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| [TGx]Downloaded from torrentgalaxy.to .txt |
585б |
| 0 |
100б |
| 1 |
221б |
| 1. Course structure.mp4 |
19.26Мб |
| 1. Course structure.srt |
3.77Кб |
| 1. Introduction.mp4 |
24.35Мб |
| 1. Introduction.mp4 |
10.73Мб |
| 1. Introduction.srt |
2.58Кб |
| 1. Introduction.srt |
3.54Кб |
| 1. Introduction to convolution neural network.mp4 |
44.18Мб |
| 1. Introduction to convolution neural network.srt |
7.74Кб |
| 1. Introduction to semantic segmentation.mp4 |
12.78Мб |
| 1. Introduction to semantic segmentation.srt |
2.71Кб |
| 1. Introduction to the project.mp4 |
28.62Мб |
| 1. Introduction to the project.mp4 |
7.65Мб |
| 1. Introduction to the project.srt |
4.30Кб |
| 1. Introduction to the project.srt |
1.85Кб |
| 1. Introduction to tracking objects.mp4 |
7.10Мб |
| 1. Introduction to tracking objects.srt |
1.42Кб |
| 1. Thank you.mp4 |
23.30Мб |
| 1. Thank you.srt |
1.62Кб |
| 1. What is activation function.mp4 |
7.61Мб |
| 1. What is activation function.srt |
1.45Кб |
| 10 |
20.21Кб |
| 10. Benefit of Self-Driving Cars.mp4 |
24.70Мб |
| 10. Benefit of Self-Driving Cars.srt |
5.34Кб |
| 10. Implementing pedestrians detection Part 3.mp4 |
105.93Мб |
| 10. Implementing pedestrians detection Part 3.srt |
10.08Кб |
| 10. Introduction to RGB space.mp4 |
4.16Мб |
| 10. Introduction to RGB space.srt |
635б |
| 10. Saving and loading models.mp4 |
11.93Мб |
| 10. Saving and loading models.srt |
1.97Кб |
| 10. Semantic segmentation Implementation Part 1.mp4 |
83.92Мб |
| 10. Semantic segmentation Implementation Part 1.srt |
10.90Кб |
| 10. Summary of the project.mp4 |
4.24Мб |
| 10. Summary of the project.srt |
1.06Кб |
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227.63Кб |
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76.10Кб |
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241.89Кб |
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| 11 |
617.11Кб |
| 11.1 Udemy_Auto_mpg.ipynb |
280.43Кб |
| 11.1 Udemy_Detecting_pedestrian (2).ipynb |
47.67Мб |
| 11. Building the safe systems.mp4 |
27.17Мб |
| 11. Building the safe systems.srt |
5.87Кб |
| 11. Implementing pedestrians detection Part 4.mp4 |
98.83Мб |
| 11. Implementing pedestrians detection Part 4.srt |
9.23Кб |
| 11. Introduction to HSV space.mp4 |
15.79Мб |
| 11. Introduction to HSV space.srt |
2.54Кб |
| 11. Semantic segmentation Implementation Part 2.mp4 |
92.23Мб |
| 11. Semantic segmentation Implementation Part 2.srt |
8.64Кб |
| 11. Summary of the project.mp4 |
34.53Мб |
| 11. Summary of the project.srt |
4.19Кб |
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174.88Кб |
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| 12 |
349.35Кб |
| 12.1 Udemy_Semantic_Segmentation.ipynb |
588.52Кб |
| 12. Deep learning and computer vision approaches for Self-Driving Cars.mp4 |
28.49Мб |
| 12. Deep learning and computer vision approaches for Self-Driving Cars.srt |
5.00Кб |
| 12. Introduction to Color space manipulation.mp4 |
5.92Мб |
| 12. Introduction to Color space manipulation.srt |
1.11Кб |
| 12. Semantic segmentation Implementation Part 3.mp4 |
39.98Мб |
| 12. Semantic segmentation Implementation Part 3.srt |
3.69Кб |
| 12. Summary of the section.mp4 |
7.99Мб |
| 12. Summary of the section.srt |
1.61Кб |
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| 13 |
72.88Кб |
| 13.1 enet-cityscapes.zip |
2.65Мб |
| 13. Implementing Color space manipulation Part 1.mp4 |
54.01Мб |
| 13. Implementing Color space manipulation Part 1.srt |
5.92Кб |
| 13. LIDAR and computer vision for Self-Driving Cars vision.mp4 |
15.94Мб |
| 13. LIDAR and computer vision for Self-Driving Cars vision.srt |
3.10Кб |
| 13. Semantic segmentation Implementation Part 4.mp4 |
199.87Мб |
| 13. Semantic segmentation Implementation Part 4.srt |
17.23Кб |
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202.45Кб |
| 14.1 Udemy_Semantic_Segmentation.ipynb |
198.14Мб |
| 14. Implementing Color space manipulation Part 2.mp4 |
121.66Мб |
| 14. Implementing Color space manipulation Part 2.srt |
8.74Кб |
| 14. Semantic segmentation Implementation Part 5.mp4 |
54.46Мб |
| 14. Semantic segmentation Implementation Part 5.srt |
5.46Кб |
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768.25Кб |
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414.64Кб |
| 15.1 Udemy_Converting_images_from_RGB_to_grayscale.ipynb |
15.73Мб |
| 15. Implementing Color space manipulation Part 3.mp4 |
49.90Мб |
| 15. Implementing Color space manipulation Part 3.srt |
3.42Кб |
| 15. Summary of the section.mp4 |
7.19Мб |
| 15. Summary of the section.srt |
1.67Кб |
| 16 |
76.55Кб |
| 16. Introduction to convolution.mp4 |
26.14Мб |
| 16. Introduction to convolution.srt |
4.46Кб |
| 17 |
80.84Кб |
| 17. Introduction to Sharpening and blurring.mp4 |
16.51Мб |
| 17. Introduction to Sharpening and blurring.srt |
3.22Кб |
| 18 |
615.71Кб |
| 18.1 Udemy_Converting_images_from_RGB_to_grayscale (1).ipynb |
22.73Мб |
| 18. Sharpening and blurring Implementation.mp4 |
109.60Мб |
| 18. Sharpening and blurring Implementation.srt |
7.90Кб |
| 19 |
179.05Кб |
| 19. Introduction to Edge detection and gradient calculation.mp4 |
13.25Мб |
| 19. Introduction to Edge detection and gradient calculation.srt |
2.51Кб |
| 2 |
414б |
| 2. Background subtraction.mp4 |
25.52Мб |
| 2. Background subtraction.srt |
5.31Кб |
| 2. Computer vision Introduction.mp4 |
14.35Мб |
| 2. Computer vision Introduction.srt |
2.54Кб |
| 2. Convolution Layers.mp4 |
16.16Мб |
| 2. Convolution Layers.srt |
4.12Кб |
| 2. How To Make The Most Out Of This Course.mp4 |
8.20Мб |
| 2. How To Make The Most Out Of This Course.srt |
2.45Кб |
| 2. Importing Data and Libraries.mp4 |
78.47Мб |
| 2. Importing Data and Libraries.srt |
8.24Кб |
| 2. Loading the image using OpenCV and Converting the image into grayscale.mp4 |
50.99Мб |
| 2. Loading the image using OpenCV and Converting the image into grayscale.srt |
5.58Кб |
| 2. Semantic Segmentation Achitecture.mp4 |
15.97Мб |
| 2. Semantic Segmentation Achitecture.srt |
2.71Кб |
| 2. What is Rectified Linear Unit function.mp4 |
4.31Мб |
| 2. What is Rectified Linear Unit function.srt |
1.71Кб |
| 2. What makes YOLO different.mp4 |
15.83Мб |
| 2. What makes YOLO different.srt |
2.17Кб |
| 20 |
785.73Кб |
| 20. Introduction to Sobel.mp4 |
11.59Мб |
| 20. Introduction to Sobel.srt |
2.26Кб |
| 21 |
465.52Кб |
| 21. Introduction to Laplacian edge detector.mp4 |
8.88Мб |
| 21. Introduction to Laplacian edge detector.srt |
1.45Кб |
| 22 |
377.27Кб |
| 22.1 Chapter_4_Code_Notebook.ipynb |
30.00Мб |
| 22. Canny edge detection.mp4 |
80.71Мб |
| 22. Canny edge detection.srt |
5.25Кб |
| 23 |
285.21Кб |
| 23. Application of image transformation.mp4 |
6.09Мб |
| 23. Application of image transformation.srt |
1.50Кб |
| 24 |
79.73Кб |
| 24. Introduction to Affine and Projective transformation.mp4 |
14.30Мб |
| 24. Introduction to Affine and Projective transformation.srt |
2.41Кб |
| 25 |
293.80Кб |
| 25. Image rotation Implementation.mp4 |
39.07Мб |
| 25. Image rotation Implementation.srt |
3.71Кб |
| 26 |
547.46Кб |
| 26.1 Udemy_Converting_images_from_RGB_to_grayscale.ipynb |
25.53Мб |
| 26. Image translation Implementation.mp4 |
36.70Мб |
| 26. Image translation Implementation.srt |
3.85Кб |
| 27 |
712.33Кб |
| 27.1 Udemy_Converting_images_from_RGB_to_grayscale.ipynb |
25.65Мб |
| 27. Image resizing Implementation.mp4 |
23.55Мб |
| 27. Image resizing Implementation.srt |
2.87Кб |
| 28 |
113.52Кб |
| 28. Introduction to Perspective transformation.mp4 |
8.38Мб |
| 28. Introduction to Perspective transformation.srt |
1.49Кб |
| 29 |
428.14Кб |
| 29.1 Udemy_Converting_images_from_RGB_to_grayscale (1).ipynb |
26.50Мб |
| 29. Perspective transformation Implementation.mp4 |
65.89Мб |
| 29. Perspective transformation Implementation.srt |
6.71Кб |
| 3 |
2б |
| 3.1 Udemy_MOG_background_subtractor.ipynb |
86.72Мб |
| 3.1 Udemy_road_markings.ipynb |
1.21Мб |
| 3.2 hallway.mpg |
2.73Мб |
| 3. Challenges in Computer Vision.mp4 |
24.91Мб |
| 3. Challenges in Computer Vision.srt |
3.88Кб |
| 3. Different Semantic Segmentation Architectures.mp4 |
4.69Мб |
| 3. Different Semantic Segmentation Architectures.srt |
1.05Кб |
| 3. MOG background subtractor.mp4 |
133.98Мб |
| 3. MOG background subtractor.srt |
14.13Кб |
| 3. Pooling Layers.mp4 |
20.86Мб |
| 3. Pooling Layers.srt |
4.00Кб |
| 3. Smoothing the image and Implementing Canny Edge detection.mp4 |
29.88Мб |
| 3. Smoothing the image and Implementing Canny Edge detection.srt |
3.96Кб |
| 3. Splitting the dataset into training test and test set.mp4 |
23.85Мб |
| 3. Splitting the dataset into training test and test set.srt |
2.70Кб |
| 3. The YOLO loss function.mp4 |
10.18Мб |
| 3. The YOLO loss function.srt |
1.63Кб |
| 3. What is ANN.mp4 |
18.25Мб |
| 3. What is ANN.srt |
4.23Кб |
| 3. What is Leaky ReLU function.mp4 |
2.25Мб |
| 3. What is Leaky ReLU function.srt |
844б |
| 30 |
439.46Кб |
| 30.1 Udemy_Converting_images_from_RGB_to_grayscale (2).ipynb |
27.16Мб |
| 30. Cropping, dilating, and eroding an image Implementation.mp4 |
50.17Мб |
| 30. Cropping, dilating, and eroding an image Implementation.srt |
5.45Кб |
| 31 |
316.88Кб |
| 31.1 Udemy_Converting_images_from_RGB_to_grayscale (3).ipynb |
10.56Мб |
| 31. Masking regions of interest.mp4 |
91.55Мб |
| 31. Masking regions of interest.srt |
9.21Кб |
| 32 |
91.66Кб |
| 32. Introduction to The Hough transform.mp4 |
33.15Мб |
| 32. Introduction to The Hough transform.srt |
4.88Кб |
| 33 |
1022.82Кб |
| 33.1 The_Hough_transform.ipynb |
50.72Кб |
| 33. The Hough transform Implementation.mp4 |
59.69Мб |
| 33. The Hough transform Implementation.srt |
6.74Кб |
| 34 |
557.01Кб |
| 34. Summary of the section.mp4 |
6.46Мб |
| 34. Summary of the section.srt |
1.60Кб |
| 35 |
1017.96Кб |
| 36 |
135.55Кб |
| 37 |
479.72Кб |
| 38 |
896.47Кб |
| 39 |
821.08Кб |
| 4 |
83б |
| 4.1 Udemy_KNN_background_subtractor.ipynb |
62.58Мб |
| 4.2 traffic.flv |
2.39Мб |
| 4. Introduction to the project.mp4 |
36.94Мб |
| 4. Introduction to the project.srt |
4.99Кб |
| 4. KNN background subtractor.mp4 |
48.70Мб |
| 4. KNN background subtractor.srt |
5.63Кб |
| 4. Masking the region of interest.mp4 |
40.11Мб |
| 4. Masking the region of interest.srt |
4.97Кб |
| 4. Requirement of Self-Driving Cars.mp4 |
24.91Мб |
| 4. Requirement of Self-Driving Cars.srt |
3.88Кб |
| 4. The YOLO architecture.mp4 |
8.96Мб |
| 4. The YOLO architecture.srt |
1.73Кб |
| 4. U-NET.mp4 |
12.80Мб |
| 4. U-NET.srt |
2.88Кб |
| 4. Visualizing data.mp4 |
61.57Мб |
| 4. Visualizing data.srt |
4.88Кб |
| 4. What is Neuron.mp4 |
7.22Мб |
| 4. What is Neuron.srt |
1.79Кб |
| 4. What is tanh function.mp4 |
2.40Мб |
| 4. What is tanh function.srt |
1.12Кб |
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849.12Кб |
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97.58Кб |
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307.94Кб |
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727.21Кб |
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909.13Кб |
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19.69Кб |
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| 5 |
725.70Кб |
| 5.1 traffic-signs-data-20210225T074843Z-001.zip |
117.80Мб |
| 5.1 yolo.h5 |
237.19Мб |
| 5.2 coco_classes.txt |
625б |
| 5.3 images3-20210325T032710Z-001.zip |
346.71Кб |
| 5.4 Udemy_Image_YOLO_Implementation.ipynb |
10.08Кб |
| 5. Applying bitwise_and.mp4 |
22.89Мб |
| 5. Applying bitwise_and.srt |
2.76Кб |
| 5. Detecting pedestrians Introduction.mp4 |
51.20Мб |
| 5. Detecting pedestrians Introduction.srt |
7.25Кб |
| 5. Digital representation of an image.mp4 |
31.26Мб |
| 5. Digital representation of an image.srt |
5.41Кб |
| 5. Loading data.mp4 |
56.00Мб |
| 5. Loading data.srt |
7.72Кб |
| 5. SegNet.mp4 |
10.76Мб |
| 5. SegNet.srt |
1.98Кб |
| 5. Standardizing data.mp4 |
14.46Мб |
| 5. Standardizing data.srt |
2.10Кб |
| 5. What is Multilayer Neural Network.mp4 |
40.29Мб |
| 5. What is Multilayer Neural Network.srt |
6.93Кб |
| 5. What is Softmax function.mp4 |
2.41Мб |
| 5. What is Softmax function.srt |
1.23Кб |
| 5. YOLO Implementation Part 1.mp4 |
177.40Мб |
| 5. YOLO Implementation Part 1.srt |
19.07Кб |
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| 6 |
40.29Кб |
| 6.1 Udemy_Converting_images_from_RGB_to_grayscale.ipynb |
236.17Кб |
| 6.1 Udemy_road_markings (1).ipynb |
1.91Мб |
| 6.1 Udemy_YOLO_detection_video.ipynb |
178.96Мб |
| 6.2 images2-20210319T024702Z-001.zip |
638.45Кб |
| 6.2 video_sample.mp4 |
8.71Мб |
| 6. Applying the Hough transform.mp4 |
53.87Мб |
| 6. Applying the Hough transform.srt |
6.48Кб |
| 6. Building and compiling the model.mp4 |
53.12Мб |
| 6. Building and compiling the model.srt |
6.17Кб |
| 6. Converting images from RGB to grayscale.mp4 |
37.49Мб |
| 6. Converting images from RGB to grayscale.srt |
5.53Кб |
| 6. Encoder and Decoder.mp4 |
5.06Мб |
| 6. Encoder and Decoder.srt |
1.18Кб |
| 6. Exploring image.mp4 |
6.43Мб |
| 6. Exploring image.srt |
1.31Кб |
| 6. MeanShift Introduction.mp4 |
72.30Мб |
| 6. MeanShift Introduction.srt |
7.30Кб |
| 6. What is keras (Optional from AI in Healthcare course).mp4 |
34.08Мб |
| 6. What is keras (Optional from AI in Healthcare course).srt |
6.05Кб |
| 6. What is The Exponential linear unit function.mp4 |
1.86Мб |
| 6. What is The Exponential linear unit function.srt |
822б |
| 6. YOLO Implementation Part 2.mp4 |
329.85Мб |
| 6. YOLO Implementation Part 2.srt |
23.17Кб |
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760.03Кб |
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616.69Кб |
| 7.1 Udemy_Converting_images_from_RGB_to_grayscale (1).ipynb |
259.59Кб |
| 7.1 Udemy_Detecting_pedestrian.ipynb |
2.58Кб |
| 7.1 Udemy_road_markings (2).ipynb |
2.58Мб |
| 7. Data Preperation.mp4 |
38.89Мб |
| 7. Data Preperation.srt |
5.47Кб |
| 7. Detection with the grayscale image.mp4 |
31.63Мб |
| 7. Detection with the grayscale image.srt |
4.50Кб |
| 7. Important Terms in this course.mp4 |
105.92Мб |
| 7. Important Terms in this course.srt |
11.16Кб |
| 7. Kalman filter.mp4 |
53.53Мб |
| 7. Kalman filter.srt |
4.49Кб |
| 7. Optimizing the detected road markings.mp4 |
193.40Мб |
| 7. Optimizing the detected road markings.srt |
17.77Кб |
| 7. Pyramid Scene Parsing Network.mp4 |
12.93Мб |
| 7. Pyramid Scene Parsing Network.srt |
2.21Кб |
| 7. Training the model.mp4 |
124.40Мб |
| 7. Training the model.srt |
12.03Кб |
| 7. What is Swish function.mp4 |
3.75Мб |
| 7. What is Swish function.srt |
1.92Кб |
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767.46Кб |
| 8.1 Udemy_Converting_images_from_RGB_to_grayscale (1).ipynb |
259.59Кб |
| 8.1 Udemy_road_markings_videos.ipynb |
156.25Мб |
| 8.1 Udemy_Traffic_sign.ipynb |
49.27Кб |
| 8.2 test2.mp4 |
31.93Мб |
| 8. DeepLabv3+.mp4 |
10.18Мб |
| 8. DeepLabv3+.srt |
2.12Кб |
| 8. Detecting road markings in a video.mp4 |
150.21Мб |
| 8. Detecting road markings in a video.srt |
16.44Кб |
| 8. Implementing pedestrians detection Part 1.mp4 |
119.93Мб |
| 8. Implementing pedestrians detection Part 1.srt |
11.63Кб |
| 8. Important note about tools in this course.mp4 |
5.27Мб |
| 8. Important note about tools in this course.srt |
2.18Кб |
| 8. Predicting new, unseen data.mp4 |
24.36Мб |
| 8. Predicting new, unseen data.srt |
3.25Кб |
| 8. QUICK FIX Video.mp4 |
8.83Мб |
| 8. QUICK FIX Video.srt |
977б |
| 8. Training model.mp4 |
105.40Мб |
| 8. Training model.srt |
10.74Кб |
| 8. What is sigmoid function.mp4 |
3.25Мб |
| 8. What is sigmoid function.srt |
1.50Кб |
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719.00Кб |
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115.91Кб |
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275.00Кб |
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146.98Кб |
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806.91Кб |
| 9.1 Udemy_Detecting_pedestrian (1).ipynb |
8.19Кб |
| 9.1 Udemy_Traffic_sign (1).ipynb |
236.65Кб |
| 9. Activation Function Implementation.mp4 |
36.90Мб |
| 9. Activation Function Implementation.srt |
6.85Кб |
| 9. E-Net.mp4 |
23.76Мб |
| 9. E-Net.srt |
4.18Кб |
| 9. Evaluating the model's performance.mp4 |
13.94Мб |
| 9. Evaluating the model's performance.srt |
2.05Кб |
| 9. Implementing pedestrians detection Part 2.mp4 |
88.63Мб |
| 9. Implementing pedestrians detection Part 2.srt |
8.72Кб |
| 9. Introduction to Self-Driving Cars.mp4 |
58.91Мб |
| 9. Introduction to Self-Driving Cars.srt |
9.02Кб |
| 9. Introduction to the Color space techniques.mp4 |
7.25Мб |
| 9. Introduction to the Color space techniques.srt |
1.31Кб |
| 9. Model accuracy.mp4 |
181.35Мб |
| 9. Model accuracy.srt |
18.56Кб |
| 9. Summary of the section.mp4 |
7.99Мб |
| 9. Summary of the section.srt |
1.80Кб |
| 90 |
173.98Кб |
| 91 |
212.88Кб |
| 92 |
273.98Кб |
| 93 |
553.06Кб |
| 94 |
660.77Кб |
| 95 |
715.77Кб |
| 96 |
60.67Кб |
| 97 |
767.93Кб |
| 98 |
70.60Кб |
| 99 |
205.31Кб |
| TutsNode.com.txt |
63б |