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Deep Learning with Python Third Edition Video Edition

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Files:

Get Bonus Downloads Here.url 180 bytes
001. Chapter 1. What is deep learning.en.srt 2.3 KB
001. Chapter 1. What is deep learning.mp4 5.0 MB
002. Chapter 1. Artificial intelligence.en.srt 3.8 KB
002. Chapter 1. Artificial intelligence.mp4 7.5 MB
003. Chapter 1. Machine learning.en.srt 6.1 KB
003. Chapter 1. Machine learning.mp4 12.6 MB
004. Chapter 1. Learning rules and representations from data.en.srt 9.6 KB
004. Chapter 1. Learning rules and representations from data.mp4 17.0 MB
005. Chapter 1. The deep in deep learning .en.srt 4.5 KB
005. Chapter 1. The deep in deep learning .mp4 9.8 MB
006. Chapter 1. Understanding how deep learning works, in three figures.en.srt 4.3 KB
006. Chapter 1. Understanding how deep learning works, in three figures.mp4 6.9 MB
007. Chapter 1. Understanding how deep learning works, in three figures.en.srt 3.7 KB
007. Chapter 1. Understanding how deep learning works, in three figures.mp4 7.9 MB
008. Chapter 1. The age of generative AI.en.srt 3.0 KB
008. Chapter 1. The age of generative AI.mp4 4.4 MB
009. Chapter 1. What deep learning has achieved so far.en.srt 2.7 KB
009. Chapter 1. What deep learning has achieved so far.mp4 6.5 MB
010. Chapter 1. Beware of the short-term hype.en.srt 6.6 KB
010. Chapter 1. Beware of the short-term hype.mp4 15.1 MB
011. Chapter 1. Summer can turn to winter.en.srt 4.3 KB
011. Chapter 1. Summer can turn to winter.mp4 11.0 MB
012. Chapter 1. The promise of AI.en.srt 4.3 KB
012. Chapter 1. The promise of AI.mp4 8.5 MB
013. Chapter 2. The mathematical building blocks of neural networks.en.srt 14.7 KB
013. Chapter 2. The mathematical building blocks of neural networks.mp4 22.2 MB
014. Chapter 2. Data representations for neural networks.en.srt 17.7 KB
014. Chapter 2. Data representations for neural networks.mp4 32.6 MB
015. Chapter 2. The gears of neural networks - Tensor operations.en.srt 23.8 KB
015. Chapter 2. The gears of neural networks - Tensor operations.mp4 30.7 MB
016. Chapter 2. The engine of neural networks - Gradient-based optimization.en.srt 35.2 KB
016. Chapter 2. The engine of neural networks - Gradient-based optimization.mp4 60.2 MB
017. Chapter 2. Looking back at our first example.en.srt 11.4 KB
017. Chapter 2. Looking back at our first example.mp4 19.3 MB
018. Chapter 2. Summary.en.srt 2.9 KB
018. Chapter 2. Summary.mp4 4.5 MB
019. Chapter 3. Introduction to TensorFlow, PyTorch, JAX, and Keras.en.srt 9.4 KB
019. Chapter 3. Introduction to TensorFlow, PyTorch, JAX, and Keras.mp4 20.0 MB
020. Chapter 3. How these frameworks relate to each other.en.srt 3.0 KB
020. Chapter 3. How these frameworks relate to each other.mp4 5.9 MB
021. Chapter 3. Introduction to TensorFlow.en.srt 21.2 KB
021. Chapter 3. Introduction to TensorFlow.mp4 35.5 MB
022. Chapter 3. Introduction to PyTorch.en.srt 17.9 KB
022. Chapter 3. Introduction to PyTorch.mp4 26.9 MB
023. Chapter 3. Introduction to JAX.en.srt 17.5 KB
023. Chapter 3. Introduction to JAX.mp4 27.5 MB
024. Chapter 3. Introduction to Keras.en.srt 28.1 KB
024. Chapter 3. Introduction to Keras.mp4 48.3 MB
025. Chapter 3. Summary.en.srt 1.3 KB
025. Chapter 3. Summary.mp4 4.0 MB
026. Chapter 4. Classification and regression.en.srt 28.0 KB
026. Chapter 4. Classification and regression.mp4 47.8 MB
027. Chapter 4. Classifying newswires - A multiclass classification example.en.srt 14.4 KB
027. Chapter 4. Classifying newswires - A multiclass classification example.mp4 23.6 MB
028. Chapter 4. Predicting house prices - A regression example.en.srt 15.5 KB
028. Chapter 4. Predicting house prices - A regression example.mp4 25.0 MB
029. Chapter 4. Summary.en.srt 1.4 KB
029. Chapter 4. Summary.mp4 2.1 MB
030. Chapter 5. Fundamentals of machine learning.en.srt 32.7 KB
030. Chapter 5. Fundamentals of machine learning.mp4 51.8 MB
031. Chapter 5. Evaluating machine-learning models.en.srt 14.6 KB
031. Chapter 5. Evaluating machine-learning models.mp4 25.3 MB
032. Chapter 5. Improving model fit.en.srt 9.5 KB
032. Chapter 5. Improving model fit.mp4 15.7 MB
033. Chapter 5. Improving generalization.en.srt 25.0 KB
033. Chapter 5. Improving generalization.mp4 40.4 MB
034. Chapter 5. Summary.en.srt 2.9 KB
034. Chapter 5. Summary.mp4 6.9 MB
035. Chapter 6. The universal workflow of machine learning.en.srt 30.1 KB
035. Chapter 6. The universal workflow of machine learning.mp4 60.2 MB
036. Chapter 6. Developing a model.en.srt 18.5 KB
036. Chapter 6. Developing a model.mp4 31.7 MB
037. Chapter 6. Deploying your model.en.srt 21.6 KB
037. Chapter 6. Deploying your model.mp4 37.9 MB
038. Chapter 6. Summary.en.srt 1.8 KB
038. Chapter 6. Summary.mp4 3.9 MB
039. Chapter 7. A deep dive on Keras.en.srt 5.6 KB
039. Chapter 7. A deep dive on Keras.mp4 11.0 MB
040. Chapter 7. Different ways to build Keras models.en.srt 20.2 KB
040. Chapter 7. Different ways to build Keras models.mp4 32.5 MB
041. Chapter 7. Using built-in training and evaluation loops.en.srt 14.7 KB
041. Chapter 7. Using built-in training and evaluation loops.mp4 24.6 MB
042. Chapter 7. Writing your own training and evaluation loops.en.srt 23.7 KB
042. Chapter 7. Writing your own training and evaluation loops.mp4 38.6 MB
043. Chapter 7. Summary.en.srt 1.3 KB
043. Chapter 7. Summary.mp4 4.0 MB
044. Chapter 8. Image classification.en.srt 27.0 KB
044. Chapter 8. Image classification.mp4 47.7 MB
045. Chapter 8. Training a ConvNet from scratch on a small dataset.en.srt 27.4 KB
045. Chapter 8. Training a ConvNet from scratch on a small dataset.mp4 48.3 MB
046. Chapter 8. Using a pretrained model.en.srt 23.6 KB
046. Chapter 8. Using a pretrained model.mp4 42.4 MB
047. Chapter 8. Summary.en.srt 1.1 KB
047. Chapter 8. Summary.mp4 2.9 MB
048. Chapter 9. ConvNet architecture patterns.en.srt 11.6 KB
048. Chapter 9. ConvNet architecture patterns.mp4 24.1 MB
049. Chapter 9. Residual connections.en.srt 4.7 KB
049. Chapter 9. Residual connections.mp4 8.5 MB
050. Chapter 9. Batch normalization.en.srt 7.0 KB
050. Chapter 9. Batch normalization.mp4 12.6 MB
051. Chapter 9. Depthwise separable convolutions.en.srt 7.6 KB
051. Chapter 9. Depthwise separable convolutions.mp4 17.3 MB
052. Chapter 9. Putting it together - A mini Xception-like model.en.srt 2.9 KB
052. Chapter 9. Putting it together - A mini Xception-like model.mp4 5.9 MB
053. Chapter 9. Beyond convolution - Vision Transformers.en.srt 3.5 KB
053. Chapter 9. Beyond convolution - Vision Transformers.mp4 6.1 MB
054. Chapter 9. Summary.en.srt 716 bytes
054. Chapter 9. Summary.mp4 1.7 MB
055. Chapter 10. Interpreting what ConvNets learn.en.srt 11.0 KB
055. Chapter 10. Interpreting what ConvNets learn.mp4 21.8 MB
056. Chapter 10. Visualizing ConvNet filters.en.srt 10.9 KB
056. Chapter 10. Visualizing ConvNet filters.mp4 17.7 MB
057. Chapter 10. Visualizing heatmaps of class activation.en.srt 8.2 KB
057. Chapter 10. Visualizing heatmaps of class activation.mp4 15.6 MB
058. Chapter 10. Visualizing the latent space of a ConvNet.en.srt 4.8 KB
058. Chapter 10. Visualizing the latent space of a ConvNet.mp4 8.0 MB
059. Chapter 10. Summary.en.srt 789 bytes
059. Chapter 10. Summary.mp4 1.6 MB
060. Chapter 11. Image segmentation.en.srt 6.4 KB
060. Chapter 11. Image segmentation.mp4 12.2 MB
061. Chapter 11. Training a segmentation model from scratch.en.srt 10.3 KB
061. Chapter 11. Training a segmentation model from scratch.mp4 23.8 MB
062. Chapter 11. Using a pretrained segmentation model.en.srt 13.8 KB
062. Chapter 11. Using a pretrained segmentation model.mp4 20.8 MB
063. Chapter 11. Summary.en.srt 856 bytes
063. Chapter 11. Summary.mp4 2.2 MB
064. Chapter 12. Object detection.en.srt 8.0 KB
064. Chapter 12. Object detection.mp4 14.3 MB
065. Chapter 12. Training a YOLO model from scratch.en.srt 19.7 KB
065. Chapter 12. Training a YOLO model from scratch.mp4 39.7 MB
066. Chapter 12. Using a pretrained RetinaNet detector.en.srt 5.8 KB
066. Chapter 12. Using a pretrained RetinaNet detector.mp4 11.0 MB
067. Chapter 12. Summary.en.srt 1.8 KB
067. Chapter 12. Summary.mp4 3.3 MB
068. Chapter 13. Timeseries forecasting.en.srt 3.8 KB
068. Chapter 13. Timeseries forecasting.mp4 7.7 MB
069. Chapter 13. A temperature forecasting example.en.srt 21.4 KB
069. Chapter 13. A temperature forecasting example.mp4 39.3 MB
070. Chapter 13. Recurrent neural networks.en.srt 45.0 KB
070. Chapter 13. Recurrent neural networks.mp4 72.9 MB
071. Chapter 13. Going even further.en.srt 4.0 KB
071. Chapter 13. Going even further.mp4 6.9 MB
072. Chapter 13. Summary.en.srt 1.6 KB
072. Chapter 13. Summary.mp4 4.9 MB
073. Chapter 14. Text classification.en.srt 12.4 KB
073. Chapter 14. Text classification.mp4 27.9 MB
074. Chapter 14. Preparing text data.en.srt 23.6 KB
074. Chapter 14. Preparing text data.mp4 40.9 MB
075. Chapter 14. Sets vs. sequences.en.srt 7.8 KB
075. Chapter 14. Sets vs. sequences.mp4 13.3 MB
076. Chapter 14. Set models.en.srt 13.6 KB
076. Chapter 14. Set models.mp4 25.2 MB
077. Chapter 14. Sequence models.en.srt 35.6 KB
077. Chapter 14. Sequence models.mp4 57.5 MB
078. Chapter 14. Summary.en.srt 2.0 KB
078. Chapter 14. Summary.mp4 3.6 MB
079. Chapter 15. Language models and the Transformer.en.srt 16.2 KB
079. Chapter 15. Language models and the Transformer.mp4 29.4 MB
080. Chapter 15. Sequence-to-sequence learning.en.srt 14.5 KB
080. Chapter 15. Sequence-to-sequence learning.mp4 29.0 MB
081. Chapter 15. The Transformer architecture.en.srt 37.6 KB
081. Chapter 15. The Transformer architecture.mp4 63.3 MB
082. Chapter 15. Classification with a pretrained Transformer.en.srt 19.0 KB
082. Chapter 15. Classification with a pretrained Transformer.mp4 33.5 MB
083. Chapter 15. What makes the Transformer effective.en.srt 12.1 KB
083. Chapter 15. What makes the Transformer effective.mp4 25.2 MB
084. Chapter 15. Summary.en.srt 2.9 KB
084. Chapter 15. Summary.mp4 7.2 MB
085. Chapter 16. Text generation.en.srt 13.7 KB
085. Chapter 16. Text generation.mp4 24.9 MB
086. Chapter 16. Training a mini-GPT.en.srt 29.9 KB
086. Chapter 16. Training a mini-GPT.mp4 53.7 MB
087. Chapter 16. Using a pretrained LLM.en.srt 21.2 KB
087. Chapter 16. Using a pretrained LLM.mp4 33.3 MB
088. Chapter 16. Going further with LLMs.en.srt 27.6 KB
088. Chapter 16. Going further with LLMs.mp4 46.6 MB
089. Chapter 16. Where are LLMs heading next.en.srt 5.0 KB
089. Chapter 16. Where are LLMs heading next.mp4 9.3 MB
090. Chapter 16. Summary.en.srt 2.5 KB
090. Chapter 16. Summary.mp4 3.9 MB
091. Chapter 17. Image generation.en.srt 20.3 KB
091. Chapter 17. Image generation.mp4 37.1 MB
092. Chapter 17. Diffusion models.en.srt 17.5 KB
092. Chapter 17. Diffusion models.mp4 31.6 MB
093. Chapter 17. Text-to-image models.en.srt 13.5 KB
093. Chapter 17. Text-to-image models.mp4 23.6 MB
094. Chapter 17. Summary.en.srt 1.9 KB
094. Chapter 17. Summary.mp4 4.0 MB
095. Chapter 18. Best practices for the real world.en.srt 32.0 KB
095. Chapter 18. Best practices for the real world.mp4 46.4 MB
096. Chapter 18. Scaling up model training with multiple devices.en.srt 25.4 KB
096. Chapter 18. Scaling up model training with multiple devices.mp4 41.8 MB
097. Chapter 18. Speeding up training and inference with lower-precision computation.en.srt 18.5 KB
097. Chapter 18. Speeding up training and inference with lower-precision computation.mp4 30.7 MB
098. Chapter 18. Summary.en.srt 1.1 KB
098. Chapter 18. Summary.mp4 3.2 MB
099. Chapter 19. The future of AI.en.srt 21.7 KB
099. Chapter 19. The future of AI.mp4 43.3 MB
100. Chapter 19. Scale isn t all you need.en.srt 22.2 KB
100. Chapter 19. Scale isn t all you need.mp4 49.7 MB
101. Chapter 19. How to build intelligence.en.srt 28.2 KB
101. Chapter 19. How to build intelligence.mp4 56.3 MB
102. Chapter 19. The missing ingredients - Search and symbols.en.srt 36.1 KB
102. Chapter 19. The missing ingredients - Search and symbols.mp4 70.4 MB
103. Chapter 20. Conclusions.en.srt 31.0 KB
103. Chapter 20. Conclusions.mp4 66.7 MB
104. Chapter 20. Limitations of deep learning.en.srt 4.6 KB
104. Chapter 20. Limitations of deep learning.mp4 8.6 MB
105. Chapter 20. What might lie ahead.en.srt 3.3 KB
105. Chapter 20. What might lie ahead.mp4 7.0 MB
106. Chapter 20. Staying up to date in a fast-moving field.en.srt 5.6 KB
106. Chapter 20. Staying up to date in a fast-moving field.mp4 11.5 MB
107. Chapter 20. Final words.en.srt 764 bytes
107. Chapter 20. Final words.mp4 1.5 MB
Bonus Resources.txt 70 bytes

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