Learning 2 V1 V6-3rIYZgCXVXY.zh-CN.vtt
12. MLND SL NB Solution Naive Bayes Algorithm-QDj3xzjuYmo.zh-CN.vtt
09. SL NB 08 S Bayesian Learning 2 V1 V6-3rIYZgCXVXY.en.vtt
09. SL NB 08 S Bayesian Learning 2 V1 V6-3rIYZgCXVXY.pt-BR.vtt
06. SL NB 05 Q False Positives V1 V2-ngA6v09eP08.zh-CN.vtt
12. MLND SL NB Solution Naive Bayes Algorithm-QDj3xzjuYmo.pt-BR.vtt
12. MLND SL NB Solution Naive Bayes Algorithm-QDj3xzjuYmo.en.vtt
06. SL NB 05 Q False Positives V1 V2-ngA6v09eP08.en.vtt
06. SL NB 05 Q False Positives V1 V2-ngA6v09eP08.pt-BR.vtt
08. SL NB 07 Q Bayesian Learning 1 V1 V4-J4BmsKXPnkA.zh-CN.vtt
08. SL NB 07 Q Bayesian Learning 1 V1 V4-J4BmsKXPnkA.pt-BR.vtt
05. SL NB 04 Bayes Theorem V1 V2-nVbPJmf53AI.zh-CN.vtt
08. SL NB 07 Q Bayesian Learning 1 V1 V4-J4BmsKXPnkA.en.vtt
05. SL NB 04 Bayes Theorem V1 V2-nVbPJmf53AI.en.vtt
10. SL NB 09 Bayesian Learning 3 V1 V4-u-Hj4RsJn1o.zh-CN.vtt
05. SL NB 04 Bayes Theorem V1 V2-nVbPJmf53AI.pt-BR.vtt
10. SL NB 09 Bayesian Learning 3 V1 V4-u-Hj4RsJn1o.pt-BR.vtt
02. SL NB 01 Guess The Person V1 V1-tAOAjI-7ins.zh-CN.vtt
10. SL NB 09 Bayesian Learning 3 V1 V4-u-Hj4RsJn1o.en.vtt
02. SL NB 01 Guess The Person V1 V1-tAOAjI-7ins.pt-BR.vtt
02. SL NB 01 Guess The Person V1 V1-tAOAjI-7ins.en.vtt
index.html
07. SL NB 06 S False Positives V1 V3-Bg6_Tvcv81A.zh-CN.vtt
11. MLND SL NB Naive Bayes Algorithm-CQBMB9jwcp8.zh-CN.vtt
04. SL NB 03 Guess The Person Now V1 V2-pQgO1KF90yU.zh-CN.vtt
01. Intro.html
16. Outro.html
05. Bayes Theorem.html
02. Guess the Person.html
03. Known and Inferred.html
11. MLND SL NB Naive Bayes Algorithm-CQBMB9jwcp8.pt-BR.vtt
07. Solution False Positives.html
09. Bayesian Learning 2.html
12. Naive Bayes Algorithm 2.html
07. SL NB 06 S False Positives V1 V3-Bg6_Tvcv81A.en.vtt
11. MLND SL NB Naive Bayes Algorithm-CQBMB9jwcp8.en.vtt
04. Guess the Person Now.html
07. SL NB 06 S False Positives V1 V3-Bg6_Tvcv81A.pt-BR.vtt
10. Bayesian Learning 3.html
15. Spam Classifier - Workspace.html
14. Project.html
04. SL NB 03 Guess The Person Now V1 V2-pQgO1KF90yU.en.vtt
13. Building a Spam Classifier.html
04. SL NB 03 Guess The Person Now V1 V2-pQgO1KF90yU.pt-BR.vtt
06. Quiz False Positives.html
11. Naive Bayes Algorithm 1.html
08. Bayesian Learning 1.html
img
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spamham.png
meme.png
12. MLND SL NB Solution Naive Bayes Algorithm-QDj3xzjuYmo.mp4
09. SL NB 08 S Bayesian Learning 2 V1 V6-3rIYZgCXVXY.mp4
03. SL NB 02 Known And Inferred V1 V2-DrYfZXiDLQI.mp4
01. Naive Bayes Intro V2-vNOiQXghgRY.mp4
11. MLND SL NB Naive Bayes Algorithm-CQBMB9jwcp8.mp4
06. SL NB 05 Q False Positives V1 V2-ngA6v09eP08.mp4
08. SL NB 07 Q Bayesian Learning 1 V1 V4-J4BmsKXPnkA.mp4
05. SL NB 04 Bayes Theorem V1 V2-nVbPJmf53AI.mp4
02. SL NB 01 Guess The Person V1 V1-tAOAjI-7ins.mp4
10. SL NB 09 Bayesian Learning 3 V1 V4-u-Hj4RsJn1o.mp4
07. SL NB 06 S False Positives V1 V3-Bg6_Tvcv81A.mp4
04. SL NB 03 Guess The Person Now V1 V2-pQgO1KF90yU.mp4
Part 08-Module 01-Lesson 01_Conduct a Job Search
04. Open Yourself Up to Opportunity-1OamTNkk1xM.en.vtt
04. Open Yourself Up to Opportunity-1OamTNkk1xM.zh-CN.vtt
04. Open Yourself Up to Opportunity-1OamTNkk1xM.es-MX.vtt
04. Open Yourself Up to Opportunity-1OamTNkk1xM.pt-BR.vtt
01. Introduction-axcFtHK6If4.zh-CN.vtt
01. Introduction-axcFtHK6If4.es-MX.vtt
01. Introduction-axcFtHK6If4.pt-BR.vtt
02. Job Search Mindset-cBk7bno3KS0.zh-CN.vtt
01. Introduction-axcFtHK6If4.en.vtt
02. Job Search Mindset-cBk7bno3KS0.en.vtt
02. Job Search Mindset-cBk7bno3KS0.es-MX.vtt
02. Job Search Mindset-cBk7bno3KS0.pt-BR.vtt
index.html
03. Target Your Application to An Employer-X9JBzbrkcvs.zh-CN.vtt
03. Target Your Application to An Employer-X9JBzbrkcvs.en.vtt
03. Target Your Application to An Employer-X9JBzbrkcvs.pt-BR.vtt
03. Target Your Application to An Employer-X9JBzbrkcvs.es-MX.vtt
01. Introduction.html
02. Job Search Mindset.html
04. Open Yourself Up to Opportunity.html
03. Target Your Application to An Employer.html
05. Resources in Your Career Portal.html
img
career-portal-sidebar.png
screen-shot-2017-10-31-at-1.06.42-pm.png
04. Open Yourself Up to Opportunity-1OamTNkk1xM.mp4
01. Introduction-axcFtHK6If4.mp4
02. Job Search Mindset-cBk7bno3KS0.mp4
03. Target Your Application to An Employer-X9JBzbrkcvs.mp4
Part 10-Module 02-Lesson 01_Introduction and Efficiency
04. Syntax-08M93RaBSgU.zh-CN.vtt
04. Syntax-08M93RaBSgU.en.vtt
04. Syntax-08M93RaBSgU.en-US.vtt
04. Syntax-08M93RaBSgU.pt-BR.vtt
09. Notation Continued-ZeGnkrKZWBQ.zh-CN.vtt
01. Course Introduction-NKBUbUiedzc.zh-CN.vtt
09. Notation Continued-ZeGnkrKZWBQ.en.vtt
09. Notation Continued-ZeGnkrKZWBQ.en-US.vtt
09. Notation Continued-ZeGnkrKZWBQ.pt-BR.vtt
07. Efficiency-I-RASDPbDrI.zh-CN.vtt
01. Course Introduction-NKBUbUiedzc.en.vtt
01. Course Introduction-NKBUbUiedzc.en-US.vtt
01. Course Introduction-NKBUbUiedzc.pt-BR.vtt
08. Notation Intro-xHwIU4j3gBc.zh-CN.vtt
07. Efficiency-I-RASDPbDrI.en.vtt
07. Efficiency-I-RASDPbDrI.en-US.vtt
07. Efficiency-I-RASDPbDrI.pt-BR.vtt
10. Worst Case and Approximation-ZYcmui02J40.zh-CN.vtt
08. Notation Intro-xHwIU4j3gBc.pt-BR.vtt
08. Notation Intro-xHwIU4j3gBc.en.vtt
08. Notation Intro-xHwIU4j3gBc.en-US.vtt
index.html
10. Worst Case and Approximation-ZYcmui02J40.pt-BR.vtt
10. Worst Case and Approximation-ZYcmui02J40.en.vtt
10. Worst Case and Approximation-ZYcmui02J40.en-US.vtt
04. Syntax.html
07. Efficiency.html
03. Course Expectations.html
09. Notation Continued.html
01. Course Introduction.html
08. Notation Intro.html
10. Worst Case and Approximation.html
02. Course Outline.html
06. Python The Basics.html
11. Efficiency Practice.html
05. Python Practice.html
img
7883232307.gif
7889679710.gif
04. Syntax-08M93RaBSgU.mp4
09. Notation Continued-ZeGnkrKZWBQ.mp4
10. Worst Case and Approximation-ZYcmui02J40.mp4
08. Notation Intro-xHwIU4j3gBc.mp4
01. Course Introduction-NKBUbUiedzc.mp4
07. Efficiency-I-RASDPbDrI.mp4
Part 06-Module 01-Lesson 02_The RL Framework The Problem
01. Introduction-X_9l_ZqXXBA.zh-CN.vtt
01. Introduction-X_9l_ZqXXBA.en.vtt
01. Introduction-X_9l_ZqXXBA.pt-BR.vtt
17. MDPs, Part 3-UlXHFbla3QI.zh-CN.vtt
03. Episodic vs. Continuing Tasks-E1I-BPanSM8.zh-CN.vtt
07. Goals and Rewards, Part 1-XPnj3Ya3EuM.zh-CN.vtt
03. Episodic vs. Continuing Tasks-E1I-BPanSM8.en.vtt
17. MDPs, Part 3-UlXHFbla3QI.en.vtt
06. The Reward Hypothesis-uAqNwgZ49JE.zh-CN.vtt
03. Episodic vs. Continuing Tasks-E1I-BPanSM8.pt-BR.vtt
07. Goals and Rewards, Part 1-XPnj3Ya3EuM.en.vtt
17. MDPs, Part 3-UlXHFbla3QI.pt-BR.vtt
13. MDPs, Part 1-NBWbluSbxPg.zh-CN.vtt
06. The Reward Hypothesis-uAqNwgZ49JE.en.vtt
07. Goals and Rewards, Part 1-XPnj3Ya3EuM.pt-BR.vtt
06. The Reward Hypothesis-uAqNwgZ49JE.pt-BR.vtt
13. MDPs, Part 1-NBWbluSbxPg.en.vtt
img
maze.png
index.jpg
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pup.jpg
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13. MDPs, Part 1-NBWbluSbxPg.pt-BR.vtt
10. Cumulative Reward-ysriH65lV9o.zh-CN.vtt
02. The Setting, Revisited-V6Q1uF8a6kA.zh-CN.vtt
index.html
08. Goals and Rewards, Part 2-pVIFc72VYH8.zh-CN.vtt
10. Cumulative Reward-ysriH65lV9o.en.vtt
02. The Setting, Revisited-V6Q1uF8a6kA.en.vtt
14. MDPs, Part 2-CUTtQvxKkNw.zh-CN.vtt
08. Goals and Rewards, Part 2-pVIFc72VYH8.en.vtt
02. The Setting, Revisited-V6Q1uF8a6kA.pt-BR.vtt
11. Discounted Return-opXGNPwwn7g.zh-CN.vtt
10. Cumulative Reward-ysriH65lV9o.pt-BR.vtt
08. Goals and Rewards, Part 2-pVIFc72VYH8.pt-BR.vtt
14. MDPs, Part 2.html
17. MDPs, Part 3.html
10. Cumulative Reward.html
06. The Reward Hypothesis.html
02. The Setting, Revisited.html
07. Goals and Rewards, Part 1.html
03. Episodic vs. Continuing Tasks.html
01. Introduction.html
14. MDPs, Part 2-CUTtQvxKkNw.en.vtt
11. Discounted Return-opXGNPwwn7g.en.vtt
08. Goals and Rewards, Part 2.html
14. MDPs, Part 2-CUTtQvxKkNw.pt-BR.vtt
11. Discounted Return.html
13. MDPs, Part 1.html
11. Discounted Return-opXGNPwwn7g.pt-BR.vtt
05. Quiz Episodic or Continuing.html
15. Quiz One-Step Dynamics, Part 1.html
18. Finite MDPs.html
12. Quiz Pole-Balancing.html
19. Summary.html
04. Quiz Test Your Intuition.html
09. Quiz Goals and Rewards.html
16. Quiz One-Step Dynamics, Part 2.html
01. Introduction-X_9l_ZqXXBA.mp4
13. MDPs, Part 1-NBWbluSbxPg.mp4
06. The Reward Hypothesis-uAqNwgZ49JE.mp4
14. MDPs, Part 2-CUTtQvxKkNw.mp4
07. Goals and Rewards, Part 1-XPnj3Ya3EuM.mp4
02. The Setting, Revisited-V6Q1uF8a6kA.mp4
08. Goals and Rewards, Part 2-pVIFc72VYH8.mp4
10. Cumulative Reward-ysriH65lV9o.mp4
03. Episodic vs. Continuing Tasks-E1I-BPanSM8.mp4
11. Discounted Return-opXGNPwwn7g.mp4
17. MDPs, Part 3-UlXHFbla3QI.mp4
Part 03-Module 01-Lesson 03_Decision Trees
12. MLND SL DT 10 Q Information Gain MAIN V1-tVLOLPEtLFw.pt-BR.vtt
12. MLND SL DT 10 Q Information Gain MAIN V1-tVLOLPEtLFw.zh-CN.vtt
12. MLND SL DT 10 Q Information Gain MAIN V1-tVLOLPEtLFw.en.vtt
03. MLND SL DT 02 Recommending Apps 2 MAIN V3-KSrIYqKZwCA.zh-CN.vtt
03. MLND SL DT 02 Recommending Apps 2 MAIN V3-KSrIYqKZwCA.pt-BR.vtt
02. MLND SL DT 01 Recommending Apps 1 MAIN V3-uI_yNrqqKVg.zh-CN.vtt
03. MLND SL DT 02 Recommending Apps 2 MAIN V3-KSrIYqKZwCA.en.vtt
05. MLND SL DT 04 Q Student Admissions V3 MAIN V1-MOa335cQGI4.pt-BR.vtt
05. MLND SL DT 04 Q Student Admissions V3 MAIN V1-MOa335cQGI4.zh-CN.vtt
02. MLND SL DT 01 Recommending Apps 1 MAIN V3-uI_yNrqqKVg.en.vtt
02. MLND SL DT 01 Recommending Apps 1 MAIN V3-uI_yNrqqKVg.pt-BR.vtt
08. Entropy Formula-iZiSYrOKvpo.pt-BR.vtt
08. Entropy Formula-iZiSYrOKvpo.zh-CN.vtt
08. Entropy Formula-iZiSYrOKvpo.en.vtt
05. MLND SL DT 04 Q Student Admissions V3 MAIN V1-MOa335cQGI4.en.vtt
06. Student Admissions-TdgBi6LtOB8.pt-BR.vtt
06. Student Admissions-TdgBi6LtOB8.zh-CN.vtt
15. MLND SL DT 13 Random Forests MAIN V1-n5DhXhcYKcw.zh-CN.vtt
06. Student Admissions-TdgBi6LtOB8.en.vtt
15. MLND SL DT 13 Random Forests MAIN V1-n5DhXhcYKcw.pt-BR.vtt
04. Recommending Apps-nEvW8B1HNq4.pt-BR.vtt
10. Entropy Formula-w73JTBVeyjE.zh-CN.vtt
04. Recommending Apps-nEvW8B1HNq4.zh-CN.vtt
10. Entropy Formula-w73JTBVeyjE.pt-BR.vtt
15. MLND SL DT 13 Random Forests MAIN V1-n5DhXhcYKcw.en.vtt
04. Recommending Apps-nEvW8B1HNq4.en.vtt
13. Information Gain-k9iZL53PAmw.pt-BR.vtt
13. Information Gain-k9iZL53PAmw.zh-CN.vtt
10. Entropy Formula-w73JTBVeyjE.en.vtt
14. Maximizing Information Gain-3FgJOpKfdY8.pt-BR.vtt
13. Information Gain-k9iZL53PAmw.en.vtt
14. Maximizing Information Gain-3FgJOpKfdY8.zh-CN.vtt
09. MLND SL DT 08 Entropy Formula 2 MAIN V2-6GHg70hrSJw.pt-BR.vtt
14. Maximizing Information Gain-3FgJOpKfdY8.en.vtt
07. Entropy-piLpj1V1HEk.pt-BR.vtt
img
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table.png
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01. MLND SL DT 00 Intro V2-l34ijtQhVNk.pt-BR.vtt
01. MLND SL DT 00 Intro V2-l34ijtQhVNk.zh-CN.vtt
07. Entropy-piLpj1V1HEk.zh-CN.vtt
01. MLND SL DT 00 Intro V2-l34ijtQhVNk.en.vtt
09. MLND SL DT 08 Entropy Formula 2 MAIN V2-6GHg70hrSJw.zh-CN.vtt
07. Entropy-piLpj1V1HEk.en.vtt
09. MLND SL DT 08 Entropy Formula 2 MAIN V2-6GHg70hrSJw.en.vtt
index.html
07. Entropy.html
20. Outro.html
01. Intro.html
04. Recommending Apps 3.html
14. Maximizing Information Gain.html
15. Random Forests.html
06. Solution Student Admissions.html
10. Entropy Formula 3.html
19. [Solution] Titanic Survival Model.html
18. Titanic Survival Model with Decision Trees.html
13. Solution Information Gain.html
03. Recommending Apps 2.html
09. Entropy Formula 2.html
05. Quiz Student Admissions.html
08. Entropy Formula 1.html
11. Multiclass Entropy.html
12. Quiz Information Gain.html
02. Recommending Apps 1.html
16. Hyperparameters.html
17. Decision Trees in sklearn.html
12. MLND SL DT 10 Q Information Gain MAIN V1-tVLOLPEtLFw.mp4
05. MLND SL DT 04 Q Student Admissions V3 MAIN V1-MOa335cQGI4.mp4
03. MLND SL DT 02 Recommending Apps 2 MAIN V3-KSrIYqKZwCA.mp4
08. Entropy Formula-iZiSYrOKvpo.mp4
02. MLND SL DT 01 Recommending Apps 1 MAIN V3-uI_yNrqqKVg.mp4
06. Student Admissions-TdgBi6LtOB8.mp4
04. Recommending Apps-nEvW8B1HNq4.mp4
10. Entropy Formula-w73JTBVeyjE.mp4
15. MLND SL DT 13 Random Forests MAIN V1-n5DhXhcYKcw.mp4
13. Information Gain-k9iZL53PAmw.mp4
09. MLND SL DT 08 Entropy Formula 2 MAIN V2-6GHg70hrSJw.mp4
07. Entropy-piLpj1V1HEk.mp4
14. Maximizing Information Gain-3FgJOpKfdY8.mp4
01. MLND SL DT 00 Intro V2-l34ijtQhVNk.mp4
Part 02-Module 01-Lesson 01_Training and Testing Models
08. MLND Turning Paramaters-eSv2lPcnRM0.pt-BR.vtt
08. MLND Turning Paramaters-eSv2lPcnRM0.zh-CN.vtt
08. MLND Turning Paramaters-eSv2lPcnRM0.en.vtt
01. 01 Intro-4C4PuJANIdE.zh-CN.vtt
01. 01 Intro-4C4PuJANIdE.pt-BR.vtt
01. 01 Intro-4C4PuJANIdE.en.vtt
02. 02 Intro SC V1-mIgABrjJVBY.pt-BR.vtt
02. 02 Intro SC V1-mIgABrjJVBY.en.vtt
index.html
02. Outline.html
01. Intro.html
08. Tuning Parameters Automatically.html
03. Stats Refresher.html
09. Testing-gmxGRJSKEb0.zh-CN.vtt
09. Testing-gmxGRJSKEb0.pt-BR.vtt
09. Testing-gmxGRJSKEb0.en-US.vtt
04. Loading data into Pandas.html
img
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points.png
linear-boundary.png
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09. Testing your models.html
05. NumPy Arrays.html
10. Quiz Testing in sklearn.html
07. Tuning Parameters Manually.html
06. Training models in sklearn.html
02. 02 Intro SC V1-mIgABrjJVBY.mp4
08. MLND Turning Paramaters-eSv2lPcnRM0.mp4
01. 01 Intro-4C4PuJANIdE.mp4
09. Testing-gmxGRJSKEb0.mp4
Part 10-Module 02-Lesson 05_Trees
01. Trees-PXie7f22v2Q.zh-CN.vtt
01. Trees-PXie7f22v2Q.pt-BR.vtt
01. Trees-PXie7f22v2Q.en.vtt
01. Trees-PXie7f22v2Q.en-US.vtt
10. Binary Search Trees-7-ZQrugO-Yc.zh-CN.vtt
10. Binary Search Trees-7-ZQrugO-Yc.pt-BR.vtt
10. Binary Search Trees-7-ZQrugO-Yc.en.vtt
10. Binary Search Trees-7-ZQrugO-Yc.en-US.vtt
13. BST Complications-pcB0wV7myy4.pt-BR.vtt
16. Heapify-CAbDbiCfERY.zh-CN.vtt
02. Tree Basics-oaxLPzaXRDc.zh-CN.vtt
16. Heapify-CAbDbiCfERY.pt-BR.vtt
16. Heapify-CAbDbiCfERY.en.vtt
16. Heapify-CAbDbiCfERY.en-US.vtt
02. Tree Basics-oaxLPzaXRDc.pt-BR.vtt
02. Tree Basics-oaxLPzaXRDc.en.vtt
02. Tree Basics-oaxLPzaXRDc.en-US.vtt
08. Search and Delete-KbL-HK3ztX8.pt-BR.vtt
08. Search and Delete-KbL-HK3ztX8.zh-CN.vtt
18. Self-Balancing Trees-EHI548K3jiw.zh-CN.vtt
20. Tree Rotations-O5Yl-m0YbVA.zh-CN.vtt
17. Heap Implementation-2LAdml6_pDY.zh-CN.vtt
05. Tree Traversal-KZOdmzypynw.zh-CN.vtt
13. BST Complications-pcB0wV7myy4.zh-CN.vtt
17. Heap Implementation-2LAdml6_pDY.pt-BR.vtt
03. Tree Terminology-mPUsDUR_sj8.zh-CN.vtt
08. Search and Delete-KbL-HK3ztX8.en.vtt
08. Search and Delete-KbL-HK3ztX8.en-US.vtt
18. Self-Balancing Trees-EHI548K3jiw.pt-BR.vtt
12. BSTs-abRNGLhGUmE.zh-CN.vtt
20. Tree Rotations-O5Yl-m0YbVA.pt-BR.vtt
20. Tree Rotations-O5Yl-m0YbVA.en.vtt
20. Tree Rotations-O5Yl-m0YbVA.en-US.vtt
03. Tree Terminology-mPUsDUR_sj8.pt-BR.vtt
18. Self-Balancing Trees-EHI548K3jiw.en.vtt
18. Self-Balancing Trees-EHI548K3jiw.en-US.vtt
17. Heap Implementation-2LAdml6_pDY.en.vtt
17. Heap Implementation-2LAdml6_pDY.en-US.vtt
05. Tree Traversal-KZOdmzypynw.pt-BR.vtt
12. BSTs-abRNGLhGUmE.pt-BR.vtt
03. Tree Terminology-mPUsDUR_sj8.en.vtt
03. Tree Terminology-mPUsDUR_sj8.en-US.vtt
19. Red-Black Trees - Insertion-dIuWLtWnkgs.zh-CN.vtt
05. Tree Traversal-KZOdmzypynw.en.vtt
05. Tree Traversal-KZOdmzypynw.en-US.vtt
12. BSTs-abRNGLhGUmE.en.vtt
12. BSTs-abRNGLhGUmE.en-US.vtt
19. Red-Black Trees - Insertion-dIuWLtWnkgs.pt-BR.vtt
09. Insert-j6PkPa2ZHWg.zh-CN.vtt
19. Red-Black Trees - Insertion-dIuWLtWnkgs.en.vtt
19. Red-Black Trees - Insertion-dIuWLtWnkgs.en-US.vtt
15. Heaps-M3B0UJWS_ag.zh-CN.vtt
09. Insert-j6PkPa2ZHWg.pt-BR.vtt
15. Heaps-M3B0UJWS_ag.pt-BR.vtt
09. Insert-j6PkPa2ZHWg.en.vtt
09. Insert-j6PkPa2ZHWg.en-US.vtt
15. Heaps-M3B0UJWS_ag.en.vtt
15. Heaps-M3B0UJWS_ag.en-US.vtt
06. Depth-First Traversals-wp5ohHFTieM.zh-CN.vtt
06. Depth-First Traversals-wp5ohHFTieM.pt-BR.vtt
06. Depth-First Traversals-wp5ohHFTieM.en.vtt
06. Depth-First Traversals-wp5ohHFTieM.en-US.vtt
index.html
13. BST Complications.html
12. BSTs.html
15. Heaps.html
01. Trees.html
09. Insert.html
16. Heapify.html
02. Tree Basics.html
05. Tree Traversal.html
20. Tree Rotations.html
03. Tree Terminology.html
08. Search and Delete.html
17. Heap Implementation.html
10. Binary Search Trees.html
18. Self-Balancing Trees.html
06. Depth-First Traversals.html
19. Red-Black Trees - Insertion.html
07. Tree Traversal Practice.html
04. Tree Practice.html
14. BST Practice.html
11. Binary Tree Practice.html
img
7900766165.gif
tree-traversal-practice.jpg
13. BST Complications-pcB0wV7myy4.mp4
08. Search and Delete-KbL-HK3ztX8.mp4
16. Heapify-CAbDbiCfERY.mp4
02. Tree Basics-oaxLPzaXRDc.mp4
17. Heap Implementation-2LAdml6_pDY.mp4
19. Red-Black Trees - Insertion-dIuWLtWnkgs.mp4
12. BSTs-abRNGLhGUmE.mp4
20. Tree Rotations-O5Yl-m0YbVA.mp4
18. Self-Balancing Trees-EHI548K3jiw.mp4
03. Tree Terminology-mPUsDUR_sj8.mp4
15. Heaps-M3B0UJWS_ag.mp4
05. Tree Traversal-KZOdmzypynw.mp4
09. Insert-j6PkPa2ZHWg.mp4
01. Trees-PXie7f22v2Q.mp4
10. Binary Search Trees-7-ZQrugO-Yc.mp4
06. Depth-First Traversals-wp5ohHFTieM.mp4
Part 04-Module 02-Lesson 03_Hierarchical and Density-based Clustering
12. MLND - Unsupervised Learning - L2 09 DBSCAN Implementation MAIN V1 V1-qEMUzQFylg8.zh-CN.vtt
12. MLND - Unsupervised Learning - L2 09 DBSCAN Implementation MAIN V1 V1-qEMUzQFylg8.pt-BR.vtt
12. MLND - Unsupervised Learning - L2 09 DBSCAN Implementation MAIN V1 V1-qEMUzQFylg8.en.vtt
02. MLND - Unsupervised Learning - L2 02 V1-Ed6RKuBzKWA.zh-CN.vtt
02. MLND - Unsupervised Learning - L2 02 V1-Ed6RKuBzKWA.en.vtt
02. MLND - Unsupervised Learning - L2 02 V1-Ed6RKuBzKWA.pt-BR.vtt
06. MLND - Unsupervised Learning - L2 06 Hierarchical Clustering Implementation MAIN V1 V1-tRqKsk5M9Mc.pt-BR.vtt
09. MLND - Unsupervised Learning - L2 07 HC Examples & Applications MAIN V1 V2-HTahFoQwk2g.zh-CN.vtt
06. MLND - Unsupervised Learning - L2 06 Hierarchical Clustering Implementation MAIN V1 V1-tRqKsk5M9Mc.zh-CN.vtt
06. MLND - Unsupervised Learning - L2 06 Hierarchical Clustering Implementation MAIN V1 V1-tRqKsk5M9Mc.en.vtt
09. MLND - Unsupervised Learning - L2 07 HC Examples & Applications MAIN V1 V2-HTahFoQwk2g.en.vtt
09. MLND - Unsupervised Learning - L2 07 HC Examples & Applications MAIN V1 V2-HTahFoQwk2g.pt-BR.vtt
15. MLND - Unsupervised Learning - L2 10 DBSCAN Examples & Applications MAIN V1 V2-GhyFsjQ4FkA.zh-CN.vtt
15. MLND - Unsupervised Learning - L2 10 DBSCAN Examples & Applications MAIN V1 V2-GhyFsjQ4FkA.pt-BR.vtt
15. MLND - Unsupervised Learning - L2 10 DBSCAN Examples & Applications MAIN V1 V2-GhyFsjQ4FkA.en.vtt
01. MLND - Unsupervised Learning - L2 01 V2-NHb8w_M8nDY.pt-BR.vtt
01. MLND - Unsupervised Learning - L2 01 V2-NHb8w_M8nDY.zh-CN.vtt
index.html
01. MLND - Unsupervised Learning - L2 01 V2-NHb8w_M8nDY.en.vtt
03. MLND - Unsupervised Learning - L2 03 V2-pd9Ix3WMP_Q.zh-CN.vtt
04. MLND - Unsupervised Learning - L2 04 Examining SingleLink Clustering MAIN V1 V2-foLcmCOLDos.pt-BR.vtt
03. MLND - Unsupervised Learning - L2 03 V2-pd9Ix3WMP_Q.pt-BR.vtt
04. MLND - Unsupervised Learning - L2 04 Examining SingleLink Clustering MAIN V1 V2-foLcmCOLDos.zh-CN.vtt
11. MLND - Unsupervised Learning - L2 08 DBSCAN MAIN V1 V2--dqyFkfnctI.pt-BR.vtt
11. MLND - Unsupervised Learning - L2 08 DBSCAN MAIN V1 V2--dqyFkfnctI.zh-CN.vtt
03. MLND - Unsupervised Learning - L2 03 V2-pd9Ix3WMP_Q.en.vtt
04. MLND - Unsupervised Learning - L2 04 Examining SingleLink Clustering MAIN V1 V2-foLcmCOLDos.en.vtt
01. K-means considerations.html
11. MLND - Unsupervised Learning - L2 08 DBSCAN MAIN V1 V2--dqyFkfnctI.en.vtt
02. Overview of other clustering methods.html
03. Hierarchical clustering single-link.html
12. DBSCAN implementation.html
05. Complete-link, average-link, Ward.html
04. Examining single-link clustering.html
06. Hierarchical clustering implementation.html
13. [Lab] DBSCAN.html
14. [Lab Solution] DBSCAN.html
07. [Lab] Hierarchical clustering .html
08. [Lab Solution] Hierarchical Clustering.html
11. DBSCAN.html
05. MLND - Unsupervised Learning - L2 05 CompleteLink AverageLink Ward MAIN V1 V2-dWGQVcZ95d0.zh-CN.vtt
15. DBSCAN examples & applications.html
09. HC examples and applications.html
05. MLND - Unsupervised Learning - L2 05 CompleteLink AverageLink Ward MAIN V1 V2-dWGQVcZ95d0.pt-BR.vtt
05. MLND - Unsupervised Learning - L2 05 CompleteLink AverageLink Ward MAIN V1 V2-dWGQVcZ95d0.en.vtt
16. [Quiz] DBSCAN.html
10. [Quiz] Hierarchical clustering.html
02. MLND - Unsupervised Learning - L2 02 V1-Ed6RKuBzKWA.mp4
12. MLND - Unsupervised Learning - L2 09 DBSCAN Implementation MAIN V1 V1-qEMUzQFylg8.mp4
06. MLND - Unsupervised Learning - L2 06 Hierarchical Clustering Implementation MAIN V1 V1-tRqKsk5M9Mc.mp4
09. MLND - Unsupervised Learning - L2 07 HC Examples & Applications MAIN V1 V2-HTahFoQwk2g.mp4
01. MLND - Unsupervised Learning - L2 01 V2-NHb8w_M8nDY.mp4
15. MLND - Unsupervised Learning - L2 10 DBSCAN Examples & Applications MAIN V1 V2-GhyFsjQ4FkA.mp4
03. MLND - Unsupervised Learning - L2 03 V2-pd9Ix3WMP_Q.mp4
11. MLND - Unsupervised Learning - L2 08 DBSCAN MAIN V1 V2--dqyFkfnctI.mp4
05. MLND - Unsupervised Learning - L2 05 CompleteLink AverageLink Ward MAIN V1 V2-dWGQVcZ95d0.mp4
04. MLND - Unsupervised Learning - L2 04 Examining SingleLink Clustering MAIN V1 V2-foLcmCOLDos.mp4
Part 06-Module 02-Lesson 03_Policy-Based Methods
01. M2L3 01 V1-YOSREyp04HA.zh-CN.vtt
06. M2L3 06 V1-RMjdQkl6CqE.zh-CN.vtt
01. M2L3 01 V1-YOSREyp04HA.en.vtt
06. M2L3 06 V1-RMjdQkl6CqE.en.vtt
08. M2L3 08 V1-og3W6CXn1F0.zh-CN.vtt
08. M2L3 08 V1-og3W6CXn1F0.en.vtt
index.html
04. M2L3 04 V1-QicxmyE5vTo.zh-CN.vtt
03. M2L3 03 V2-TePX-0Bs23E.zh-CN.vtt
08. Recap.html
05. Policy Gradients.html
01. Policy-Based Methods.html
04. Stochastic Policy Search.html
02. Why Policy-Based Methods.html
06. Monte Carlo Policy Gradients.html
07. Constrained Policy Gradients.html
03. Policy Function Approximation.html
04. M2L3 04 V1-QicxmyE5vTo.en.vtt
03. M2L3 03 V2-TePX-0Bs23E.en.vtt
05. M2L3 05 V1-eZxxNNIZuwA.zh-CN.vtt
05. M2L3 05 V1-eZxxNNIZuwA.en.vtt
07. M2L3 07 V2-ZBLLGIN1EfU.zh-CN.vtt
02. M2L3 02 V2-ToS8vXGdODE.zh-CN.vtt
02. M2L3 02 V2-ToS8vXGdODE.en.vtt
07. M2L3 07 V2-ZBLLGIN1EfU.en.vtt
06. M2L3 06 V1-RMjdQkl6CqE.mp4
01. M2L3 01 V1-YOSREyp04HA.mp4
08. M2L3 08 V1-og3W6CXn1F0.mp4
05. M2L3 05 V1-eZxxNNIZuwA.mp4
03. M2L3 03 V2-TePX-0Bs23E.mp4
04. M2L3 04 V1-QicxmyE5vTo.mp4
02. M2L3 02 V2-ToS8vXGdODE.mp4
07. M2L3 07 V2-ZBLLGIN1EfU.mp4
Part 08-Module 03-Lesson 01_Craft Your Cover Letter
06. Write the Conclusion-i3ozyhGPmIg.en.vtt
06. Write the Conclusion-i3ozyhGPmIg.es-MX.vtt
06. Write the Conclusion-i3ozyhGPmIg.pt-BR.vtt
03. Cover Letter Components-DVvLiKedRw4.zh-CN.vtt
03. Cover Letter Components-DVvLiKedRw4.es-MX.vtt
03. Cover Letter Components-DVvLiKedRw4.en.vtt
03. Cover Letter Components-DVvLiKedRw4.pt-BR.vtt
07. Format-Xlqoq-SoJso.es-MX.vtt
07. Format-Xlqoq-SoJso.pt-BR.vtt
02. Purpose-7F7cMCTcyhM.es-MX.vtt
07. Format-Xlqoq-SoJso.zh-CN.vtt
02. Purpose-7F7cMCTcyhM.pt-BR.vtt
02. Purpose-7F7cMCTcyhM.zh-CN.vtt
02. Purpose-7F7cMCTcyhM.en.vtt
07. Format-Xlqoq-SoJso.en.vtt
01. Get an Interview with a Cover Letter!-BH1KY63YfAM.zh-CN.vtt
04. Writing Your Introduction-5S5PH73WLLY.es-MX.vtt
04. Writing Your Introduction-5S5PH73WLLY.pt-BR.vtt
04. Writing Your Introduction-5S5PH73WLLY.en.vtt
01. Get an Interview with a Cover Letter!-BH1KY63YfAM.es-MX.vtt
01. Get an Interview with a Cover Letter!-BH1KY63YfAM.pt-BR.vtt
01. Get an Interview with a Cover Letter!-BH1KY63YfAM.en.vtt
05. Writing the Body-aK9Qnv3a6Wg.es-MX.vtt
05. Writing the Body-aK9Qnv3a6Wg.pt-BR.vtt
05. Writing the Body-aK9Qnv3a6Wg.en.vtt
index.html
02. Purpose of the Cover Letter.html
01. Get an Interview with a Cover Letter!.html
07. Format.html
08. Resources in Your Career Portal.html
06. Write the Conclusion.html
03. Cover Letter Components.html
Project Description - Craft Your Cover Letter.html
04. Write the Introduction.html
05. Write the Body.html
Project Rubric - Craft Your Cover Letter.html
img
career-portal-sidebar.png
cover-letter-intro-bad.png
cover-letter-good-conclusion.png
cover-letter-intro-good.png
cover-letter-body-good.png
cover-letter-career-service-example.png
screen-shot-2017-10-31-at-1.06.42-pm.png
06. Write the Conclusion-i3ozyhGPmIg.mp4
02. Purpose-7F7cMCTcyhM.mp4
03. Cover Letter Components-DVvLiKedRw4.mp4
07. Format-Xlqoq-SoJso.mp4
04. Writing Your Introduction-5S5PH73WLLY.mp4
01. Get an Interview with a Cover Letter!-BH1KY63YfAM.mp4
05. Writing the Body-aK9Qnv3a6Wg.mp4
Part 06-Module 01-Lesson 05_Monte Carlo Methods
01. Introduction-W2EP3riQSus.zh-CN.vtt
01. Introduction-W2EP3riQSus.en.vtt
01. Introduction-W2EP3riQSus.pt-BR.vtt
12. MC Control Policy Evaluation-3_opwMzpEEI.zh-CN.vtt
09. Generalized Policy Iteration-XRmz4nolEsw.zh-CN.vtt
12. MC Control Policy Evaluation-3_opwMzpEEI.en.vtt
09. Generalized Policy Iteration-XRmz4nolEsw.en.vtt
12. MC Control Policy Evaluation-3_opwMzpEEI.pt-BR.vtt
09. Generalized Policy Iteration-XRmz4nolEsw.pt-BR.vtt
18. MC Control Constant-alpha-QFV1nI9Zpoo.zh-CN.vtt
18. MC Control Constant-alpha-QFV1nI9Zpoo.en.vtt
18. MC Control Constant-alpha-QFV1nI9Zpoo.pt-BR.vtt
10. MC Control Incremental Mean-E2RITH-2NUE.zh-CN.vtt
06. MC Prediction Action Values-08tLtbh0xLs.zh-CN.vtt
index.html
10. MC Control Incremental Mean-E2RITH-2NUE.en.vtt
13. MC Control Policy Improvement-2RKH-BInX7s.zh-CN.vtt
10. MC Control Incremental Mean-E2RITH-2NUE.pt-BR.vtt
06. MC Prediction Action Values-08tLtbh0xLs.en.vtt
13. MC Control Policy Improvement-2RKH-BInX7s.en.vtt
06. MC Prediction Action Values-08tLtbh0xLs.pt-BR.vtt
03. MC Prediction State Values.html
06. MC Prediction Action Values.html
10. MC Control Incremental Mean.html
18. MC Control Constant-alpha, Part 1.html
09. Generalized Policy Iteration.html
12. MC Control Policy Evaluation.html
13. MC Control Policy Improvement.html
13. MC Control Policy Improvement-2RKH-BInX7s.pt-BR.vtt
03. MC Prediction State Values-0q2wSWyuBj8.zh-CN.vtt
01. Introduction.html
08. Mini Project MC (Part 2).html
17. Mini Project MC (Part 3).html
21. Mini Project MC (Part 4).html
05. Mini Project MC (Parts 0 and 1).html
16. Implementation.html
20. Implementation.html
03. MC Prediction State Values-0q2wSWyuBj8.en.vtt
07. Implementation.html
03. MC Prediction State Values-0q2wSWyuBj8.pt-BR.vtt
02. OpenAI Gym BlackjackEnv.html
04. Implementation.html
11. Quiz Incremental Mean.html
14. Quiz Epsilon-Greedy Policies.html
22. Summary.html
19. MC Control Constant-alpha, Part 2.html
15. Exploration vs. Exploitation.html
img
screen-shot-2017-10-04-at-2.46.11-pm.png
screen-shot-2017-10-12-at-5.47.45-pm.png
screen-shot-2017-10-05-at-3.55.40-pm.png
constant-alpha.png
incremental.png
2-card-21.png
exploration-vs.-exploitation.png
screen-shot-2017-10-04-at-5.01.26-pm.png
mc-control-constant-a.png
mc-control-glie.png
mc-pred-state.png
mc-pred-action.png
screen-shot-2017-10-04-at-4.58.58-pm.png
01. Introduction-W2EP3riQSus.mp4
09. Generalized Policy Iteration-XRmz4nolEsw.mp4
12. MC Control Policy Evaluation-3_opwMzpEEI.mp4
18. MC Control Constant-alpha-QFV1nI9Zpoo.mp4
10. MC Control Incremental Mean-E2RITH-2NUE.mp4
13. MC Control Policy Improvement-2RKH-BInX7s.mp4
06. MC Prediction Action Values-08tLtbh0xLs.mp4
03. MC Prediction State Values-0q2wSWyuBj8.mp4
Part 10-Module 02-Lesson 07_Case Studies in Algorithms
01. Case Study Introduction-r8uEDyBylHY.zh-CN.vtt
07. Traveling Salesman Problem-9ruR5Ux63QU.zh-CN.vtt
01. Case Study Introduction-r8uEDyBylHY.pt-BR.vtt
07. Traveling Salesman Problem-9ruR5Ux63QU.en.vtt
07. Traveling Salesman Problem-9ruR5Ux63QU.en-US.vtt
07. Traveling Salesman Problem-9ruR5Ux63QU.pt-BR.vtt
01. Case Study Introduction-r8uEDyBylHY.en.vtt
01. Case Study Introduction-r8uEDyBylHY.en-US.vtt
02. Shortest Path Problem-huKUM97Vve8.zh-CN.vtt
02. Shortest Path Problem-huKUM97Vve8.pt-BR.vtt
02. Shortest Path Problem-huKUM97Vve8.en.vtt
02. Shortest Path Problem-huKUM97Vve8.en-US.vtt
04. Knapsack Problem--xRKazHGtjU.zh-CN.vtt
04. Knapsack Problem--xRKazHGtjU.pt-BR.vtt
04. Knapsack Problem--xRKazHGtjU.en.vtt
04. Knapsack Problem--xRKazHGtjU.en-US.vtt
06. Dynamic Programming-VQeFcG9pjJU.zh-CN.vtt
03. Dijkstra's Algorithm-SoPMK03cOgk.zh-CN.vtt
05. A Faster Algorithm-J7S3CHFBZJA.zh-CN.vtt
08. Exact and Approximate Algorithms-3A8YqOYlAwQ.zh-CN.vtt
06. Dynamic Programming-VQeFcG9pjJU.pt-BR.vtt
03. Dijkstra's Algorithm-SoPMK03cOgk.pt-BR.vtt
03. Dijkstra's Algorithm-SoPMK03cOgk.en.vtt
03. Dijkstra's Algorithm-SoPMK03cOgk.en-US.vtt
06. Dynamic Programming-VQeFcG9pjJU.en.vtt
06. Dynamic Programming-VQeFcG9pjJU.en-US.vtt
05. A Faster Algorithm-J7S3CHFBZJA.pt-BR.vtt
05. A Faster Algorithm-J7S3CHFBZJA.en.vtt
05. A Faster Algorithm-J7S3CHFBZJA.en-US.vtt
08. Exact and Approximate Algorithms-3A8YqOYlAwQ.en.vtt
08. Exact and Approximate Algorithms-3A8YqOYlAwQ.en-US.vtt
08. Exact and Approximate Algorithms-3A8YqOYlAwQ.pt-BR.vtt
index.html
04. Knapsack Problem.html
05. A Faster Algorithm.html
06. Dynamic Programming.html
02. Shortest Path Problem.html
01. Case Study Introduction.html
03. Dijkstra's Algorithm.html
07. Traveling Salesman Problem.html
08. Exact and Approximate Algorithms.html
02. Shortest Path Problem-huKUM97Vve8.mp4
04. Knapsack Problem--xRKazHGtjU.mp4
03. Dijkstra's Algorithm-SoPMK03cOgk.mp4
01. Case Study Introduction-r8uEDyBylHY.mp4
07. Traveling Salesman Problem-9ruR5Ux63QU.mp4
06. Dynamic Programming-VQeFcG9pjJU.mp4
08. Exact and Approximate Algorithms-3A8YqOYlAwQ.mp4
05. A Faster Algorithm-J7S3CHFBZJA.mp4
Part 06-Module 01-Lesson 04_Dynamic Programming
01. Introduction-ek2PD9RDrWw.zh-CN.vtt
01. Introduction-ek2PD9RDrWw.en.vtt
01. Introduction-ek2PD9RDrWw.pt-BR.vtt
04. Another Gridworld Example-n9SbomnLb-U.zh-CN.vtt
17. Policy Iteration-gqv7o1kBDc0.zh-CN.vtt
04. Another Gridworld Example-n9SbomnLb-U.en.vtt
17. Policy Iteration-gqv7o1kBDc0.en.vtt
04. Another Gridworld Example-n9SbomnLb-U.pt-BR.vtt
17. Policy Iteration-gqv7o1kBDc0.pt-BR.vtt
20. Truncated Policy Iteration-a-RvCxlPMho.zh-CN.vtt
20. Truncated Policy Iteration-a-RvCxlPMho.en.vtt
23. Value Iteration-XNeQn8N36y8.zh-CN.vtt
20. Truncated Policy Iteration-a-RvCxlPMho.pt-BR.vtt
23. Value Iteration-XNeQn8N36y8.en.vtt
23. Value Iteration-XNeQn8N36y8.pt-BR.vtt
index.html
08. Iterative Policy Evaluation-eDXIL_oOJHI.zh-CN.vtt
05. An Iterative Method-AX-hG3KvwzY.zh-CN.vtt
14. Policy Improvement-4_adUEK0IHg.zh-CN.vtt
08. Iterative Policy Evaluation-eDXIL_oOJHI.en.vtt
23. Value Iteration.html
17. Policy Iteration.html
14. Policy Improvement.html
05. An Iterative Method, Part 1.html
20. Truncated Policy Iteration.html
08. Iterative Policy Evaluation-eDXIL_oOJHI.pt-BR.vtt
08. Iterative Policy Evaluation.html
05. An Iterative Method-AX-hG3KvwzY.en.vtt
22. Mini Project DP (Part 5).html
16. Mini Project DP (Part 3).html
19. Mini Project DP (Part 4).html
25. Mini Project DP (Part 6).html
13. Mini Project DP (Part 2).html
10. Mini Project DP (Parts 0 and 1).html
04. Another Gridworld Example.html
01. Introduction.html
14. Policy Improvement-4_adUEK0IHg.en.vtt
05. An Iterative Method-AX-hG3KvwzY.pt-BR.vtt
18. Implementation.html
14. Policy Improvement-4_adUEK0IHg.pt-BR.vtt
12. Implementation.html
15. Implementation.html
21. Implementation.html
03. Your Workspace.html
24. Implementation.html
02. OpenAI Gym FrozenLakeEnv.html
26. Check Your Understanding.html
07. Quiz An Iterative Method.html
11. Action Values.html
09. Implementation.html
img
screen-shot-2017-10-02-at-10.41.44-am.png
improve.png
est-action.png
screen-shot-2017-12-17-at-9.41.03-am.png
screen-shot-2017-09-26-at-4.22.09-pm.png
truncated-eval.png
iteration.png
policy-eval.png
screen-shot-2017-09-26-at-11.03.16-pm.png
truncated-iter.png
value-iteration.png
screen-shot-2017-09-26-at-2.18.38-pm.png
actionvalue.png
statevalue.png
frozen-lake-6.jpg
06. An Iterative Method, Part 2.html
27. Summary.html
04. Another Gridworld Example-n9SbomnLb-U.mp4
01. Introduction-ek2PD9RDrWw.mp4
17. Policy Iteration-gqv7o1kBDc0.mp4
20. Truncated Policy Iteration-a-RvCxlPMho.mp4
23. Value Iteration-XNeQn8N36y8.mp4
08. Iterative Policy Evaluation-eDXIL_oOJHI.mp4
05. An Iterative Method-AX-hG3KvwzY.mp4
14. Policy Improvement-4_adUEK0IHg.mp4
Part 06-Module 02-Lesson 04_Actor-Critic Methods
06. RL M2L4 06 Actor Critic With Advantage RENDER V1 V1-Bwd2OF7hJXQ.zh-CN.vtt
06. RL M2L4 06 Actor Critic With Advantage RENDER V1 V1-Bwd2OF7hJXQ.en.vtt
06. RL M2L4 06 Actor Critic With Advantage RENDER V1 V1-Bwd2OF7hJXQ.pt-BR.vtt
01. RL M2L4 01 Actor Critic Methods RENDER V1 V1-FXhyxJzgt8U.zh-CN.vtt
03. RL M2L4 03 Two Function Approximators V1-37KQEgLaLfw.zh-CN.vtt
01. RL M2L4 01 Actor Critic Methods RENDER V1 V1-FXhyxJzgt8U.en.vtt
03. RL M2L4 03 Two Function Approximators V1-37KQEgLaLfw.en.vtt
01. RL M2L4 01 Actor Critic Methods RENDER V1 V1-FXhyxJzgt8U.pt-BR.vtt
03. RL M2L4 03 Two Function Approximators V1-37KQEgLaLfw.pt-BR.vtt
07. Summary-hvYQ_3LgCYs.zh-CN.vtt
07. Summary-hvYQ_3LgCYs.en.vtt
02. RL M2L4 02 A Better Score Function V2-_HBJ3l10-OE.zh-CN.vtt
04. RL M2L4 04 The Actor And The Critic V1-bvbE9F7urd4.zh-CN.vtt
02. RL M2L4 02 A Better Score Function V2-_HBJ3l10-OE.en.vtt
04. RL M2L4 04 The Actor And The Critic V1-bvbE9F7urd4.en.vtt
07. Summary-hvYQ_3LgCYs.pt-BR.vtt
04. RL M2L4 04 The Actor And The Critic V1-bvbE9F7urd4.pt-BR.vtt
02. RL M2L4 02 A Better Score Function V2-_HBJ3l10-OE.pt-BR.vtt
05. RL M2L4 05 Advantage Function RENDER V1 V2-vpLmzKqcgfc.zh-CN.vtt
05. RL M2L4 05 Advantage Function RENDER V1 V2-vpLmzKqcgfc.en.vtt
index.html
05. RL M2L4 05 Advantage Function RENDER V1 V2-vpLmzKqcgfc.pt-BR.vtt
07. Summary.html
02. A Better Score Function.html
04. The Actor and The Critic.html
05. Advantage Function.html
03. Two Function Approximators.html
01. Actor-Critic Methods.html
06. Actor-Critic with Advantage.html
06. RL M2L4 06 Actor Critic With Advantage RENDER V1 V1-Bwd2OF7hJXQ.mp4
03. RL M2L4 03 Two Function Approximators V1-37KQEgLaLfw.mp4
02. RL M2L4 02 A Better Score Function V2-_HBJ3l10-OE.mp4
04. RL M2L4 04 The Actor And The Critic V1-bvbE9F7urd4.mp4
01. RL M2L4 01 Actor Critic Methods RENDER V1 V1-FXhyxJzgt8U.mp4
05. RL M2L4 05 Advantage Function RENDER V1 V2-vpLmzKqcgfc.mp4
07. Summary-hvYQ_3LgCYs.mp4
Part 10-Module 02-Lesson 08_Technical Interview - Python
06. Runtime Analysis-8bI9OgOB2qI.zh-CN.vtt
03. Confirming Inputs-8lPTOG1yLsg.pt-BR.vtt
06. Runtime Analysis-8bI9OgOB2qI.pt-BR.vtt
10. Interview Wrap-Up-sz4Ekcu9a_Q.pt-BR.vtt
03. Confirming Inputs-8lPTOG1yLsg.zh-CN.vtt
06. Runtime Analysis-8bI9OgOB2qI.en.vtt
06. Runtime Analysis-8bI9OgOB2qI.en-US.vtt
10. Interview Wrap-Up-sz4Ekcu9a_Q.zh-CN.vtt
04. Test Cases-7CNatJ7PqZ4.pt-BR.vtt
03. Confirming Inputs-8lPTOG1yLsg.en.vtt
03. Confirming Inputs-8lPTOG1yLsg.en-US.vtt
10. Interview Wrap-Up-sz4Ekcu9a_Q.en.vtt
10. Interview Wrap-Up-sz4Ekcu9a_Q.en-US.vtt
04. Test Cases-7CNatJ7PqZ4.zh-CN.vtt
04. Test Cases-7CNatJ7PqZ4.en.vtt
04. Test Cases-7CNatJ7PqZ4.en-US.vtt
01. Interview Introduction-dRsHYt1Lddc.pt-BR.vtt
02. Clarifying the Question-XvvKBmKC_84.pt-BR.vtt
02. Clarifying the Question-XvvKBmKC_84.zh-CN.vtt
01. Interview Introduction-dRsHYt1Lddc.zh-CN.vtt
01. Interview Introduction-dRsHYt1Lddc.en.vtt
01. Interview Introduction-dRsHYt1Lddc.en-US.vtt
02. Clarifying the Question-XvvKBmKC_84.en.vtt
02. Clarifying the Question-XvvKBmKC_84.en-US.vtt
09. Debugging-Bz1tlvkql9Q.zh-CN.vtt
09. Debugging-Bz1tlvkql9Q.pt-BR.vtt
09. Debugging-Bz1tlvkql9Q.en.vtt
09. Debugging-Bz1tlvkql9Q.en-US.vtt
05. Brainstorming-LJFYhMDCCsU.pt-BR.vtt
05. Brainstorming-LJFYhMDCCsU.zh-CN.vtt
index.html
05. Brainstorming-LJFYhMDCCsU.en.vtt
05. Brainstorming-LJFYhMDCCsU.en-US.vtt
08. Coding 2.html
07. Coding.html
09. Debugging.html
04. Test Cases.html
05. Brainstorming.html
06. Runtime Analysis.html
03. Confirming Inputs.html
01. Interview Introduction.html
02. Clarifying the Question.html
10. Interview Wrap-Up.html
07. Coding-zhQYREUI8Z0.zh-CN.vtt
07. Coding-zhQYREUI8Z0.pt-BR.vtt
13. Resources in Your Career Portal.html
08. Coding 2-qEteyPNRSwU.en.vtt
11. Time for Live Practice with Pramp.html
Project Description - Technical Interview Practice.html
Project Rubric - Technical Interview Practice.html
12. Next Steps.html
14. Project Description.html
07. Coding-zhQYREUI8Z0.en.vtt
07. Coding-zhQYREUI8Z0.en-US.vtt
img
career-portal-sidebar.png
screen-shot-2017-10-31-at-1.06.42-pm.png
10. Interview Wrap-Up-sz4Ekcu9a_Q.mp4
01. Interview Introduction-dRsHYt1Lddc.mp4
06. Runtime Analysis-8bI9OgOB2qI.mp4
03. Confirming Inputs-8lPTOG1yLsg.mp4
04. Test Cases-7CNatJ7PqZ4.mp4
02. Clarifying the Question-XvvKBmKC_84.mp4
09. Debugging-Bz1tlvkql9Q.mp4
05. Brainstorming-LJFYhMDCCsU.mp4
08. Coding 2-qEteyPNRSwU.mp4
07. Coding-zhQYREUI8Z0.mp4
Part 10-Module 01-Lesson 03_Interview Fails
01. Interview Fails-FD6UNqMa0xc.zh-CN.vtt
01. Interview Fails-FD6UNqMa0xc.es-MX.vtt
01. Interview Fails-FD6UNqMa0xc.en.vtt
01. Interview Fails-FD6UNqMa0xc.pt-BR.vtt
02. Interviewing Fails Mike Wales-OGXRmzBglI4.zh-CN.vtt
02. Interviewing Fails Mike Wales-OGXRmzBglI4.en.vtt
02. Interviewing Fails Mike Wales-OGXRmzBglI4.es-MX.vtt
02. Interviewing Fails Mike Wales-OGXRmzBglI4.pt-BR.vtt
03. Interviewing Fails Siya Raj Purohit-wYop-N5YgeA.zh-CN.vtt
03. Interviewing Fails Siya Raj Purohit-wYop-N5YgeA.es-MX.vtt
03. Interviewing Fails Siya Raj Purohit-wYop-N5YgeA.en.vtt
03. Interviewing Fails Siya Raj Purohit-wYop-N5YgeA.pt-BR.vtt
index.html
01. Interview Fails.html
02. Interviewing Fails Mike Wales.html
04. Interviewing Fails Lyla Fujiwara.html
03. Interviewing Fails Siya Raj Purohit.html
04. Interviewing Fails Lyla Fujiwara-CgK2HxdJzc8.zh-CN.vtt
04. Interviewing Fails Lyla Fujiwara-CgK2HxdJzc8.pt-BR.vtt
04. Interviewing Fails Lyla Fujiwara-CgK2HxdJzc8.es-MX.vtt
04. Interviewing Fails Lyla Fujiwara-CgK2HxdJzc8.en.vtt
01. Interview Fails-FD6UNqMa0xc.mp4
02. Interviewing Fails Mike Wales-OGXRmzBglI4.mp4
03. Interviewing Fails Siya Raj Purohit-wYop-N5YgeA.mp4
04. Interviewing Fails Lyla Fujiwara-CgK2HxdJzc8.mp4
Part 04-Module 06-Lesson 01_Random Projection and ICA
07. L6 5 ICA Implementation V1 V1-fZGxYfJmKaE.en.vtt
07. L6 5 ICA Implementation V1 V1-fZGxYfJmKaE.pt-BR.vtt
03. L6 2 Random Projection Impl MAINv1 V1 V1-5DhvurLgRII.en.vtt
03. L6 2 Random Projection Impl MAINv1 V1 V1-5DhvurLgRII.pt-BR.vtt
04. L6 3 ICA V1 V1-ae94x-1JDzg.pt-BR.vtt
04. L6 3 ICA V1 V1-ae94x-1JDzg.en.vtt
10. L6 6 ICA Applications MAIN V1 V1 V1-th12mTv1B7g.pt-BR.vtt
10. L6 6 ICA Applications MAIN V1 V1 V1-th12mTv1B7g.en.vtt
index.html
07. ICA in sklearn.html
03. Random Projection in sklearn.html
05. FastICA Algorithm.html
08. [Lab] Independent Component Analysis.html
09. [Solution] Independent Component Analysis.html
04. Independent Component Analysis (ICA).html
10. ICA Applications.html
01. Random Projection.html
05. L6 4 ICA Algorithm V2 V1-xlhd5UWk_-E.en.vtt
06. ICA.html
05. L6 4 ICA Algorithm V2 V1-xlhd5UWk_-E.pt-BR.vtt
02. Random Projection.html
01. L6 1 Random Projection MAIN V1 V1 V1-Iat1a8mzI-Y.en.vtt
01. L6 1 Random Projection MAIN V1 V1 V1-Iat1a8mzI-Y.pt-BR.vtt
img
eeg-ica.png
07. L6 5 ICA Implementation V1 V1-fZGxYfJmKaE.mp4
03. L6 2 Random Projection Impl MAINv1 V1 V1-5DhvurLgRII.mp4
04. L6 3 ICA V1 V1-ae94x-1JDzg.mp4
05. L6 4 ICA Algorithm V2 V1-xlhd5UWk_-E.mp4
01. L6 1 Random Projection MAIN V1 V1 V1-Iat1a8mzI-Y.mp4
10. L6 6 ICA Applications MAIN V1 V1 V1-th12mTv1B7g.mp4
Part 10-Module 02-Lesson 06_Graphs
09. Graph Traversal-Dkt-XxHZaZE.zh-CN.vtt
09. Graph Traversal-Dkt-XxHZaZE.pt-BR.vtt
01. Graph Introduction-DFR8F2Q9lgo.pt-BR.vtt
09. Graph Traversal-Dkt-XxHZaZE.en.vtt
09. Graph Traversal-Dkt-XxHZaZE.en-US.vtt
01. Graph Introduction-DFR8F2Q9lgo.zh-CN.vtt
01. Graph Introduction-DFR8F2Q9lgo.en.vtt
01. Graph Introduction-DFR8F2Q9lgo.en-US.vtt
04. Connectivity-4x6u2KtNDg4.zh-CN.vtt
04. Connectivity-4x6u2KtNDg4.pt-BR.vtt
04. Connectivity-4x6u2KtNDg4.en.vtt
04. Connectivity-4x6u2KtNDg4.en-US.vtt
07. Adjacency Matrices-FsFhoTALA1c.zh-CN.vtt
11. BFS-pol4kGNlvJA.zh-CN.vtt
11. BFS-pol4kGNlvJA.pt-BR.vtt
06. Graph Representations-uw9u6dtl0WA.zh-CN.vtt
11. BFS-pol4kGNlvJA.en-US.vtt
11. BFS-pol4kGNlvJA.en.vtt
03. Directions and Cycles-lF0vUktQDPo.zh-CN.vtt
07. Adjacency Matrices-FsFhoTALA1c.en.vtt
07. Adjacency Matrices-FsFhoTALA1c.en-US.vtt
07. Adjacency Matrices-FsFhoTALA1c.pt-BR.vtt
06. Graph Representations-uw9u6dtl0WA.en.vtt
06. Graph Representations-uw9u6dtl0WA.en-US.vtt
06. Graph Representations-uw9u6dtl0WA.pt-BR.vtt
03. Directions and Cycles-lF0vUktQDPo.pt-BR.vtt
03. Directions and Cycles-lF0vUktQDPo.en.vtt
03. Directions and Cycles-lF0vUktQDPo.en-US.vtt
10. DFS-BC8jEidd2EQ.zh-CN.vtt
13. Eulerian Path-zS34kHSo7fs.zh-CN.vtt
10. DFS-BC8jEidd2EQ.pt-BR.vtt
10. DFS-BC8jEidd2EQ.en.vtt
10. DFS-BC8jEidd2EQ.en-US.vtt
13. Eulerian Path-zS34kHSo7fs.en.vtt
13. Eulerian Path-zS34kHSo7fs.en-US.vtt
13. Eulerian Path-zS34kHSo7fs.pt-BR.vtt
02. What Is a Graph-p-_DFOyEMV8.zh-CN.vtt
02. What Is a Graph-p-_DFOyEMV8.pt-BR.vtt
02. What Is a Graph-p-_DFOyEMV8.en.vtt
02. What Is a Graph-p-_DFOyEMV8.en-US.vtt
index.html
10. DFS.html
11. BFS.html
04. Connectivity.html
13. Eulerian Path.html
09. Graph Traversal.html
02. What Is a Graph.html
07. Adjacency Matrices.html
01. Graph Introduction.html
03. Directions and Cycles.html
06. Graph Representations.html
05. Graph Practice.html
08. Graph Representation Practice.html
12. Graph Traversal Practice.html
media
unnamed-69567-0.gif
5gl2J73khhHQAERWImk7Y-GBP8onqRMMF5wIztkfj_8l8iT70qfBNIgUuaqS6Zoz1qUreJZA6PIMadm5ACc=s0#w=1920&h=1080
img
7919804788.gif
04. Connectivity-4x6u2KtNDg4.mp4
13. Eulerian Path-zS34kHSo7fs.mp4
03. Directions and Cycles-lF0vUktQDPo.mp4
11. BFS-pol4kGNlvJA.mp4
10. DFS-BC8jEidd2EQ.mp4
07. Adjacency Matrices-FsFhoTALA1c.mp4
09. Graph Traversal-Dkt-XxHZaZE.mp4
02. What Is a Graph-p-_DFOyEMV8.mp4
01. Graph Introduction-DFR8F2Q9lgo.mp4
06. Graph Representations-uw9u6dtl0WA.mp4
Part 10-Module 02-Lesson 04_Maps and Hashing
01. Introduction to Maps-JEw3iQAnGKQ.zh-CN.vtt
01. Introduction to Maps-JEw3iQAnGKQ.pt-BR.vtt
01. Introduction to Maps-JEw3iQAnGKQ.en.vtt
01. Introduction to Maps-JEw3iQAnGKQ.en-US.vtt
08. Hash Maps-A-ahUVi8pYQ.zh-CN.vtt
08. Hash Maps-A-ahUVi8pYQ.pt-BR.vtt
08. Hash Maps-A-ahUVi8pYQ.en.vtt
08. Hash Maps-A-ahUVi8pYQ.en-US.vtt
04. Introduction to Hashing-8yik3RlDFgM.zh-CN.vtt
04. Introduction to Hashing-8yik3RlDFgM.pt-BR.vtt
04. Introduction to Hashing-8yik3RlDFgM.en.vtt
04. Introduction to Hashing-8yik3RlDFgM.en-US.vtt
02. Sets and Maps-gmIb-qZhTDQ.zh-CN.vtt
02. Sets and Maps-gmIb-qZhTDQ.pt-BR.vtt
02. Sets and Maps-gmIb-qZhTDQ.en.vtt
02. Sets and Maps-gmIb-qZhTDQ.en-US.vtt
09. String Keys-WyFwieF1NN4.zh-CN.vtt
05. Hashing-kCPFfHx_LgQ.zh-CN.vtt
09. String Keys-WyFwieF1NN4.pt-BR.vtt
09. String Keys-WyFwieF1NN4.en.vtt
09. String Keys-WyFwieF1NN4.en-US.vtt
05. Hashing-kCPFfHx_LgQ.pt-BR.vtt
05. Hashing-kCPFfHx_LgQ.en.vtt
05. Hashing-kCPFfHx_LgQ.en-US.vtt
06. Collisions-BUaWIjZ_ToY.zh-CN.vtt
06. Collisions-BUaWIjZ_ToY.pt-BR.vtt
index.html
06. Collisions-BUaWIjZ_ToY.en.vtt
06. Collisions-BUaWIjZ_ToY.en-US.vtt
05. Hashing.html
06. Collisions.html
02. Sets and Maps.html
01. Introduction to Maps.html
04. Introduction to Hashing.html
08. Hash Maps.html
09. String Keys.html
07. Load Factor.html
10. String Keys Practice.html
03. Python Dictionaries.html
img
7905614952.gif
08. Hash Maps-A-ahUVi8pYQ.mp4
02. Sets and Maps-gmIb-qZhTDQ.mp4
05. Hashing-kCPFfHx_LgQ.mp4
09. String Keys-WyFwieF1NN4.mp4
06. Collisions-BUaWIjZ_ToY.mp4
01. Introduction to Maps-JEw3iQAnGKQ.mp4
04. Introduction to Hashing-8yik3RlDFgM.mp4
Part 06-Module 01-Lesson 06_Temporal-Difference Methods
13. TD Control Expected Sarsa-kEKupCyU0P0.zh-CN.vtt
13. TD Control Expected Sarsa-kEKupCyU0P0.en.vtt
07. TD Control Sarsa(0)-LkFkjfsRpXc.zh-CN.vtt
07. TD Control Sarsa(0)-LkFkjfsRpXc.en.vtt
06. TD Prediction Action Values-1c029-7_9GA.zh-CN.vtt
01. Introduction-yXErXQulI_o.zh-CN.vtt
06. TD Prediction Action Values-1c029-7_9GA.en.vtt
01. Introduction-yXErXQulI_o.en.vtt
10. TD Control Sarsamax-4DxoYuR7aZ4.zh-CN.vtt
10. TD Control Sarsamax-4DxoYuR7aZ4.en.vtt
index.html
07. TD Control Sarsa(0).html
03. TD Prediction TD(0).html
01. Introduction.html
13. TD Control Expected Sarsa.html
10. TD Control Sarsamax.html
06. TD Prediction Action Values.html
15. Mini Project TD (Part 4).html
12. Mini Project TD (Part 3).html
09. Mini Project TD (Part 2).html
05. Mini Project TD (Parts 0 and 1).html
11. Implementation.html
03. TD Prediction TD(0)-CsD6b0csU7o.zh-CN.vtt
14. Implementation.html
04. Implementation.html
02. OpenAI Gym CliffWalkingEnv.html
16. Analyzing Performance.html
08. Implementation.html
03. TD Prediction TD(0)-CsD6b0csU7o.en.vtt
17. Summary.html
img
screen-shot-2017-10-17-at-11.02.44-am.png
matengai-of-kuniga-coast-in-oki-island-shimane-pref600.jpg
expected-sarsa.png
sarsamax.png
sarsa.png
td-prediction.png
screen-shot-2017-12-17-at-12.49.34-pm.png
13. TD Control Expected Sarsa-kEKupCyU0P0.mp4
07. TD Control Sarsa(0)-LkFkjfsRpXc.mp4
06. TD Prediction Action Values-1c029-7_9GA.mp4
10. TD Control Sarsamax-4DxoYuR7aZ4.mp4
01. Introduction-yXErXQulI_o.mp4
03. TD Prediction TD(0)-CsD6b0csU7o.mp4
Part 10-Module 02-Lesson 02_List-Based Collections
08. Stacks-DQoCO8aGcNc.zh-CN.vtt
08. Stacks-DQoCO8aGcNc.pt-BR.vtt
08. Stacks-DQoCO8aGcNc.en.vtt
08. Stacks-DQoCO8aGcNc.en-US.vtt
01. Welcome to Collections-cZORvZq-tI0.zh-CN.vtt
02. Lists-KUQSgUMtyv0.zh-CN.vtt
01. Welcome to Collections-cZORvZq-tI0.en.vtt
01. Welcome to Collections-cZORvZq-tI0.en-US.vtt
02. Lists-KUQSgUMtyv0.en.vtt
02. Lists-KUQSgUMtyv0.en-US.vtt
01. Welcome to Collections-cZORvZq-tI0.pt-BR.vtt
02. Lists-KUQSgUMtyv0.pt-BR.vtt
05. Linked Lists-zxkpZrozDUk.zh-CN.vtt
09. Stacks Details-HpaVHzDeZC4.zh-CN.vtt
09. Stacks Details-HpaVHzDeZC4.pt-BR.vtt
05. Linked Lists-zxkpZrozDUk.en.vtt
05. Linked Lists-zxkpZrozDUk.en-US.vtt
05. Linked Lists-zxkpZrozDUk.pt-BR.vtt
09. Stacks Details-HpaVHzDeZC4.en.vtt
09. Stacks Details-HpaVHzDeZC4.en-US.vtt
11. Queues-XAbzlilAHZw.zh-CN.vtt
11. Queues-XAbzlilAHZw.pt-BR.vtt
11. Queues-XAbzlilAHZw.en.vtt
11. Queues-XAbzlilAHZw.en-US.vtt
06. Linked Lists in Depth-ZONGA5wmREI.zh-CN.vtt
03. Arrays-OnPP5xDmFv0.zh-CN.vtt
06. Linked Lists in Depth-ZONGA5wmREI.pt-BR.vtt
03. Arrays-OnPP5xDmFv0.en.vtt
03. Arrays-OnPP5xDmFv0.en-US.vtt
06. Linked Lists in Depth-ZONGA5wmREI.en.vtt
06. Linked Lists in Depth-ZONGA5wmREI.en-US.vtt
index.html
03. Arrays-OnPP5xDmFv0.pt-BR.vtt
02. Lists.html
03. Arrays.html
08. Stacks.html
11. Queues.html
05. Linked Lists.html
09. Stacks Details.html
06. Linked Lists in Depth.html
01. Welcome to Collections.html
12. Queue Practice.html
04. Python Lists.html
10. Stack Practice.html
07. Linked List Practice.html
img
7890272657.gif
02. Lists-KUQSgUMtyv0.mp4
06. Linked Lists in Depth-ZONGA5wmREI.mp4
03. Arrays-OnPP5xDmFv0.mp4
08. Stacks-DQoCO8aGcNc.mp4
01. Welcome to Collections-cZORvZq-tI0.mp4
05. Linked Lists-zxkpZrozDUk.mp4
09. Stacks Details-HpaVHzDeZC4.mp4
11. Queues-XAbzlilAHZw.mp4
Part 10-Module 01-Lesson 04_Land a Job Offer
01. Land a Job Offer-ZQJoT8QL_hw.zh-CN.vtt
01. Land a Job Offer-ZQJoT8QL_hw.es-MX.vtt
01. Land a Job Offer-ZQJoT8QL_hw.pt-BR.vtt
01. Land a Job Offer-ZQJoT8QL_hw.en.vtt
index.html
01. Land a Job Offer.html
01. Land a Job Offer-ZQJoT8QL_hw.mp4
Part 06-Module 02-Lesson 02_Deep Q-Learning
13. Wrap Up-x6JggcDTcys.zh-CN.vtt
13. Wrap Up-x6JggcDTcys.en.vtt
01. Intro to Deep Q-Learning-o3cmuUDhP3I.zh-CN.vtt
01. Intro to Deep Q-Learning-o3cmuUDhP3I.en.vtt
13. Wrap Up-x6JggcDTcys.pt-BR.vtt
01. Intro to Deep Q-Learning-o3cmuUDhP3I.pt-BR.vtt
03. Monte Carlo Learning-qOviWYwcvsg.zh-CN.vtt
03. Monte Carlo Learning-qOviWYwcvsg.en.vtt
03. Monte Carlo Learning-qOviWYwcvsg.pt-BR.vtt
02. Neural Nets as Value Functions-cBi7vLrk8QQ.zh-CN.vtt
04. Temporal Difference Learning-lpmDi0QeUm8.zh-CN.vtt
02. Neural Nets as Value Functions-cBi7vLrk8QQ.en.vtt
09. Deep Q-Learning Algorithm-MqTXoCxQ_eY.zh-CN.vtt
02. Neural Nets as Value Functions-cBi7vLrk8QQ.pt-BR.vtt
04. Temporal Difference Learning-lpmDi0QeUm8.en.vtt
index.html
04. Temporal Difference Learning-lpmDi0QeUm8.pt-BR.vtt
09. Deep Q-Learning Algorithm-MqTXoCxQ_eY.en.vtt
05. Q-Learning-AI5gLgYMSq8.zh-CN.vtt
08. Fixed Q Targets-SWpyiEezfp4.zh-CN.vtt
06. Deep Q Network-GgtR_d1OB-M.zh-CN.vtt
05. Q-Learning-AI5gLgYMSq8.en.vtt
09. Deep Q-Learning Algorithm-MqTXoCxQ_eY.pt-BR.vtt
08. Fixed Q Targets-SWpyiEezfp4.en.vtt
05. Q-Learning-AI5gLgYMSq8.pt-BR.vtt
13. Wrap Up.html
06. Deep Q Network-GgtR_d1OB-M.en.vtt
08. Fixed Q Targets-SWpyiEezfp4.pt-BR.vtt
01. Intro to Deep Q-Learning.html
04. Temporal Difference Learning.html
02. Neural Nets as Value Functions.html
08. Fixed Q Targets.html
05. Q-Learning.html
03. Monte Carlo Learning.html
06. Deep Q Network.html
07. Experience Replay.html
06. Deep Q Network-GgtR_d1OB-M.pt-BR.vtt
12. TensorFlow Implementation.html
09. Deep Q-Learning Algorithm.html
10. DQN Improvements.html
11. Implementing Deep Q-Learning.html
07. Experience Replay-wX_-SZG-YMQ.zh-CN.vtt
07. Experience Replay-wX_-SZG-YMQ.en.vtt
10. DQN Improvements-Zfdbp93A2GU.zh-CN.vtt
07. Experience Replay-wX_-SZG-YMQ.pt-BR.vtt
10. DQN Improvements-Zfdbp93A2GU.en.vtt
10. DQN Improvements-Zfdbp93A2GU.pt-BR.vtt
img
enable-gpu.png
atari-network.png
13. Wrap Up-x6JggcDTcys.mp4
01. Intro to Deep Q-Learning-o3cmuUDhP3I.mp4
03. Monte Carlo Learning-qOviWYwcvsg.mp4
02. Neural Nets as Value Functions-cBi7vLrk8QQ.mp4
04. Temporal Difference Learning-lpmDi0QeUm8.mp4
05. Q-Learning-AI5gLgYMSq8.mp4
09. Deep Q-Learning Algorithm-MqTXoCxQ_eY.mp4
08. Fixed Q Targets-SWpyiEezfp4.mp4
06. Deep Q Network-GgtR_d1OB-M.mp4
10. DQN Improvements-Zfdbp93A2GU.mp4
07. Experience Replay-wX_-SZG-YMQ.mp4
Part 06-Module 01-Lesson 03_The RL Framework The Solution
01. Introduction-9Wyf5Zsska8.zh-CN.vtt
01. Introduction-9Wyf5Zsska8.en.vtt
01. Introduction-9Wyf5Zsska8.pt-BR.vtt
04. Gridworld Example-XeHBmPFqTsE.zh-CN.vtt
04. Gridworld Example-XeHBmPFqTsE.en.vtt
04. Gridworld Example-XeHBmPFqTsE.pt-BR.vtt
06. Bellman Equations-UgIaDMvSdUo.zh-CN.vtt
11. Optimal Policies-2rguYpVyCto.zh-CN.vtt
06. Bellman Equations-UgIaDMvSdUo.en.vtt
08. Optimality-j231aRV74QM.zh-CN.vtt
06. Bellman Equations-UgIaDMvSdUo.pt-BR.vtt
05. State-Value Functions-llakAjwox_8.zh-CN.vtt
11. Optimal Policies-2rguYpVyCto.en.vtt
09. Action-Value Functions-KJLaRfOOPGA.zh-CN.vtt
02. Policies-hc3LrvaC13U.zh-CN.vtt
11. Optimal Policies-2rguYpVyCto.pt-BR.vtt
index.html
08. Optimality-j231aRV74QM.en.vtt
09. Action-Value Functions-KJLaRfOOPGA.en.vtt
05. State-Value Functions-llakAjwox_8.en.vtt
02. Policies-hc3LrvaC13U.en.vtt
08. Optimality-j231aRV74QM.pt-BR.vtt
05. State-Value Functions-llakAjwox_8.pt-BR.vtt
09. Action-Value Functions-KJLaRfOOPGA.pt-BR.vtt
02. Policies-hc3LrvaC13U.pt-BR.vtt
02. Policies.html
08. Optimality.html
11. Optimal Policies.html
04. Gridworld Example.html
01. Introduction.html
09. Action-Value Functions.html
10. Quiz Action-Value Functions.html
05. State-Value Functions.html
06. Bellman Equations.html
13. Summary.html
03. Quiz Interpret the Policy.html
07. Quiz State-Value Functions.html
12. Quiz Optimal Policies.html
img
screen-shot-2017-09-25-at-11.35.38-am.png
screen-shot-2017-09-25-at-9.18.00-pm.png
screen-shot-2017-09-25-at-5.51.40-pm.png
screen-shot-2017-09-25-at-6.02.37-pm.png
screen-shot-2017-09-21-at-12.20.30-pm.png
screen-shot-2017-08-31-at-3.27.10-pm.png
screen-shot-2017-09-24-at-4.28.04-pm.png
04. Gridworld Example-XeHBmPFqTsE.mp4
06. Bellman Equations-UgIaDMvSdUo.mp4
05. State-Value Functions-llakAjwox_8.mp4
01. Introduction-9Wyf5Zsska8.mp4
08. Optimality-j231aRV74QM.mp4
09. Action-Value Functions-KJLaRfOOPGA.mp4
11. Optimal Policies-2rguYpVyCto.mp4
02. Policies-hc3LrvaC13U.mp4
Part 06-Module 02-Lesson 01_RL in Continuous Spaces
13. Non-Linear Function Approximation-rITnmpD2mN8.zh-CN.vtt
14. Summary-MTEBk43oByU.zh-CN.vtt
13. Non-Linear Function Approximation-rITnmpD2mN8.en.vtt
14. Summary-MTEBk43oByU.en.vtt
13. Non-Linear Function Approximation-rITnmpD2mN8.pt-BR.vtt
14. Summary-MTEBk43oByU.pt-BR.vtt
12. Kernel Functions-RdkPVYyVOvU.zh-CN.vtt
09. Coarse Coding-Uu1J5KLAfTU.zh-CN.vtt
12. Kernel Functions-RdkPVYyVOvU.en.vtt
09. Coarse Coding-Uu1J5KLAfTU.en.vtt
12. Kernel Functions-RdkPVYyVOvU.pt-BR.vtt
07. Tile Coding-BRs7AnTZ_8k.zh-CN.vtt
09. Coarse Coding-Uu1J5KLAfTU.pt-BR.vtt
10. Function Approximation-UTGWVY6jEdg.zh-CN.vtt
07. Tile Coding-BRs7AnTZ_8k.en.vtt
05. Discretization-j2eZyUpy--E.zh-CN.vtt
10. Function Approximation-UTGWVY6jEdg.en.vtt
07. Tile Coding-BRs7AnTZ_8k.pt-BR.vtt
index.html
05. Discretization-j2eZyUpy--E.en.vtt
10. Function Approximation-UTGWVY6jEdg.pt-BR.vtt
05. Discretization-j2eZyUpy--E.pt-BR.vtt
01. Deep Reinforcement Learning-GPjK124RU5g.zh-CN.vtt
03. Discrete vs. Continuous Spaces-uHstLeRzaE8.zh-CN.vtt
11. Linear Function Approximation-OJ5wrB7o-pI.zh-CN.vtt
14. Summary.html
07. Tile Coding.html
09. Coarse Coding.html
05. Discretization.html
12. Kernel Functions.html
11. Linear Function Approximation.html
03. Discrete vs. Continuous Spaces.html
13. Non-Linear Function Approximation.html
08. Exercise Tile Coding.html
06. Exercise Discretization.html
01. Deep Reinforcement Learning-GPjK124RU5g.en.vtt
01. Deep Reinforcement Learning.html
03. Discrete vs. Continuous Spaces-uHstLeRzaE8.en.vtt
11. Linear Function Approximation-OJ5wrB7o-pI.en.vtt
04. Quiz Space Representations.html
10. Function Approximation.html
01. Deep Reinforcement Learning-GPjK124RU5g.pt-BR.vtt
03. Discrete vs. Continuous Spaces-uHstLeRzaE8.pt-BR.vtt
11. Linear Function Approximation-OJ5wrB7o-pI.pt-BR.vtt
02. Resources.html
img
poker-hand-3-of-a-kind.png
13. Non-Linear Function Approximation-rITnmpD2mN8.mp4
12. Kernel Functions-RdkPVYyVOvU.mp4
14. Summary-MTEBk43oByU.mp4
09. Coarse Coding-Uu1J5KLAfTU.mp4
07. Tile Coding-BRs7AnTZ_8k.mp4
05. Discretization-j2eZyUpy--E.mp4
10. Function Approximation-UTGWVY6jEdg.mp4
03. Discrete vs. Continuous Spaces-uHstLeRzaE8.mp4
11. Linear Function Approximation-OJ5wrB7o-pI.mp4
01. Deep Reinforcement Learning-GPjK124RU5g.mp4
Part 03-Module 01-Lesson 08_Supervised Learning Project
01. ML Charity Project-aVodYHcOB8U.en.vtt
01. ML Charity Project-aVodYHcOB8U.pt-BR.vtt
index.html
06. Project Workspace.html
01. Overview.html
05. Uploading to Workspace.html
03. Starting the project.html
04. Submitting the project.html
02. Software Requirements.html
Project Description - Finding Donors for CharityML.html
Project Rubric - Finding Donors for CharityML.html
img
step-2-file-upload.png
step1-file-upload.png
step-0.png
01. ML Charity Project-aVodYHcOB8U.mp4
Part 10-Module 01-Lesson 02_Practice Behavioral Questions
05. What Motivates You at the Workplace-Aa9SFwiRbho.zh-CN.vtt
05. What Motivates You at the Workplace-Aa9SFwiRbho.en.vtt
05. What Motivates You at the Workplace-Aa9SFwiRbho.pt-BR.vtt
04. Time When You Showed Initiative-29mkriaGT0E.zh-CN.vtt
04. Time When You Showed Initiative-29mkriaGT0E.en.vtt
07. What Do You Know About the Company-CcTfHemUvbM.zh-CN.vtt
04. Time When You Showed Initiative-29mkriaGT0E.pt-BR.vtt
07. What Do You Know About the Company-CcTfHemUvbM.en.vtt
08. Time When You Dealt With Failure-Qb4o_4hCuyg.zh-CN.vtt
07. What Do You Know About the Company-CcTfHemUvbM.pt-BR.vtt
08. Time When You Dealt With Failure-Qb4o_4hCuyg.pt-BR.vtt
08. Time When You Dealt With Failure-Qb4o_4hCuyg.en.vtt
index.html
06. A Problem and How You Dealt With It-7IKqdW30GvQ.zh-CN.vtt
06. A Problem and How You Dealt With It-7IKqdW30GvQ.en.vtt
06. A Problem and How You Dealt With It-7IKqdW30GvQ.pt-BR.vtt
01. Introduction.html
07. What Do You Know About the Company.html
08. Time When You Dealt With Failure.html
05. What Motivates You at the Workplace.html
04. Time When You Showed Initiative.html
03. Analyzing Behavioral Answers.html
06. A Problem and How You Dealt With It.html
02. Self-Practice Behavioral Questions.html
media
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unnamed-59153-0.gif
05. What Motivates You at the Workplace-Aa9SFwiRbho.mp4
07. What Do You Know About the Company-CcTfHemUvbM.mp4
04. Time When You Showed Initiative-29mkriaGT0E.mp4
08. Time When You Dealt With Failure-Qb4o_4hCuyg.mp4
06. A Problem and How You Dealt With It-7IKqdW30GvQ.mp4
Part 09-Module 01-Lesson 01_Develop Your Personal Brand
06. Pitching to a Recruiter-LxAdWaA-qTQ.pt-BR.vtt
06. Pitching to a Recruiter-LxAdWaA-qTQ.es-MX.vtt
06. Pitching to a Recruiter-LxAdWaA-qTQ.zh-CN.vtt
05. Elevator Pitch-0QtgTG49E9I.pt-BR.vtt
06. Pitching to a Recruiter-LxAdWaA-qTQ.en.vtt
05. Elevator Pitch-0QtgTG49E9I.zh-CN.vtt
05. Elevator Pitch-0QtgTG49E9I.es-MX.vtt
05. Elevator Pitch-0QtgTG49E9I.en.vtt
06. Pitching to a Recruiter-LxAdWaA-qTQ.ar.vtt
05. Elevator Pitch-0QtgTG49E9I.ar.vtt
07. Use Your Elevator Pitch-e-v60ieggSs.pt-BR.vtt
07. Use Your Elevator Pitch-e-v60ieggSs.es-MX.vtt
07. Use Your Elevator Pitch-e-v60ieggSs.zh-CN.vtt
07. Use Your Elevator Pitch-e-v60ieggSs.en.vtt
01. Why Network-exjEm9Paszk.pt-BR.vtt
01. Why Network-exjEm9Paszk.es-MX.vtt
01. Why Network-exjEm9Paszk.zh-CN.vtt
01. Why Network-exjEm9Paszk.en.vtt
02. Elevator Pitch-S-nAHPrkQrQ.zh-CN.vtt
02. Elevator Pitch-S-nAHPrkQrQ.pt-BR.vtt
02. Elevator Pitch-S-nAHPrkQrQ.en.vtt
02. Elevator Pitch-S-nAHPrkQrQ.es-MX.vtt
index.html
04. Meet Chris-0ccflD9x5WU.zh-CN.vtt
07. Use Your Elevator Pitch-e-v60ieggSs.ar.vtt
04. Meet Chris-0ccflD9x5WU.pt-BR.vtt
04. Meet Chris-0ccflD9x5WU.es-MX.vtt
04. Meet Chris-0ccflD9x5WU.en.vtt
02. Elevator Pitch-S-nAHPrkQrQ.ar.vtt
01. Why Network-exjEm9Paszk.ar.vtt
05. Elevator Pitch.html
02. Why Use Elevator Pitches.html
06. Pitching to a Recruiter.html
04. Meet Chris.html
01. Why Network.html
04. Meet Chris-0ccflD9x5WU.ar.vtt
08. Resources in Your Career Portal.html
07. Use Your Elevator Pitch.html
03. Personal Branding.html
img
career-portal-sidebar.png
screen-shot-2017-10-31-at-1.06.42-pm.png
06. Pitching to a Recruiter-LxAdWaA-qTQ.mp4
05. Elevator Pitch-0QtgTG49E9I.mp4
07. Use Your Elevator Pitch-e-v60ieggSs.mp4
01. Why Network-exjEm9Paszk.mp4
02. Elevator Pitch-S-nAHPrkQrQ.mp4
04. Meet Chris-0ccflD9x5WU.mp4
Part 10-Module 01-Lesson 01_Ace Your Interview
01. Introduction-pg4HUMgKLxI.es-MX.vtt
01. Introduction-pg4HUMgKLxI.pt-BR.vtt
01. Introduction-pg4HUMgKLxI.zh-CN.vtt
01. Introduction-pg4HUMgKLxI.en.vtt
02. Interviewing Conversations-klqXp09Pen4.zh-CN.vtt
02. Interviewing Conversations-klqXp09Pen4.pt-BR.vtt
02. Interviewing Conversations-klqXp09Pen4.es-MX.vtt
02. Interviewing Conversations-klqXp09Pen4.en.vtt
index.html
02. Interviews are Conversations.html
01. Introduction.html
03. STAR Method.html
01. Introduction-pg4HUMgKLxI.mp4
02. Interviewing Conversations-klqXp09Pen4.mp4
Part 08-Module 02-Lesson 02_Refine Your Career Change Resume
04. Describe Your Work Experiences-B1LED4txinI.zh-CN.vtt
04. Describe Your Work Experiences-B1LED4txinI.en.vtt
04. Describe Your Work Experiences-B1LED4txinI.es-MX.vtt
04. Describe Your Work Experiences-B1LED4txinI.pt-BR.vtt
05. Resume Reflection-8Cj_tCp8mls.es-MX.vtt
05. Resume Reflection-8Cj_tCp8mls.pt-BR.vtt
01. Convey Your Skills Concisely-xnQr3ohml9s.zh-CN.vtt
05. Resume Reflection-8Cj_tCp8mls.zh-CN.vtt
01. Convey Your Skills Concisely-xnQr3ohml9s.es-MX.vtt
01. Convey Your Skills Concisely-xnQr3ohml9s.pt-BR.vtt
05. Resume Reflection-8Cj_tCp8mls.en.vtt
02. Effective Resume Components-AiFcaHRGdEA.zh-CN.vtt
01. Convey Your Skills Concisely-xnQr3ohml9s.en.vtt
06. Resume Review-L3F2BFGYMtI.zh-CN.vtt
02. Effective Resume Components-AiFcaHRGdEA.en.vtt
02. Effective Resume Components-AiFcaHRGdEA.es-MX.vtt
02. Effective Resume Components-AiFcaHRGdEA.pt-BR.vtt
06. Resume Review-L3F2BFGYMtI.es-MX.vtt
06. Resume Review-L3F2BFGYMtI.en.vtt
06. Resume Review-L3F2BFGYMtI.pt-BR.vtt
03. Resume Structure-POM0MqLTj98.zh-CN.vtt
03. Resume Structure-POM0MqLTj98.en.vtt
03. Resume Structure-POM0MqLTj98.es-MX.vtt
03. Resume Structure-POM0MqLTj98.pt-BR.vtt
index.html
03. Resume Structure.html
05. Resume Reflection.html
02. Effective Resume Components.html
01. Convey Your Skills Concisely.html
04. Describe Your Work Experiences.html
06. Resume Review.html
Project Description - Resume Review Project (Career Change).html
08. Resources in Your Career Portal.html
07. Resume Review (Career Change).html
img
career-portal-sidebar.png
screen-shot-2017-10-31-at-1.06.42-pm.png
Project Rubric - Resume Review Project (Career Change).html
04. Describe Your Work Experiences-B1LED4txinI.mp4
01. Convey Your Skills Concisely-xnQr3ohml9s.mp4
03. Resume Structure-POM0MqLTj98.mp4
05. Resume Reflection-8Cj_tCp8mls.mp4
06. Resume Review-L3F2BFGYMtI.mp4
02. Effective Resume Components-AiFcaHRGdEA.mp4
Part 08-Module 02-Lesson 03_Refine Your Prior Industry Experience Resume
04. Describe Your Work Experiences-B1LED4txinI.zh-CN.vtt
04. Describe Your Work Experiences-B1LED4txinI.en.vtt
04. Describe Your Work Experiences-B1LED4txinI.es-MX.vtt
04. Describe Your Work Experiences-B1LED4txinI.pt-BR.vtt
05. Resume Reflection-8Cj_tCp8mls.es-MX.vtt
05. Resume Reflection-8Cj_tCp8mls.pt-BR.vtt
01. Convey Your Skills Concisely-xnQr3ohml9s.zh-CN.vtt
05. Resume Reflection-8Cj_tCp8mls.zh-CN.vtt
01. Convey Your Skills Concisely-xnQr3ohml9s.es-MX.vtt
01. Convey Your Skills Concisely-xnQr3ohml9s.pt-BR.vtt
05. Resume Reflection-8Cj_tCp8mls.en.vtt
02. Effective Resume Components-AiFcaHRGdEA.zh-CN.vtt
01. Convey Your Skills Concisely-xnQr3ohml9s.en.vtt
06. Resume Review-L3F2BFGYMtI.zh-CN.vtt
02. Effective Resume Components-AiFcaHRGdEA.en.vtt
02. Effective Resume Components-AiFcaHRGdEA.es-MX.vtt
02. Effective Resume Components-AiFcaHRGdEA.pt-BR.vtt
06. Resume Review-L3F2BFGYMtI.es-MX.vtt
06. Resume Review-L3F2BFGYMtI.en.vtt
06. Resume Review-L3F2BFGYMtI.pt-BR.vtt
03. Resume Structure-POM0MqLTj98.zh-CN.vtt
03. Resume Structure-POM0MqLTj98.en.vtt
03. Resume Structure-POM0MqLTj98.es-MX.vtt
03. Resume Structure-POM0MqLTj98.pt-BR.vtt
index.html
03. Resume Structure.html
05. Resume Reflection.html
02. Effective Resume Components.html
01. Convey Your Skills Concisely.html
04. Describe Your Work Experiences.html
06. Resume Review.html
Project Description - Resume Review Project (Prior Industry Experience).html
08. Resources in Your Career Portal.html
07. Resume Review (Prior Industry Experience).html
img
career-portal-sidebar.png
screen-shot-2017-10-31-at-1.06.42-pm.png
Project Rubric - Resume Review Project (Prior Industry Experience).html
04. Describe Your Work Experiences-B1LED4txinI.mp4
01. Convey Your Skills Concisely-xnQr3ohml9s.mp4
03. Resume Structure-POM0MqLTj98.mp4
05. Resume Reflection-8Cj_tCp8mls.mp4
06. Resume Review-L3F2BFGYMtI.mp4
02. Effective Resume Components-AiFcaHRGdEA.mp4
Part 08-Module 02-Lesson 01_Refine Your Entry-Level Resume
04. Describe Your Work Experiences-B1LED4txinI.zh-CN.vtt
04. Describe Your Work Experiences-B1LED4txinI.en.vtt
04. Describe Your Work Experiences-B1LED4txinI.es-MX.vtt
04. Describe Your Work Experiences-B1LED4txinI.pt-BR.vtt
05. Resume Reflection-8Cj_tCp8mls.es-MX.vtt
05. Resume Reflection-8Cj_tCp8mls.pt-BR.vtt
01. Convey Your Skills Concisely-xnQr3ohml9s.zh-CN.vtt
05. Resume Reflection-8Cj_tCp8mls.zh-CN.vtt
01. Convey Your Skills Concisely-xnQr3ohml9s.es-MX.vtt
01. Convey Your Skills Concisely-xnQr3ohml9s.pt-BR.vtt
05. Resume Reflection-8Cj_tCp8mls.en.vtt
02. Effective Resume Components-AiFcaHRGdEA.zh-CN.vtt
01. Convey Your Skills Concisely-xnQr3ohml9s.en.vtt
06. Resume Review-L3F2BFGYMtI.zh-CN.vtt
02. Effective Resume Components-AiFcaHRGdEA.en.vtt
02. Effective Resume Components-AiFcaHRGdEA.es-MX.vtt
02. Effective Resume Components-AiFcaHRGdEA.pt-BR.vtt
06. Resume Review-L3F2BFGYMtI.es-MX.vtt
06. Resume Review-L3F2BFGYMtI.en.vtt
06. Resume Review-L3F2BFGYMtI.pt-BR.vtt
03. Resume Structure-POM0MqLTj98.zh-CN.vtt
03. Resume Structure-POM0MqLTj98.en.vtt
03. Resume Structure-POM0MqLTj98.es-MX.vtt
03. Resume Structure-POM0MqLTj98.pt-BR.vtt
index.html
03. Resume Structure.html
05. Resume Reflection.html
02. Effective Resume Components.html
01. Convey Your Skills Concisely.html
04. Describe Your Work Experiences.html
06. Resume Review.html
Project Description - Resume Review Project (Entry-level).html
08. Resources in Your Career Portal.html
07. Resume Review (Entry-level).html
img
career-portal-sidebar.png
screen-shot-2017-10-31-at-1.06.42-pm.png
Project Rubric - Resume Review Project (Entry-level).html
04. Describe Your Work Experiences-B1LED4txinI.mp4
01. Convey Your Skills Concisely-xnQr3ohml9s.mp4
03. Resume Structure-POM0MqLTj98.mp4
05. Resume Reflection-8Cj_tCp8mls.mp4
06. Resume Review-L3F2BFGYMtI.mp4
02. Effective Resume Components-AiFcaHRGdEA.mp4
Part 06-Module 01-Lesson 01_Introduction to RL
05. Resources-_YPqfAnCqtk.zh-CN.vtt
04. OpenAI Gym-MktEOWp3QLg.zh-CN.vtt
05. Resources-_YPqfAnCqtk.en.vtt
01. Introduction-6jSFl5kxIBs.zh-CN.vtt
04. OpenAI Gym-MktEOWp3QLg.en.vtt
05. Resources-_YPqfAnCqtk.pt-BR.vtt
01. Introduction-6jSFl5kxIBs.en.vtt
01. Introduction-6jSFl5kxIBs.pt-BR.vtt
04. OpenAI Gym-MktEOWp3QLg.pt-BR.vtt
02. Applications-CV6B84mKRNM.zh-CN.vtt
02. Applications-CV6B84mKRNM.en.vtt
02. Applications-CV6B84mKRNM.pt-BR.vtt
index.html
03. The Setting.html
01. Introduction.html
06. Reference Guide.html
05. Resources.html
03. The Setting-nh8Gwdu19nc.zh-CN.vtt
04. OpenAI Gym.html
03. The Setting-nh8Gwdu19nc.en.vtt
02. Applications.html
03. The Setting-nh8Gwdu19nc.pt-BR.vtt
img
paper-notes.svg.png
01. Introduction-6jSFl5kxIBs.mp4
05. Resources-_YPqfAnCqtk.mp4
03. The Setting-nh8Gwdu19nc.mp4
02. Applications-CV6B84mKRNM.mp4
04. OpenAI Gym-MktEOWp3QLg.mp4
Part 10-Module 02-Lesson 03_Searching and Sorting
01. Binary Search-0VN5iwEyq4c.pt-BR.vtt
01. Binary Search-0VN5iwEyq4c.zh-CN.vtt
01. Binary Search-0VN5iwEyq4c.en.vtt
01. Binary Search-0VN5iwEyq4c.en-US.vtt
06. Intro to Sorting-Z6yuIen71zM.zh-CN.vtt
06. Intro to Sorting-Z6yuIen71zM.pt-BR.vtt
08. Efficiency of Bubble Sort-KddkHygi7is.pt-BR.vtt
06. Intro to Sorting-Z6yuIen71zM.en.vtt
06. Intro to Sorting-Z6yuIen71zM.en-US.vtt
08. Efficiency of Bubble Sort-KddkHygi7is.zh-CN.vtt
08. Efficiency of Bubble Sort-KddkHygi7is.en.vtt
08. Efficiency of Bubble Sort-KddkHygi7is.en-US.vtt
07. Bubble Sort-h_osLG3GmjE.zh-CN.vtt
07. Bubble Sort-h_osLG3GmjE.pt-BR.vtt
07. Bubble Sort-h_osLG3GmjE.en.vtt
07. Bubble Sort-h_osLG3GmjE.en-US.vtt
14. Efficiency of Quick Sort-aMb5GHPGQ1U.pt-BR.vtt
14. Efficiency of Quick Sort-aMb5GHPGQ1U.zh-CN.vtt
13. Quick Sort-kUon6854joI.zh-CN.vtt
13. Quick Sort-kUon6854joI.pt-BR.vtt
index.html
14. Efficiency of Quick Sort-aMb5GHPGQ1U.en.vtt
14. Efficiency of Quick Sort-aMb5GHPGQ1U.en-US.vtt
10. Merge Sort-K916wfSzKxE.zh-CN.vtt
13. Quick Sort-kUon6854joI.en.vtt
13. Quick Sort-kUon6854joI.en-US.vtt
10. Merge Sort-K916wfSzKxE.pt-BR.vtt
10. Merge Sort-K916wfSzKxE.en.vtt
10. Merge Sort-K916wfSzKxE.en-US.vtt
11. Efficiency of Merge Sort-HKiK5Y-YSkk.zh-CN.vtt
04. Recursion.html
13. Quick Sort.html
10. Merge Sort.html
07. Bubble Sort.html
01. Binary Search.html
06. Intro to Sorting.html
11. Efficiency of Merge Sort.html
14. Efficiency of Quick Sort.html
08. Efficiency of Bubble Sort.html
02. Efficiency of Binary Search.html
11. Efficiency of Merge Sort-HKiK5Y-YSkk.pt-BR.vtt
15. Quick Sort Practice.html
11. Efficiency of Merge Sort-HKiK5Y-YSkk.en.vtt
11. Efficiency of Merge Sort-HKiK5Y-YSkk.en-US.vtt
12. Merge Sort Practice.html
09. Bubble Sort Practice.html
03. Binary Search Practice.html
04. Recursion-_aI2Jch6Epk.zh-CN.vtt
04. Recursion-_aI2Jch6Epk.pt-BR.vtt
05. Recursion Practice.html
04. Recursion-_aI2Jch6Epk.en.vtt
04. Recursion-_aI2Jch6Epk.en-US.vtt
02. Efficiency of Binary Search-7WbRB7dSyvc.zh-CN.vtt
02. Efficiency of Binary Search-7WbRB7dSyvc.pt-BR.vtt
02. Efficiency of Binary Search-7WbRB7dSyvc.en.vtt
02. Efficiency of Binary Search-7WbRB7dSyvc.en-US.vtt
img
7881207114.gif
7910014174.gif
14. Efficiency of Quick Sort-aMb5GHPGQ1U.mp4
13. Quick Sort-kUon6854joI.mp4
07. Bubble Sort-h_osLG3GmjE.mp4
08. Efficiency of Bubble Sort-KddkHygi7is.mp4
02. Efficiency of Binary Search-7WbRB7dSyvc.mp4
10. Merge Sort-K916wfSzKxE.mp4
01. Binary Search-0VN5iwEyq4c.mp4
11. Efficiency of Merge Sort-HKiK5Y-YSkk.mp4
06. Intro to Sorting-Z6yuIen71zM.mp4
04. Recursion-_aI2Jch6Epk.mp4
Part 01-Module 01-Lesson 01_Welcome to Machine Learning
03. Program Structure-rjk8-r-Aa5U.zh-CN.vtt
03. Program Structure-rjk8-r-Aa5U.pt-BR.vtt
03. Program Structure-rjk8-r-Aa5U.en.vtt
02. Projects You Will Build-P7YK47GUGWk.zh-CN.vtt
02. Projects You Will Build-P7YK47GUGWk.pt-BR.vtt
02. Projects You Will Build-P7YK47GUGWk.en.vtt
01. 01 MLNDIntro Program Welcome V3-A8AnsR6e75I.zh-CN.vtt
index.html
01. 01 MLNDIntro Program Welcome V3-A8AnsR6e75I.en.vtt
01. 01 MLNDIntro Program Welcome V3-A8AnsR6e75I.pt-BR.vtt
09. Week 2 Plan.html
08. Week 1 Plan.html
01. Welcome to the Machine Learning Engineer Nanodegree Program.html
07. Program Readiness.html
06. Community Guidelines.html
02. Projects You Will Build.html
03. Program Structure.html
05. Udacity Support.html
04. Deadline Policy.html
img
semi-supervised-learning.jpg
screen-shot-2018-08-17-at-2.07.36-pm.png
screen-shot-2018-08-17-at-2.07.46-pm.png
screen-shot-2018-06-12-at-5.07.10-pm.png
03. Program Structure-rjk8-r-Aa5U.mp4
01. 01 MLNDIntro Program Welcome V3-A8AnsR6e75I.mp4
02. Projects You Will Build-P7YK47GUGWk.mp4
Part 11-Module 01-Lesson 01_Software and Tools
index.html
01. TensorFlow.html
Part 04-Module 05-Lesson 01_PCA Mini-Project
index.html
01. PCA Mini-Project.html
Part 05-Module 01-Lesson 06_Deep Learning Assessment
index.html
01. Assessment.html
Part 02-Module 04-Lesson 01_NumPy and pandas Assessment
index.html
01. Assessment.html
Part 04-Module 07-Lesson 01_Unsupervised Learning Assessment
index.html
01. Assessment.html
Part 06-Module 03-Lesson 01_Reinforcement Learning Assessment
index.html
01. Assessment.html
Part 03-Module 01-Lesson 07_Supervised Learning Assessment
index.html
01. Supervised Learning Assessment.html
Part 02-Module 04-Lesson 02_Model Evaluation and Validation Assessment
index.html
01. Model Evaluation and Validation assessment.html
Part 06-Module 01-Lesson 07_Solve OpenAI Gym's Taxi-v2 Task
index.html
03. Mini Project.html
01. Introduction.html
02. Instructions.html
img
screen-shot-2018-04-14-at-3.13.15-pm.png
new-tab.gif
open-terminal.gif
run-main.gif
open-agent-monitor-main.gif
Part 04-Module 02-Lesson 02_Clustering Mini-Project
index.html
03. Solution.html
02. K-means clustering of movie ratings.html
01. Intro.html
Part 01-Module 02-Lesson 01_Career Services Available to You
01. Meet the Careers Team-cuKecPpZ7PM.en.vtt
index.html
01. Meet the Careers Team-cuKecPpZ7PM.pt-BR.vtt
02. Access Your Career Portal.html
01. Meet the Careers Team.html
03. Your Udacity Professional Profile.html
img
screen-shot-2017-10-27-at-1.49.58-pm.png
screen-shot-2018-07-27-at-1.24.38-pm.png
udacitylogo-copy.png
get-hired-with-the-udacity-career-portal.gif
01. Meet the Careers Team-cuKecPpZ7PM.mp4
Part 11-Module 01-Lesson 02_Deep Learning
index.html
02. What You'll Watch and Learn.html
01. Deep Learning.html
03. Deep Learning What You'll Do.html
Part 01-Module 01-Lesson 03_Introductory Practice Project
index.html
04. Titanic Survival Exploration.html
01. Overview.html
02. Software Requirements.html
03. Project files.html
assets
css
styles.css
fonts
KaTeX_Size3-Regular.woff2
KaTeX_Size3-Regular.woff
KaTeX_Size4-Regular.woff2
KaTeX_Size2-Regular.woff2
KaTeX_Size1-Regular.woff2
KaTeX_Size4-Regular.woff
KaTeX_Size2-Regular.woff
KaTeX_Size1-Regular.woff
KaTeX_Size3-Regular.ttf
KaTeX_Caligraphic-Regular.woff2
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KaTeX_Size4-Regular.ttf
KaTeX_Caligraphic-Regular.woff
KaTeX_Caligraphic-Bold.woff
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KaTeX_Size2-Regular.ttf
KaTeX_Size1-Regular.ttf
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KaTeX_SansSerif-Regular.woff2
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KaTeX_SansSerif-Italic.woff
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KaTeX_SansSerif-Bold.woff
KaTeX_Caligraphic-Bold.ttf
KaTeX_Fraktur-Regular.woff2
KaTeX_Math-BoldItalic.woff2
KaTeX_Math-Italic.woff2
KaTeX_Fraktur-Bold.woff2
KaTeX_Typewriter-Regular.woff
KaTeX_Main-BoldItalic.woff2
KaTeX_Fraktur-Regular.woff
KaTeX_Main-Italic.woff2
KaTeX_Math-BoldItalic.woff
KaTeX_Fraktur-Bold.woff
KaTeX_Math-Italic.woff
KaTeX_Script-Regular.ttf
KaTeX_Main-BoldItalic.woff
KaTeX_Main-Italic.woff
KaTeX_SansSerif-Regular.ttf
KaTeX_Main-Bold.woff2
KaTeX_SansSerif-Italic.ttf
KaTeX_Main-Regular.woff2
KaTeX_AMS-Regular.woff2
KaTeX_SansSerif-Bold.ttf
KaTeX_Fraktur-Regular.ttf
KaTeX_Fraktur-Bold.ttf
KaTeX_Typewriter-Regular.ttf
KaTeX_Main-Bold.woff
KaTeX_Main-Regular.woff
KaTeX_Math-BoldItalic.ttf
KaTeX_AMS-Regular.woff
KaTeX_Math-Italic.ttf
KaTeX_Main-BoldItalic.ttf
KaTeX_Main-Italic.ttf
KaTeX_Main-Bold.ttf
KaTeX_Main-Regular.ttf
KaTeX_AMS-Regular.ttf
katex.min.css
plyr.css
jquery.mCustomScrollbar.min.css
bootstrap.min.css
js
jquery.mCustomScrollbar.concat.min.js
bootstrap.min.js
jquery-3.3.1.min.js
plyr.polyfilled.min.js
katex.min.js
img
udacimak.png
Part 05-Module 01-Lesson 07_Deep Learning Project
index.html
01. Dog Breed Recognition Project.html
02. Dog Breed Workspace.html
Project Description - Dog Breed Classifier.html
Project Rubric - Dog Breed Classifier.html
Part 09-Module 01-Lesson 02_LinkedIn Review
index.html
02. Resources in Your Career Portal.html
Project Description - LinkedIn Profile Review Project.html
01. Using LinkedIn.html
img
career-portal-sidebar.png
screen-shot-2017-10-31-at-1.06.42-pm.png
Project Rubric - LinkedIn Profile Review Project.html
media
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unnamed-project-desc-0.gif
Part 05-Module 01-Lesson 02_Cloud Computing
index.html
07. More Resources.html
02. Create an AWS Account.html
01. Overview.html
04. Apply Credits.html
03. Get Access to GPU Instances.html
img
launch.png
edit-security-group.png
aws-create-account.png
review-and-launch.png
launch-instance.png
screen-shot-2018-01-08-at-5.37.22-am.png
aws-add-sec-group.png
stop.png
amazonwebservices-logo.svg.png
p2xlarge-limit-request.png
screen-shot-2018-07-19-at-5.39.37-pm.png
screen-shot-2017-11-26-at-10.30.15-am.png
p2-limit-increase.png
screen-shot-2017-06-13-at-12.58.03-pm.png
screen-shot-2018-01-08-at-5.38.03-am.png
screen-shot-2017-11-26-at-9.55.20-am.png
screen-shot-2017-11-26-at-9.38.24-am.png
06. Login to the Instance.html
05. Launch an Instance.html
Part 07-Module 01-Lesson 01_Writing up a Capstone Proposal
index.html
05. Submitting the Project.html
04. Proposal Guidelines.html
02. Description.html
01. Overview.html
03. Software and Data Requirements.html
Project Rubric - Capstone Proposal.html
Project Description - Capstone Proposal.html
Part 07-Module 02-Lesson 01_Machine Learning Capstone Project
index.html
04. Report Guidelines.html
01. Overview.html
06. Submitting the Project.html
05. Example Reports.html
03. Software and Data Requirements.html
02. Description.html
Project Description - Capstone Project.html
Project Rubric - Capstone Project.html
Part 02-Module 05-Lesson 01_Predicting Boston Housing Prices
index.html
05. Project Workspace.html
01. Project Overview.html
04. Uploading to Workspace.html
02. Starting the project.html
03. Submitting the project.html
Project Description - Predicting Boston Housing Prices.html
Project Rubric - Predicting Boston Housing Prices.html
img
step-2-file-upload.png
step1-file-upload.png
step-0.png
Part 04-Module 08-Lesson 01_Creating Customer Segments
index.html
06. Workspace.html
05. Uploading to Workspace.html
03. Starting the project.html
04. Submitting the project.html
02. Software Requirements.html
01. Overview.html
Project Description - Creating Customer Segments.html
Project Rubric - Creating Customer Segments.html
img
step-2-file-upload.png
step1-file-upload.png
step-0.png
Part 06-Module 02-Lesson 05_Teach a Quadcopter How to Fly
index.html
02. Quadcopter workspace.html
Project Description - Teach a Quadcopter How to Fly.html
03. Replay Buffer.html
07. Ornstein–Uhlenbeck Noise.html
Project Rubric - Teach a Quadcopter How to Fly.html
08. Troubleshooting.html
05. DDPG Critic.html
01. Project Intro.html
04. DDPG Actor.html
06. DDPG Agent.html
img
parrot-ar-drone.jpg
submit-workspace.png
Part 09-Module 01-Lesson 03_Udacity Professional Profile
index.html
08. Experience.html
06. Skills.html
01. Introduction.html
02. Getting Started.html
Project Description - Udacity Professional Profile Review.html
05. Recruitment Data.html
09. Resources in Your Career Portal.html
04. Top Section.html
07. Projects.html
03. Customizing Your Profile.html
Project Rubric - Udacity Professional Profile Review.html
img
career-portal-sidebar.png
screen-shot-2017-09-04-at-2.07.44-pm.png
screen-shot-2017-12-14-at-3.11.32-pm.png
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index.html
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