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Computer Vision Masterclass
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[TutsNode.com] - Computer Vision Masterclass (Size: 9.81 GB) (Files: 643)
[TutsNode.com] - Computer Vision Masterclass
2. Face detection
8. Eye detection with haarcascades.mp4
1.1 Source code - Google Colab File.html
9. HOMERWORK – detecting other objects.html
10. Homework solution.srt
5. Face detection with Haarcascade and OpenCV.srt
14. HOMEWORK – Haarcascade x HOG x CNN.html
4. Loading and pre-processing the image.srt
18. Additional reading.html
11. HOG (Histrograms of Oriented Gradients) – intuition.srt
12. Face detection with HOG and Dlib.srt
8. Eye detection with haarcascades.srt
3. Cascade classifier - intuition.srt
7. Haarcascades parameters 2.srt
17. Face detection on the webcam.srt
6. Haarcascades parameters 1.srt
13. Face detection with CNN and Dlib.srt
15. Homework solution.srt
1. Plan of attack.srt
2. Images and pixels.srt
16. Anaconda and PyCharm.srt
5. Face detection with Haarcascade and OpenCV.mp4
7. Haarcascades parameters 2.mp4
6. Haarcascades parameters 1.mp4
3. Cascade classifier - intuition.mp4
4. Loading and pre-processing the image.mp4
12. Face detection with HOG and Dlib.mp4
11. HOG (Histrograms of Oriented Gradients) – intuition.mp4
17. Face detection on the webcam.mp4
13. Face detection with CNN and Dlib.mp4
15. Homework solution.mp4
1. Plan of attack.mp4
2. Images and pixels.mp4
10. Homework solution.mp4
16. Anaconda and PyCharm.mp4
9. Autoencoders
11. Convolutional autoencoders 2.srt
15. Homework solution.srt
6. Building and training a linear autoencoder.srt
9. Encoding and decoding the test images.srt
4. Visualizing the images.srt
11. Convolutional autoencoders 2.mp4
13. Convolutional autoencoders 4.srt
8. Decoding the images.srt
7. Encoding the images.srt
12. Convolutional autoencoders 3.srt
2. Autoencoders – intuition.srt
3. Importing the libraries and dataset.srt
10. Convolutional autoencoders 1.srt
5. Preprocessing the images.srt
1. Plan of attack.srt
14. HOMEWORK.html
16. Additional reading.html
1.1 Source code - Google Colab File.html
1.2 Homework solution.html
15. Homework solution.mp4
12. Convolutional autoencoders 3.mp4
9. Encoding and decoding the test images.mp4
6. Building and training a linear autoencoder.mp4
13. Convolutional autoencoders 4.mp4
4. Visualizing the images.mp4
7. Encoding the images.mp4
8. Decoding the images.mp4
10. Convolutional autoencoders 1.mp4
3. Importing the libraries and dataset.mp4
2. Autoencoders – intuition.mp4
5. Preprocessing the images.mp4
1. Plan of attack.mp4
11. Recognition of gestures and actions
5. Predicting body points 1.srt
7. Detecting gestures in images.srt
3. Importing the libraries and the image.srt
2. Gestures and actions recognition – intuition.srt
9. Detecting gestures in videos 2.srt
8. Detecting gestures in videos 1.srt
5. Predicting body points 1.mp4
6. Predicting body points 2.srt
4. Loading the pre-trained neural network.srt
11. Homework solution.srt
1. Plan of attack.srt
12. Additional reading.html
10. HOMEWORK.html
1.1 Source code - Google Colab File.html
7. Detecting gestures in images.mp4
9. Detecting gestures in videos 2.mp4
2. Gestures and actions recognition – intuition.mp4
3. Importing the libraries and the image.mp4
6. Predicting body points 2.mp4
11. Homework solution.mp4
8. Detecting gestures in videos 1.mp4
4. Loading the pre-trained neural network.mp4
1. Plan of attack.mp4
1. Introduction
2. Google Drive folder.html
1. Course content.srt
1. Course content.mp4
13. Style transfer
5. Building the neural network 1.srt
9. Training the neural network 1.srt
10. Training the neural network 2.srt
7. Building the neural network 3.srt
4. Loading and pre-processing the images.srt
6. Building the neural network 2.srt
8. Building the neural network 4.srt
3. Loading VGG19 network.srt
2. Style transfer – intuition.srt
13. Homework solution.srt
11. Visualizing the result.srt
1. Plan of attack.srt
14. Additional reading.html
12. HOMEWORK.html
1.1 Source code - Google Colab File.html
10. Training the neural network 2.mp4
5. Building the neural network 1.mp4
7. Building the neural network 3.mp4
6. Building the neural network 2.mp4
9. Training the neural network 1.mp4
8. Building the neural network 4.mp4
4. Loading and pre-processing the images.mp4
13. Homework solution.mp4
2. Style transfer – intuition.mp4
3. Loading VGG19 network.mp4
11. Visualizing the result.mp4
1. Plan of attack.mp4
14. GANs (Generative adversarial networks)
4. Building the generator 1.srt
3. Loading the dataset.srt
8. Training the GAN 1.srt
2. GANs – intuition.srt
9. Training the GAN 2.srt
6. Building the discriminator.srt
7. Calculating the loss.srt
5. Building the generator 2.srt
11. Homerwork solution.srt
1. Plan of attack.srt
10. HOMEWORK.html
12. Additional reading.html
1.1 Homework solution.html
1.2 Source code - Google Colab File.html
4. Building the generator 1.mp4
3. Loading the dataset.mp4
8. Training the GAN 1.mp4
9. Training the GAN 2.mp4
6. Building the discriminator.mp4
2. GANs – intuition.mp4
7. Calculating the loss.mp4
5. Building the generator 2.mp4
11. Homerwork solution.mp4
1. Plan of attack.mp4
3. Face recognition
12. Detecting facial descriptors 2.srt
4. Preprocessing the images.srt
11. Detecting facial descriptors 1.srt
13. Calculating distances between faces.srt
1.1 Source code - Google Colab File.html
10. Detecting facial points.srt
14. Recognizing faces with Dlib 1.srt
7. Evaluating the LBPH classifier.srt
16. HOMEWORK.html
3. Loading the faces dataset.srt
19. Additional reading.html
2. LBPH algorithm - intuition.srt
6. Recognizing faces with LBPH.srt
12. Detecting facial descriptors 2.mp4
17. Homework solution.srt
18. Face recognition on the webcam.srt
1. Plan of attack.srt
8. LBPH parameters.srt
5. Training the LBPH classifier.srt
9. LBPH parameters – implementation.srt
15. Recognizing faces with Dlib 2.srt
13. Calculating distances between faces.mp4
11. Detecting facial descriptors 1.mp4
4. Preprocessing the images.mp4
10. Detecting facial points.mp4
14. Recognizing faces with Dlib 1.mp4
7. Evaluating the LBPH classifier.mp4
3. Loading the faces dataset.mp4
6. Recognizing faces with LBPH.mp4
17. Homework solution.mp4
18. Face recognition on the webcam.mp4
2. LBPH algorithm - intuition.mp4
9. LBPH parameters – implementation.mp4
15. Recognizing faces with Dlib 2.mp4
5. Training the LBPH classifier.mp4
8. LBPH parameters.mp4
1. Plan of attack.mp4
5. Neural networks for image classification
39. Feature extraction with OpenCV 2.srt
6. Weight update 2.srt
5. Weight update 1.srt
34. Evaluating the neural network.srt
33. Building and training the neural network.srt
21. Hidden layers.srt
37. Extracting features from images.srt
20. Bias, error and multiple outputs.srt
27. Extracting pixels from images 1.srt
4. Perceptron.srt
48. Homework solution.srt
28. Extracting pixels from images 2.srt
14. Gradient descent and derivative.srt
1.1 Source code - Google Colab File.html
45. Evaluating the neural network.srt
19. Weight update with backprogation 2.srt
1.2 Homework solution.html
3. Artificial neuron.srt
30. Extracting pixels from images 4.srt
17. Backpropagation and learning rate.srt
44. Building and training the neural network.srt
16. Hidden layer delta.srt
29. Extracting pixels from images 3.srt
42. Feature extraction with OpenCV 5.srt
35. Saving and loading the network.srt
40. Feature extraction with OpenCV 3.srt
41. Feature extraction with OpenCV 4.srt
18. Weight update with backprogation 1.srt
36. Classifying one single image.srt
25. Pixels and neural networks.srt
38. Feature extraction with OpenCV 1.srt
2. Biological fundamentals.srt
15. Output layer delta.srt
46. Saving, loading and classifying one single image.srt
8. Activation functions.srt
23. Stochastic gradient descent.srt
32. Creating the train and test sets.srt
9. Hidden layer activation 1.srt
11. Output layer activation.srt
12. Error calculation (loss function).srt
22. Output layer with categorical data.srt
26. Importing the libraries.srt
47. HOMEWORK.html
13. Basic algorithm.srt
49. Additional reading.html
10. Hidden layer activation 2.srt
31. Normalizing the data.srt
7. Introduction to multilayer neural networks.srt
43. Creating the train and test sets.srt
1. Plan of attack.srt
24. Deep learning.srt
39. Feature extraction with OpenCV 2.mp4
27. Extracting pixels from images 1.mp4
33. Building and training the neural network.mp4
34. Evaluating the neural network.mp4
20. Bias, error and multiple outputs.mp4
48. Homework solution.mp4
28. Extracting pixels from images 2.mp4
37. Extracting features from images.mp4
42. Feature extraction with OpenCV 5.mp4
2. Biological fundamentals.mp4
40. Feature extraction with OpenCV 3.mp4
41. Feature extraction with OpenCV 4.mp4
6. Weight update 2.mp4
29. Extracting pixels from images 3.mp4
30. Extracting pixels from images 4.mp4
14. Gradient descent and derivative.mp4
5. Weight update 1.mp4
44. Building and training the neural network.mp4
45. Evaluating the neural network.mp4
35. Saving and loading the network.mp4
19. Weight update with backprogation 2.mp4
21. Hidden layers.mp4
4. Perceptron.mp4
36. Classifying one single image.mp4
16. Hidden layer delta.mp4
38. Feature extraction with OpenCV 1.mp4
18. Weight update with backprogation 1.mp4
46. Saving, loading and classifying one single image.mp4
25. Pixels and neural networks.mp4
22. Output layer with categorical data.mp4
3. Artificial neuron.mp4
23. Stochastic gradient descent.mp4
43. Creating the train and test sets.mp4
12. Error calculation (loss function).mp4
32. Creating the train and test sets.mp4
9. Hidden layer activation 1.mp4
15. Output layer delta.mp4
17. Backpropagation and learning rate.mp4
11. Output layer activation.mp4
26. Importing the libraries.mp4
31. Normalizing the data.mp4
10. Hidden layer activation 2.mp4
8. Activation functions.mp4
1. Plan of attack.mp4
24. Deep learning.mp4
7. Introduction to multilayer neural networks.mp4
13. Basic algorithm.mp4
8. Neural networks for classification of emotions
4. Building and training the neural network.srt
9. Classifying emotions in videos.srt
5. Saving and loading the model.srt
7. Classifying one single image.srt
8. Classifying multiple images.srt
11. Homework solution.srt
2. Importing the libraries and images.srt
6. Evaluating the neural network.srt
3. Creating the train and test dataset.srt
1. Plan of attack.srt
10. HOMEWORK.html
12. Additional reading.html
1.1 Homerwork solution.html
1.2 Source code - Google Colab File.html
4. Building and training the neural network.mp4
9. Classifying emotions in videos.mp4
8. Classifying multiple images.mp4
7. Classifying one single image.mp4
11. Homework solution.mp4
2. Importing the libraries and images.mp4
6. Evaluating the neural network.mp4
1. Plan of attack.mp4
3. Creating the train and test dataset.mp4
5. Saving and loading the model.mp4
6. Convolutional neural networks for image classification
10. Building and training the neural network.srt
9. Creating the train and test dataset.srt
15. Homework solution.srt
3. Convolutional operation.srt
11. Evaluating the neural network.srt
2. Introduction to convolutional neural networks.srt
5. Flattening.srt
13. Classifying one single image.srt
4. Pooling.srt
6. Dense neural network.srt
8. Loading the images.srt
1. Plan of attack.srt
1.1 Homework solution.html
1.2 Source code - Google Colab File.html
7. Importing the libraries.srt
12. Saving and loading the network.srt
14. HOMEWORK.html
16. Additional reading.html
10. Building and training the neural network.mp4
9. Creating the train and test dataset.mp4
15. Homework solution.mp4
5. Flattening.mp4
11. Evaluating the neural network.mp4
2. Introduction to convolutional neural networks.mp4
6. Dense neural network.mp4
3. Convolutional operation.mp4
13. Classifying one single image.mp4
8. Loading the images.mp4
4. Pooling.mp4
7. Importing the libraries.mp4
12. Saving and loading the network.mp4
1. Plan of attack.mp4
4. Object tracking
4. Object tracking with KCF.srt
5. Object tracking with CSRT.srt
6. HOMEWORK.html
8. Additional reading.html
3. KCF and CSRT algorithms.srt
2. Object tracking vs. object detection.srt
7. Homework solution.srt
1. Plan of attack.srt
4. Object tracking with KCF.mp4
3. KCF and CSRT algorithms.mp4
7. Homework solution.mp4
2. Object tracking vs. object detection.mp4
1. Plan of attack.mp4
5. Object tracking with CSRT.mp4
7. Transfer learning and fine tuning
5. Pre-trained neural network.srt
13. Homework solution.srt
6. Creating the custom dense layer.srt
2. Transfer learning – intuition.srt
7. Building and training the neural network.srt
8. Evaluating the neural network.srt
10. Fine tuning – implementation and evaluation.srt
3. Importing the libraries and dataset.srt
4. Creating the train and test dataset.srt
11. Saving, loading and classifying one single image.srt
1. Plan of attack.srt
1.1 Source code - Google Colab File.html
1.2 Homework solution.html
9. Fine tuning – intuition.srt
12. HOMEWORK.html
14. Additional reading.html
5. Pre-trained neural network.mp4
13. Homework solution.mp4
6. Creating the custom dense layer.mp4
7. Building and training the neural network.mp4
10. Fine tuning – implementation and evaluation.mp4
2. Transfer learning – intuition.mp4
8. Evaluating the neural network.mp4
3. Importing the libraries and dataset.mp4
4. Creating the train and test dataset.mp4
11. Saving, loading and classifying one single image.mp4
9. Fine tuning – intuition.mp4
1. Plan of attack.mp4
12. Deep dream
3. Loading the InceptionNet network.srt
7. Gradient ascent 1.srt
4. Loading and preprocessing the image.srt
9. Generating images.srt
6. Calculating the loss.srt
2. Deep dream – intuition.srt
5. Getting the activations.srt
8. Gradient ascent 2.srt
1. Plan of attack.srt
11. Homework solution.srt
10. HOMEWORK.html
12. Additional reading.html
1.1 Source code - Google Colab File.html
9. Generating images.mp4
3. Loading the InceptionNet network.mp4
7. Gradient ascent 1.mp4
4. Loading and preprocessing the image.mp4
6. Calculating the loss.mp4
5. Getting the activations.mp4
2. Deep dream – intuition.mp4
8. Gradient ascent 2.mp4
1. Plan of attack.mp4
11. Homework solution.mp4
15. Image segmentation
7. Removing the background 1.srt
4. Importing the libraries.srt
6. Detecting objects.srt
2. Image segmentation – intuition.srt
5. Loading the pre-trained neural network.srt
9. Segmentation in videos 1.srt
8. Removing the background 2.srt
1. Plan of attack.srt
10. Segmentation in videos 2.srt
3. Downloading the repository.srt
12. Homerwork solution.srt
13. Additional reading.html
11. HOMEWORK.html
1.1 Source code - Google Colab File.html
7. Removing the background 1.mp4
6. Detecting objects.mp4
4. Importing the libraries.mp4
2. Image segmentation – intuition.mp4
9. Segmentation in videos 1.mp4
5. Loading the pre-trained neural network.mp4
8. Removing the background 2.mp4
10. Segmentation in videos 2.mp4
1. Plan of attack.mp4
12. Homerwork solution.mp4
3. Downloading the repository.mp4
10. Object detection with YOLO
4. Testing the detector.srt
5. Darknet and GPU.srt
6. Threshold and ext_output parameters.srt
7. Detecting objects in videos.srt
2. YOLO – intuition.srt
3. Downloading and compiling Darknet.srt
9. Homework solution.srt
1. Plan of attack.srt
10. Additional reading.html
8. HOMEWORK.html
1.1 Source code - Google Colab File.html
6. Threshold and ext_output parameters.mp4
4. Testing the detector.mp4
5. Darknet and GPU.mp4
7. Detecting objects in videos.mp4
3. Downloading and compiling Darknet.mp4
2. YOLO – intuition.mp4
9. Homework solution.mp4
1. Plan of attack.mp4
16. Final remarks
1. Final remarks.srt
1. Final remarks.mp4
TutsNode.com.txt
[TGx]Downloaded from torrentgalaxy.to .txt
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Torrent description
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