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Autonomous Cars The Complete Computer Vision Course 2021
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[TutsNode.com] - Autonomous Cars The Complete Computer Vision Course 2021 (Size: 6.37 GB) (Files: 423)
[TutsNode.com] - Autonomous Cars The Complete Computer Vision Course 2021
9. Vehicle Detection
6. YOLO Implementation Part 2.mp4
6. YOLO Implementation Part 2.srt
5. YOLO Implementation Part 1.srt
5.1 yolo.h5
5.4 Udemy_Image_YOLO_Implementation.ipynb
5.3 images3-20210325T032710Z-001.zip
1. Introduction.srt
2. What makes YOLO different.srt
4. The YOLO architecture.srt
3. The YOLO loss function.srt
5.2 coco_classes.txt
6.1 Udemy_YOLO_detection_video.ipynb
5. YOLO Implementation Part 1.mp4
1. Introduction.mp4
2. What makes YOLO different.mp4
3. The YOLO loss function.mp4
4. The YOLO architecture.mp4
6.2 video_sample.mp4
4. Computer vision for Self-driving Cars
33.1 The_Hough_transform.ipynb
6.2 images2-20210319T024702Z-001.zip
31. Masking regions of interest.srt
14. Implementing Color space manipulation Part 2.srt
18. Sharpening and blurring Implementation.srt
33. The Hough transform Implementation.srt
29. Perspective transformation Implementation.srt
13. Implementing Color space manipulation Part 1.srt
7.1 Udemy_Converting_images_from_RGB_to_grayscale (1).ipynb
1. Introduction.srt
8.1 Udemy_Converting_images_from_RGB_to_grayscale (1).ipynb
2. Computer vision Introduction.srt
6. Converting images from RGB to grayscale.srt
30. Cropping, dilating, and eroding an image Implementation.srt
5. Digital representation of an image.srt
22. Canny edge detection.srt
8. QUICK FIX Video.srt
32. Introduction to The Hough transform.srt
9. Introduction to the Color space techniques.srt
7. Detection with the grayscale image.srt
10. Introduction to RGB space.srt
11. Introduction to HSV space.srt
16. Introduction to convolution.srt
12. Introduction to Color space manipulation.srt
3. Challenges in Computer Vision.srt
4. Requirement of Self-Driving Cars.srt
26. Image translation Implementation.srt
25. Image rotation Implementation.srt
15. Implementing Color space manipulation Part 3.srt
19. Introduction to Edge detection and gradient calculation.srt
20. Introduction to Sobel.srt
6.1 Udemy_Converting_images_from_RGB_to_grayscale.ipynb
21. Introduction to Laplacian edge detector.srt
17. Introduction to Sharpening and blurring.srt
27. Image resizing Implementation.srt
23. Application of image transformation.srt
24. Introduction to Affine and Projective transformation.srt
28. Introduction to Perspective transformation.srt
34. Summary of the section.srt
14. Implementing Color space manipulation Part 2.mp4
18. Sharpening and blurring Implementation.mp4
31. Masking regions of interest.mp4
22. Canny edge detection.mp4
29. Perspective transformation Implementation.mp4
33. The Hough transform Implementation.mp4
13. Implementing Color space manipulation Part 1.mp4
30. Cropping, dilating, and eroding an image Implementation.mp4
15. Implementing Color space manipulation Part 3.mp4
25. Image rotation Implementation.mp4
6. Converting images from RGB to grayscale.mp4
26. Image translation Implementation.mp4
32. Introduction to The Hough transform.mp4
7. Detection with the grayscale image.mp4
5. Digital representation of an image.mp4
22.1 Chapter_4_Code_Notebook.ipynb
30.1 Udemy_Converting_images_from_RGB_to_grayscale (2).ipynb
29.1 Udemy_Converting_images_from_RGB_to_grayscale (1).ipynb
16. Introduction to convolution.mp4
27.1 Udemy_Converting_images_from_RGB_to_grayscale.ipynb
26.1 Udemy_Converting_images_from_RGB_to_grayscale.ipynb
4. Requirement of Self-Driving Cars.mp4
3. Challenges in Computer Vision.mp4
27. Image resizing Implementation.mp4
18.1 Udemy_Converting_images_from_RGB_to_grayscale (1).ipynb
17. Introduction to Sharpening and blurring.mp4
11. Introduction to HSV space.mp4
15.1 Udemy_Converting_images_from_RGB_to_grayscale.ipynb
2. Computer vision Introduction.mp4
24. Introduction to Affine and Projective transformation.mp4
19. Introduction to Edge detection and gradient calculation.mp4
20. Introduction to Sobel.mp4
1. Introduction.mp4
31.1 Udemy_Converting_images_from_RGB_to_grayscale (3).ipynb
21. Introduction to Laplacian edge detector.mp4
8. QUICK FIX Video.mp4
28. Introduction to Perspective transformation.mp4
9. Introduction to the Color space techniques.mp4
34. Summary of the section.mp4
23. Application of image transformation.mp4
12. Introduction to Color space manipulation.mp4
10. Introduction to RGB space.mp4
6. Road Sign Detection
8.1 Udemy_Traffic_sign.ipynb
9. Model accuracy.srt
8. Training model.srt
1. Introduction to convolution neural network.srt
5. Loading data.srt
9. Model accuracy.mp4
9.1 Udemy_Traffic_sign (1).ipynb
7. Data Preperation.srt
4. Introduction to the project.srt
2. Convolution Layers.srt
3. Pooling Layers.srt
10. Summary of the project.srt
6. Exploring image.srt
5.1 traffic-signs-data-20210225T074843Z-001.zip
8. Training model.mp4
5. Loading data.mp4
1. Introduction to convolution neural network.mp4
7. Data Preperation.mp4
4. Introduction to the project.mp4
3. Pooling Layers.mp4
2. Convolution Layers.mp4
6. Exploring image.mp4
10. Summary of the project.mp4
1. Introduction
2. How To Make The Most Out Of This Course.srt
8. Important note about tools in this course.srt
7. Important Terms in this course.srt
9. Introduction to Self-Driving Cars.srt
5. What is Multilayer Neural Network.srt
6. What is keras (Optional from AI in Healthcare course).srt
11. Building the safe systems.srt
10. Benefit of Self-Driving Cars.srt
12. Deep learning and computer vision approaches for Self-Driving Cars.srt
3. What is ANN.srt
1. Course structure.srt
13. LIDAR and computer vision for Self-Driving Cars vision.srt
4. What is Neuron.srt
7. Important Terms in this course.mp4
9. Introduction to Self-Driving Cars.mp4
5. What is Multilayer Neural Network.mp4
6. What is keras (Optional from AI in Healthcare course).mp4
12. Deep learning and computer vision approaches for Self-Driving Cars.mp4
11. Building the safe systems.mp4
10. Benefit of Self-Driving Cars.mp4
1. Course structure.mp4
3. What is ANN.mp4
13. LIDAR and computer vision for Self-Driving Cars vision.mp4
2. How To Make The Most Out Of This Course.mp4
4. What is Neuron.mp4
8. Important note about tools in this course.mp4
7. Detecting Pedestrian Project
6. MeanShift Introduction.srt
3. MOG background subtractor.srt
8. Implementing pedestrians detection Part 1.srt
10. Implementing pedestrians detection Part 3.srt
11. Implementing pedestrians detection Part 4.srt
9. Implementing pedestrians detection Part 2.srt
9.1 Udemy_Detecting_pedestrian (1).ipynb
5. Detecting pedestrians Introduction.srt
4. KNN background subtractor.srt
2. Background subtraction.srt
7. Kalman filter.srt
7.1 Udemy_Detecting_pedestrian.ipynb
12. Summary of the section.srt
1. Introduction to tracking objects.srt
3. MOG background subtractor.mp4
8. Implementing pedestrians detection Part 1.mp4
10. Implementing pedestrians detection Part 3.mp4
11. Implementing pedestrians detection Part 4.mp4
9. Implementing pedestrians detection Part 2.mp4
3.1 Udemy_MOG_background_subtractor.ipynb
6. MeanShift Introduction.mp4
4.1 Udemy_KNN_background_subtractor.ipynb
7. Kalman filter.mp4
5. Detecting pedestrians Introduction.mp4
4. KNN background subtractor.mp4
11.1 Udemy_Detecting_pedestrian (2).ipynb
2. Background subtraction.mp4
12. Summary of the section.mp4
1. Introduction to tracking objects.mp4
3.2 hallway.mpg
4.2 traffic.flv
5. Detection of road markings by OpenCV
7. Optimizing the detected road markings.srt
8. Detecting road markings in a video.srt
6. Applying the Hough transform.srt
9. Summary of the section.srt
7. Optimizing the detected road markings.mp4
2. Loading the image using OpenCV and Converting the image into grayscale.srt
4. Masking the region of interest.srt
3. Smoothing the image and Implementing Canny Edge detection.srt
5. Applying bitwise_and.srt
1. Introduction to the project.srt
8.1 Udemy_road_markings_videos.ipynb
8. Detecting road markings in a video.mp4
6. Applying the Hough transform.mp4
2. Loading the image using OpenCV and Converting the image into grayscale.mp4
4. Masking the region of interest.mp4
8.2 test2.mp4
3. Smoothing the image and Implementing Canny Edge detection.mp4
5. Applying bitwise_and.mp4
9. Summary of the section.mp4
1. Introduction to the project.mp4
7.1 Udemy_road_markings (2).ipynb
6.1 Udemy_road_markings (1).ipynb
3.1 Udemy_road_markings.ipynb
8. Semantic Segmentation
13. Semantic segmentation Implementation Part 4.srt
10. Semantic segmentation Implementation Part 1.srt
13. Semantic segmentation Implementation Part 4.mp4
11. Semantic segmentation Implementation Part 2.srt
14.1 Udemy_Semantic_Segmentation.ipynb
12.1 Udemy_Semantic_Segmentation.ipynb
14. Semantic segmentation Implementation Part 5.srt
9. E-Net.srt
12. Semantic segmentation Implementation Part 3.srt
4. U-NET.srt
1. Introduction to semantic segmentation.srt
2. Semantic Segmentation Achitecture.srt
7. Pyramid Scene Parsing Network.srt
8. DeepLabv3+.srt
5. SegNet.srt
15. Summary of the section.srt
6. Encoder and Decoder.srt
3. Different Semantic Segmentation Architectures.srt
11. Semantic segmentation Implementation Part 2.mp4
10. Semantic segmentation Implementation Part 1.mp4
14. Semantic segmentation Implementation Part 5.mp4
12. Semantic segmentation Implementation Part 3.mp4
9. E-Net.mp4
2. Semantic Segmentation Achitecture.mp4
7. Pyramid Scene Parsing Network.mp4
4. U-NET.mp4
1. Introduction to semantic segmentation.mp4
5. SegNet.mp4
8. DeepLabv3+.mp4
15. Summary of the section.mp4
6. Encoder and Decoder.mp4
3. Different Semantic Segmentation Architectures.mp4
13.1 enet-cityscapes.zip
3. Basic Deep Learning Project
7. Training the model.srt
2. Importing Data and Libraries.srt
3. Splitting the dataset into training test and test set.srt
6. Building and compiling the model.srt
11.1 Udemy_Auto_mpg.ipynb
9. Evaluating the model's performance.srt
10. Saving and loading models.srt
8. Predicting new, unseen data.srt
4. Visualizing data.srt
1. Introduction to the project.srt
11. Summary of the project.srt
5. Standardizing data.srt
7. Training the model.mp4
2. Importing Data and Libraries.mp4
4. Visualizing data.mp4
6. Building and compiling the model.mp4
11. Summary of the project.mp4
1. Introduction to the project.mp4
8. Predicting new, unseen data.mp4
3. Splitting the dataset into training test and test set.mp4
5. Standardizing data.mp4
9. Evaluating the model's performance.mp4
10. Saving and loading models.mp4
2. Activation function
1. What is activation function.srt
2. What is Rectified Linear Unit function.srt
3. What is Leaky ReLU function.srt
4. What is tanh function.srt
5. What is Softmax function.srt
6. What is The Exponential linear unit function.srt
7. What is Swish function.srt
8. What is sigmoid function.srt
9. Activation Function Implementation.srt
9. Activation Function Implementation.mp4
1. What is activation function.mp4
2. What is Rectified Linear Unit function.mp4
7. What is Swish function.mp4
8. What is sigmoid function.mp4
5. What is Softmax function.mp4
4. What is tanh function.mp4
3. What is Leaky ReLU function.mp4
6. What is The Exponential linear unit function.mp4
10. Thank you
1. Thank you.srt
1. Thank you.mp4
TutsNode.com.txt
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