Deep Learning for Vision Systems Video Edition
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| Torrent Hash : | C3C76C01041D1771F09B8A2766015E244EE945A3 |
| Torrent Added : | at July 31, 2025, 12:42 a.m. in Other |
| Torrent Size : | 2.6 GB |
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Deep Learning for Vision Systems Video Edition
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Deep Learning for Vision Systems Video Edition
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001. Part 1. Deep learning foundation.en.srt -
001. Part 1. Deep learning foundation.mp4 -
002. Chapter 1. Welcome to computer vision.en.srt -
002. Chapter 1. Welcome to computer vision.mp4 -
003. Chapter 1. Applications of computer vision.en.srt -
003. Chapter 1. Applications of computer vision.mp4 -
004. Chapter 1. Computer vision pipeline - The big picture.en.srt -
004. Chapter 1. Computer vision pipeline - The big picture.mp4 -
005. Chapter 1. Image input.en.srt -
005. Chapter 1. Image input.mp4 -
006. Chapter 1. Image preprocessing.en.srt -
006. Chapter 1. Image preprocessing.mp4 -
007. Chapter 1. Feature extraction.en.srt -
007. Chapter 1. Feature extraction.mp4 -
008. Chapter 1. Classifier learning algorithm.en.srt -
008. Chapter 1. Classifier learning algorithm.mp4 -
009. Chapter 1. Summary.en.srt -
009. Chapter 1. Summary.mp4 -
010. Chapter 2. Deep learning and neural networks.en.srt -
010. Chapter 2. Deep learning and neural networks.mp4 -
011. Chapter 2. Multilayer perceptrons.en.srt -
011. Chapter 2. Multilayer perceptrons.mp4 -
012. Chapter 2. Activation functions.en.srt -
012. Chapter 2. Activation functions.mp4 -
013. Chapter 2. The feedforward process.en.srt -
013. Chapter 2. The feedforward process.mp4 -
014. Chapter 2. Error functions.en.srt -
014. Chapter 2. Error functions.mp4 -
015. Chapter 2. Optimization algorithms.en.srt -
015. Chapter 2. Optimization algorithms.mp4 -
016. Chapter 2. Backpropagation.en.srt -
016. Chapter 2. Backpropagation.mp4 -
017. Chapter 2. Summary.en.srt -
017. Chapter 2. Summary.mp4 -
018. Chapter 3. Convolutional neural networks.en.srt -
018. Chapter 3. Convolutional neural networks.mp4 -
019. Chapter 3. CNN architecture.en.srt -
019. Chapter 3. CNN architecture.mp4 -
020. Chapter 3. Basic components of a CNN.en.srt -
020. Chapter 3. Basic components of a CNN.mp4 -
021. Chapter 3. Image classification using CNNs.en.srt -
021. Chapter 3. Image classification using CNNs.mp4 -
022. Chapter 3. Adding dropout layers to avoid overfitting.en.srt -
022. Chapter 3. Adding dropout layers to avoid overfitting.mp4 -
023. Chapter 3. Convolution over color images (3D images).en.srt -
023. Chapter 3. Convolution over color images (3D images).mp4 -
024. Chapter 3. Project - Image classification for color images.en.srt -
024. Chapter 3. Project - Image classification for color images.mp4 -
025. Chapter 3. Summary.en.srt -
025. Chapter 3. Summary.mp4 -
026. Chapter 4. Structuring DL projects and hyperparameter tuning.en.srt -
026. Chapter 4. Structuring DL projects and hyperparameter tuning.mp4 -
027. Chapter 4. Designing a baseline model.en.srt -
027. Chapter 4. Designing a baseline model.mp4 -
028. Chapter 4. Getting your data ready for training.en.srt -
028. Chapter 4. Getting your data ready for training.mp4 -
029. Chapter 4. Evaluating the model and interpreting its performance.en.srt -
029. Chapter 4. Evaluating the model and interpreting its performance.mp4 -
030. Chapter 4. Improving the network and tuning hyperparameters.en.srt -
030. Chapter 4. Improving the network and tuning hyperparameters.mp4 -
031. Chapter 4. Learning and optimization.en.srt -
031. Chapter 4. Learning and optimization.mp4 -
032. Chapter 4. Optimization algorithms.en.srt -
032. Chapter 4. Optimization algorithms.mp4 -
033. Chapter 4. Regularization techniques to avoid overfitting.en.srt -
033. Chapter 4. Regularization techniques to avoid overfitting.mp4 -
034. Chapter 4. Batch normalization.en.srt -
034. Chapter 4. Batch normalization.mp4 -
035. Chapter 4. Project - Achieve high accuracy on image classification.en.srt -
035. Chapter 4. Project - Achieve high accuracy on image classification.mp4 -
036. Chapter 4. Summary.en.srt -
036. Chapter 4. Summary.mp4 -
037. Part 2. Image classification and detection.en.srt -
037. Part 2. Image classification and detection.mp4 -
038. Chapter 5. Advanced CNN architectures.en.srt -
038. Chapter 5. Advanced CNN architectures.mp4 -
039. Chapter 5. LeNet-5.en.srt -
039. Chapter 5. LeNet-5.mp4 -
040. Chapter 5. AlexNet.en.srt -
040. Chapter 5. AlexNet.mp4 -
041. Chapter 5. VGGNet.en.srt -
041. Chapter 5. VGGNet.mp4 -
042. Chapter 5. Inception and GoogLeNet.en.srt -
042. Chapter 5. Inception and GoogLeNet.mp4 -
043. Chapter 5. ResNet.en.srt -
043. Chapter 5. ResNet.mp4 -
044. Chapter 5. Summary.en.srt -
044. Chapter 5. Summary.mp4 -
045. Chapter 6. Transfer learning.en.srt -
045. Chapter 6. Transfer learning.mp4 -
046. Chapter 6. What is transfer learning.en.srt -
046. Chapter 6. What is transfer learning.mp4 -
047. Chapter 6. How transfer learning works.en.srt -
047. Chapter 6. How transfer learning works.mp4 -
048. Chapter 6. Transfer learning approaches.en.srt -
048. Chapter 6. Transfer learning approaches.mp4 -
049. Chapter 6. Choosing the appropriate level of transfer learning.en.srt -
049. Chapter 6. Choosing the appropriate level of transfer learning.mp4 -
050. Chapter 6. Open source datasets.en.srt -
050. Chapter 6. Open source datasets.mp4 -
051. Chapter 6. Project 1 - A pretrained network as a feature extractor.en.srt -
051. Chapter 6. Project 1 - A pretrained network as a feature extractor.mp4 -
052. Chapter 6. Project 2 - Fine-tuning.en.srt -
052. Chapter 6. Project 2 - Fine-tuning.mp4 -
053. Chapter 6. Summary.en.srt -
053. Chapter 6. Summary.mp4 -
054. Chapter 7. Object detection with R-CNN, SSD, and YOLO.en.srt -
054. Chapter 7. Object detection with R-CNN, SSD, and YOLO.mp4 -
055. Chapter 7. Region-based convolutional neural networks (R-CNNs).en.srt -
055. Chapter 7. Region-based convolutional neural networks (R-CNNs).mp4 -
056. Chapter 7. Single-shot detector (SSD).en.srt -
056. Chapter 7. Single-shot detector (SSD).mp4 -
057. Chapter 7. You only look once (YOLO).en.srt -
057. Chapter 7. You only look once (YOLO).mp4 -
058. Chapter 7. Project - Train an SSD network in a self-driving car application.en.srt -
058. Chapter 7. Project - Train an SSD network in a self-driving car application.mp4 -
059. Chapter 7. Summary.en.srt -
059. Chapter 7. Summary.mp4 -
060. Part 3. Generative models and visual embeddings.en.srt -
060. Part 3. Generative models and visual embeddings.mp4 -
061. Chapter 8. Generative adversarial networks (GANs).en.srt -
061. Chapter 8. Generative adversarial networks (GANs).mp4 -
062. Chapter 8. Evaluating GAN models.en.srt -
062. Chapter 8. Evaluating GAN models.mp4 -
063. Chapter 8. Popular GAN applications.en.srt -
063. Chapter 8. Popular GAN applications.mp4 -
064. Chapter 8. Project - Building your own GAN.en.srt -
064. Chapter 8. Project - Building your own GAN.mp4 -
065. Chapter 8. Summary.en.srt -
065. Chapter 8. Summary.mp4 -
066. Chapter 9. DeepDream and neural style transfer.en.srt -
066. Chapter 9. DeepDream and neural style transfer.mp4 -
067. Chapter 9. DeepDream.en.srt -
067. Chapter 9. DeepDream.mp4 -
068. Chapter 9. Neural style transfer.en.srt -
068. Chapter 9. Neural style transfer.mp4 -
069. Chapter 9. Summary.en.srt -
069. Chapter 9. Summary.mp4 -
070. Chapter 10. Visual embeddings.en.srt -
070. Chapter 10. Visual embeddings.mp4 -
071. Chapter 10. Learning embedding.en.srt -
071. Chapter 10. Learning embedding.mp4 -
072. Chapter 10. Loss functions.en.srt -
072. Chapter 10. Loss functions.mp4 -
073. Chapter 10. Mining informative data.en.srt -
073. Chapter 10. Mining informative data.mp4 -
074. Chapter 10. Project - Train an embedding network.en.srt -
074. Chapter 10. Project - Train an embedding network.mp4 -
075. Chapter 10. Pushing the boundaries of current accuracy.en.srt -
075. Chapter 10. Pushing the boundaries of current accuracy.mp4 -
076. Chapter 10. Summary.en.srt -
076. Chapter 10. Summary.mp4 -
Bonus Resources.txt -
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Get Bonus Downloads Here.url -
180 bytes
001. Part 1. Deep learning foundation.en.srt -
1.6 KB
001. Part 1. Deep learning foundation.mp4 -
7.2 MB
002. Chapter 1. Welcome to computer vision.en.srt -
24.5 KB
002. Chapter 1. Welcome to computer vision.mp4 -
42.8 MB
003. Chapter 1. Applications of computer vision.en.srt -
15.0 KB
003. Chapter 1. Applications of computer vision.mp4 -
44.5 MB
004. Chapter 1. Computer vision pipeline - The big picture.en.srt -
7.7 KB
004. Chapter 1. Computer vision pipeline - The big picture.mp4 -
23.0 MB
005. Chapter 1. Image input.en.srt -
11.3 KB
005. Chapter 1. Image input.mp4 -
26.9 MB
006. Chapter 1. Image preprocessing.en.srt -
10.2 KB
006. Chapter 1. Image preprocessing.mp4 -
20.0 MB
007. Chapter 1. Feature extraction.en.srt -
15.5 KB
007. Chapter 1. Feature extraction.mp4 -
44.8 MB
008. Chapter 1. Classifier learning algorithm.en.srt -
3.0 KB
008. Chapter 1. Classifier learning algorithm.mp4 -
5.5 MB
009. Chapter 1. Summary.en.srt -
1.2 KB
009. Chapter 1. Summary.mp4 -
3.0 MB
010. Chapter 2. Deep learning and neural networks.en.srt -
25.1 KB
010. Chapter 2. Deep learning and neural networks.mp4 -
58.3 MB
011. Chapter 2. Multilayer perceptrons.en.srt -
18.2 KB
011. Chapter 2. Multilayer perceptrons.mp4 -
62.5 MB
012. Chapter 2. Activation functions.en.srt -
21.9 KB
012. Chapter 2. Activation functions.mp4 -
53.7 MB
013. Chapter 2. The feedforward process.en.srt -
13.0 KB
013. Chapter 2. The feedforward process.mp4 -
41.0 MB
014. Chapter 2. Error functions.en.srt -
15.4 KB
014. Chapter 2. Error functions.mp4 -
27.5 MB
015. Chapter 2. Optimization algorithms.en.srt -
28.7 KB
015. Chapter 2. Optimization algorithms.mp4 -
85.0 MB
016. Chapter 2. Backpropagation.en.srt -
8.5 KB
016. Chapter 2. Backpropagation.mp4 -
18.8 MB
017. Chapter 2. Summary.en.srt -
1.0 KB
017. Chapter 2. Summary.mp4 -
2.8 MB
018. Chapter 3. Convolutional neural networks.en.srt -
22.3 KB
018. Chapter 3. Convolutional neural networks.mp4 -
76.6 MB
019. Chapter 3. CNN architecture.en.srt -
10.3 KB
019. Chapter 3. CNN architecture.mp4 -
34.4 MB
020. Chapter 3. Basic components of a CNN.en.srt -
36.3 KB
020. Chapter 3. Basic components of a CNN.mp4 -
96.7 MB
021. Chapter 3. Image classification using CNNs.en.srt -
10.4 KB
021. Chapter 3. Image classification using CNNs.mp4 -
22.5 MB
022. Chapter 3. Adding dropout layers to avoid overfitting.en.srt -
10.9 KB
022. Chapter 3. Adding dropout layers to avoid overfitting.mp4 -
20.2 MB
023. Chapter 3. Convolution over color images (3D images).en.srt -
9.3 KB
023. Chapter 3. Convolution over color images (3D images).mp4 -
18.3 MB
024. Chapter 3. Project - Image classification for color images.en.srt -
19.2 KB
024. Chapter 3. Project - Image classification for color images.mp4 -
62.5 MB
025. Chapter 3. Summary.en.srt -
2.2 KB
025. Chapter 3. Summary.mp4 -
10.3 MB
026. Chapter 4. Structuring DL projects and hyperparameter tuning.en.srt -
17.4 KB
026. Chapter 4. Structuring DL projects and hyperparameter tuning.mp4 -
29.4 MB
027. Chapter 4. Designing a baseline model.en.srt -
4.2 KB
027. Chapter 4. Designing a baseline model.mp4 -
9.4 MB
028. Chapter 4. Getting your data ready for training.en.srt -
14.9 KB
028. Chapter 4. Getting your data ready for training.mp4 -
43.5 MB
029. Chapter 4. Evaluating the model and interpreting its performance.en.srt -
14.9 KB
029. Chapter 4. Evaluating the model and interpreting its performance.mp4 -
27.9 MB
030. Chapter 4. Improving the network and tuning hyperparameters.en.srt -
14.7 KB
030. Chapter 4. Improving the network and tuning hyperparameters.mp4 -
29.3 MB
031. Chapter 4. Learning and optimization.en.srt -
18.7 KB
031. Chapter 4. Learning and optimization.mp4 -
44.7 MB
032. Chapter 4. Optimization algorithms.en.srt -
11.9 KB
032. Chapter 4. Optimization algorithms.mp4 -
21.6 MB
033. Chapter 4. Regularization techniques to avoid overfitting.en.srt -
9.5 KB
033. Chapter 4. Regularization techniques to avoid overfitting.mp4 -
23.5 MB
034. Chapter 4. Batch normalization.en.srt -
10.4 KB
034. Chapter 4. Batch normalization.mp4 -
29.7 MB
035. Chapter 4. Project - Achieve high accuracy on image classification.en.srt -
11.9 KB
035. Chapter 4. Project - Achieve high accuracy on image classification.mp4 -
30.6 MB
036. Chapter 4. Summary.en.srt -
772 bytes
036. Chapter 4. Summary.mp4 -
1.8 MB
037. Part 2. Image classification and detection.en.srt -
1.2 KB
037. Part 2. Image classification and detection.mp4 -
2.5 MB
038. Chapter 5. Advanced CNN architectures.en.srt -
13.8 KB
038. Chapter 5. Advanced CNN architectures.mp4 -
26.2 MB
039. Chapter 5. LeNet-5.en.srt -
7.2 KB
039. Chapter 5. LeNet-5.mp4 -
17.5 MB
040. Chapter 5. AlexNet.en.srt -
30.4 KB
040. Chapter 5. AlexNet.mp4 -
49.0 MB
041. Chapter 5. VGGNet.en.srt -
7.9 KB
041. Chapter 5. VGGNet.mp4 -
27.1 MB
042. Chapter 5. Inception and GoogLeNet.en.srt -
24.5 KB
042. Chapter 5. Inception and GoogLeNet.mp4 -
65.2 MB
043. Chapter 5. ResNet.en.srt -
24.5 KB
043. Chapter 5. ResNet.mp4 -
59.4 MB
044. Chapter 5. Summary.en.srt -
1.7 KB
044. Chapter 5. Summary.mp4 -
9.0 MB
045. Chapter 6. Transfer learning.en.srt -
10.5 KB
045. Chapter 6. Transfer learning.mp4 -
32.3 MB
046. Chapter 6. What is transfer learning.en.srt -
12.6 KB
046. Chapter 6. What is transfer learning.mp4 -
29.4 MB
047. Chapter 6. How transfer learning works.en.srt -
13.0 KB
047. Chapter 6. How transfer learning works.mp4 -
24.5 MB
048. Chapter 6. Transfer learning approaches.en.srt -
15.1 KB
048. Chapter 6. Transfer learning approaches.mp4 -
28.1 MB
049. Chapter 6. Choosing the appropriate level of transfer learning.en.srt -
9.3 KB
049. Chapter 6. Choosing the appropriate level of transfer learning.mp4 -
26.7 MB
050. Chapter 6. Open source datasets.en.srt -
11.1 KB
050. Chapter 6. Open source datasets.mp4 -
23.9 MB
051. Chapter 6. Project 1 - A pretrained network as a feature extractor.en.srt -
8.8 KB
051. Chapter 6. Project 1 - A pretrained network as a feature extractor.mp4 -
31.9 MB
052. Chapter 6. Project 2 - Fine-tuning.en.srt -
9.1 KB
052. Chapter 6. Project 2 - Fine-tuning.mp4 -
24.0 MB
053. Chapter 6. Summary.en.srt -
2.6 KB
053. Chapter 6. Summary.mp4 -
9.9 MB
054. Chapter 7. Object detection with R-CNN, SSD, and YOLO.en.srt -
24.2 KB
054. Chapter 7. Object detection with R-CNN, SSD, and YOLO.mp4 -
76.4 MB
055. Chapter 7. Region-based convolutional neural networks (R-CNNs).en.srt -
46.7 KB
055. Chapter 7. Region-based convolutional neural networks (R-CNNs).mp4 -
145.2 MB
056. Chapter 7. Single-shot detector (SSD).en.srt -
23.8 KB
056. Chapter 7. Single-shot detector (SSD).mp4 -
71.4 MB
057. Chapter 7. You only look once (YOLO).en.srt -
18.4 KB
057. Chapter 7. You only look once (YOLO).mp4 -
32.2 MB
058. Chapter 7. Project - Train an SSD network in a self-driving car application.en.srt -
10.8 KB
058. Chapter 7. Project - Train an SSD network in a self-driving car application.mp4 -
31.2 MB
059. Chapter 7. Summary.en.srt -
1.8 KB
059. Chapter 7. Summary.mp4 -
10.9 MB
060. Part 3. Generative models and visual embeddings.en.srt -
1.2 KB
060. Part 3. Generative models and visual embeddings.mp4 -
2.6 MB
061. Chapter 8. Generative adversarial networks (GANs).en.srt -
32.7 KB
061. Chapter 8. Generative adversarial networks (GANs).mp4 -
74.8 MB
062. Chapter 8. Evaluating GAN models.en.srt -
9.4 KB
062. Chapter 8. Evaluating GAN models.mp4 -
18.9 MB
063. Chapter 8. Popular GAN applications.en.srt -
7.8 KB
063. Chapter 8. Popular GAN applications.mp4 -
16.6 MB
064. Chapter 8. Project - Building your own GAN.en.srt -
11.0 KB
064. Chapter 8. Project - Building your own GAN.mp4 -
44.0 MB
065. Chapter 8. Summary.en.srt -
1.8 KB
065. Chapter 8. Summary.mp4 -
8.7 MB
066. Chapter 9. DeepDream and neural style transfer.en.srt -
29.3 KB
066. Chapter 9. DeepDream and neural style transfer.mp4 -
66.6 MB
067. Chapter 9. DeepDream.en.srt -
13.1 KB
067. Chapter 9. DeepDream.mp4 -
38.2 MB
068. Chapter 9. Neural style transfer.en.srt -
18.6 KB
068. Chapter 9. Neural style transfer.mp4 -
51.7 MB
069. Chapter 9. Summary.en.srt -
3.0 KB
069. Chapter 9. Summary.mp4 -
10.7 MB
070. Chapter 10. Visual embeddings.en.srt -
14.7 KB
070. Chapter 10. Visual embeddings.mp4 -
41.8 MB
071. Chapter 10. Learning embedding.en.srt -
2.4 KB
071. Chapter 10. Learning embedding.mp4 -
8.5 MB
072. Chapter 10. Loss functions.en.srt -
18.5 KB
072. Chapter 10. Loss functions.mp4 -
37.2 MB
073. Chapter 10. Mining informative data.en.srt -
25.1 KB
073. Chapter 10. Mining informative data.mp4 -
68.6 MB
074. Chapter 10. Project - Train an embedding network.en.srt -
22.0 KB
074. Chapter 10. Project - Train an embedding network.mp4 -
68.9 MB
075. Chapter 10. Pushing the boundaries of current accuracy.en.srt -
5.3 KB
075. Chapter 10. Pushing the boundaries of current accuracy.mp4 -
12.7 MB
076. Chapter 10. Summary.en.srt -
666 bytes
076. Chapter 10. Summary.mp4 -
6.9 MB
Bonus Resources.txt -
70 bytes
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