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001. Chapter 1. What is deep learning.en.srt
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002. Chapter 1. Artificial intelligence.en.srt
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002. Chapter 1. Artificial intelligence.mp4
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003. Chapter 1. Machine learning.en.srt
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003. Chapter 1. Machine learning.mp4
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004. Chapter 1. Learning rules and representations from data.en.srt
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004. Chapter 1. Learning rules and representations from data.mp4
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005. Chapter 1. The deep in deep learning .en.srt
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005. Chapter 1. The deep in deep learning .mp4
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006. Chapter 1. Understanding how deep learning works, in three figures.en.srt
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007. Chapter 1. Understanding how deep learning works, in three figures.en.srt
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008. Chapter 1. The age of generative AI.en.srt
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008. Chapter 1. The age of generative AI.mp4
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009. Chapter 1. What deep learning has achieved so far.en.srt
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009. Chapter 1. What deep learning has achieved so far.mp4
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010. Chapter 1. Beware of the short-term hype.en.srt
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010. Chapter 1. Beware of the short-term hype.mp4
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011. Chapter 1. Summer can turn to winter.en.srt
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011. Chapter 1. Summer can turn to winter.mp4
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012. Chapter 1. The promise of AI.en.srt
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013. Chapter 2. The mathematical building blocks of neural networks.en.srt
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013. Chapter 2. The mathematical building blocks of neural networks.mp4
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014. Chapter 2. Data representations for neural networks.en.srt
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014. Chapter 2. Data representations for neural networks.mp4
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015. Chapter 2. The gears of neural networks - Tensor operations.en.srt
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015. Chapter 2. The gears of neural networks - Tensor operations.mp4
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016. Chapter 2. The engine of neural networks - Gradient-based optimization.en.srt
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016. Chapter 2. The engine of neural networks - Gradient-based optimization.mp4
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017. Chapter 2. Looking back at our first example.en.srt
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017. Chapter 2. Looking back at our first example.mp4
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018. Chapter 2. Summary.en.srt
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019. Chapter 3. Introduction to TensorFlow, PyTorch, JAX, and Keras.en.srt
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019. Chapter 3. Introduction to TensorFlow, PyTorch, JAX, and Keras.mp4
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020. Chapter 3. How these frameworks relate to each other.en.srt
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021. Chapter 3. Introduction to TensorFlow.en.srt
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021. Chapter 3. Introduction to TensorFlow.mp4
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022. Chapter 3. Introduction to PyTorch.en.srt
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023. Chapter 3. Introduction to JAX.en.srt
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023. Chapter 3. Introduction to JAX.mp4
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024. Chapter 3. Introduction to Keras.en.srt
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024. Chapter 3. Introduction to Keras.mp4
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025. Chapter 3. Summary.en.srt
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025. Chapter 3. Summary.mp4
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026. Chapter 4. Classification and regression.en.srt
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026. Chapter 4. Classification and regression.mp4
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027. Chapter 4. Classifying newswires - A multiclass classification example.en.srt
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027. Chapter 4. Classifying newswires - A multiclass classification example.mp4
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028. Chapter 4. Predicting house prices - A regression example.en.srt
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028. Chapter 4. Predicting house prices - A regression example.mp4
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029. Chapter 4. Summary.en.srt
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030. Chapter 5. Fundamentals of machine learning.en.srt
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030. Chapter 5. Fundamentals of machine learning.mp4
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031. Chapter 5. Evaluating machine-learning models.en.srt
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031. Chapter 5. Evaluating machine-learning models.mp4
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032. Chapter 5. Improving model fit.en.srt
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032. Chapter 5. Improving model fit.mp4
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033. Chapter 5. Improving generalization.en.srt
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033. Chapter 5. Improving generalization.mp4
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034. Chapter 5. Summary.en.srt
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035. Chapter 6. The universal workflow of machine learning.en.srt
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035. Chapter 6. The universal workflow of machine learning.mp4
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036. Chapter 6. Developing a model.en.srt
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036. Chapter 6. Developing a model.mp4
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037. Chapter 6. Deploying your model.en.srt
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037. Chapter 6. Deploying your model.mp4
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038. Chapter 6. Summary.en.srt
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039. Chapter 7. A deep dive on Keras.en.srt
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039. Chapter 7. A deep dive on Keras.mp4
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040. Chapter 7. Different ways to build Keras models.en.srt
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041. Chapter 7. Using built-in training and evaluation loops.en.srt
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042. Chapter 7. Writing your own training and evaluation loops.en.srt
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043. Chapter 7. Summary.en.srt
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044. Chapter 8. Image classification.en.srt
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044. Chapter 8. Image classification.mp4
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045. Chapter 8. Training a ConvNet from scratch on a small dataset.en.srt
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045. Chapter 8. Training a ConvNet from scratch on a small dataset.mp4
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046. Chapter 8. Using a pretrained model.en.srt
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047. Chapter 8. Summary.en.srt
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047. Chapter 8. Summary.mp4
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048. Chapter 9. ConvNet architecture patterns.en.srt
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048. Chapter 9. ConvNet architecture patterns.mp4
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049. Chapter 9. Residual connections.en.srt
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049. Chapter 9. Residual connections.mp4
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050. Chapter 9. Batch normalization.en.srt
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050. Chapter 9. Batch normalization.mp4
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051. Chapter 9. Depthwise separable convolutions.en.srt
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051. Chapter 9. Depthwise separable convolutions.mp4
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052. Chapter 9. Putting it together - A mini Xception-like model.en.srt
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052. Chapter 9. Putting it together - A mini Xception-like model.mp4
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053. Chapter 9. Beyond convolution - Vision Transformers.en.srt
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053. Chapter 9. Beyond convolution - Vision Transformers.mp4
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054. Chapter 9. Summary.en.srt
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055. Chapter 10. Interpreting what ConvNets learn.en.srt
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055. Chapter 10. Interpreting what ConvNets learn.mp4
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056. Chapter 10. Visualizing ConvNet filters.en.srt
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057. Chapter 10. Visualizing heatmaps of class activation.en.srt
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058. Chapter 10. Visualizing the latent space of a ConvNet.en.srt
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059. Chapter 10. Summary.en.srt
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060. Chapter 11. Image segmentation.en.srt
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060. Chapter 11. Image segmentation.mp4
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061. Chapter 11. Training a segmentation model from scratch.en.srt
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061. Chapter 11. Training a segmentation model from scratch.mp4
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062. Chapter 11. Using a pretrained segmentation model.en.srt
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062. Chapter 11. Using a pretrained segmentation model.mp4
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063. Chapter 11. Summary.en.srt
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064. Chapter 12. Object detection.en.srt
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064. Chapter 12. Object detection.mp4
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065. Chapter 12. Training a YOLO model from scratch.en.srt
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065. Chapter 12. Training a YOLO model from scratch.mp4
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066. Chapter 12. Using a pretrained RetinaNet detector.en.srt
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066. Chapter 12. Using a pretrained RetinaNet detector.mp4
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067. Chapter 12. Summary.en.srt
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067. Chapter 12. Summary.mp4
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068. Chapter 13. Timeseries forecasting.en.srt
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068. Chapter 13. Timeseries forecasting.mp4
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069. Chapter 13. A temperature forecasting example.en.srt
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069. Chapter 13. A temperature forecasting example.mp4
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070. Chapter 13. Recurrent neural networks.en.srt
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070. Chapter 13. Recurrent neural networks.mp4
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071. Chapter 13. Going even further.en.srt
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071. Chapter 13. Going even further.mp4
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072. Chapter 13. Summary.en.srt
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072. Chapter 13. Summary.mp4
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073. Chapter 14. Text classification.en.srt
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073. Chapter 14. Text classification.mp4
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074. Chapter 14. Preparing text data.en.srt
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074. Chapter 14. Preparing text data.mp4
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075. Chapter 14. Sets vs. sequences.en.srt
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075. Chapter 14. Sets vs. sequences.mp4
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076. Chapter 14. Set models.en.srt
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076. Chapter 14. Set models.mp4
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077. Chapter 14. Sequence models.en.srt
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078. Chapter 14. Summary.en.srt
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079. Chapter 15. Language models and the Transformer.en.srt
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079. Chapter 15. Language models and the Transformer.mp4
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080. Chapter 15. Sequence-to-sequence learning.en.srt
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081. Chapter 15. The Transformer architecture.en.srt
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081. Chapter 15. The Transformer architecture.mp4
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082. Chapter 15. Classification with a pretrained Transformer.en.srt
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082. Chapter 15. Classification with a pretrained Transformer.mp4
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083. Chapter 15. What makes the Transformer effective.en.srt
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084. Chapter 15. Summary.en.srt
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085. Chapter 16. Text generation.en.srt
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086. Chapter 16. Training a mini-GPT.en.srt
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087. Chapter 16. Using a pretrained LLM.en.srt
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088. Chapter 16. Going further with LLMs.en.srt
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089. Chapter 16. Where are LLMs heading next.en.srt
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091. Chapter 17. Image generation.en.srt
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091. Chapter 17. Image generation.mp4
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092. Chapter 17. Diffusion models.en.srt
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092. Chapter 17. Diffusion models.mp4
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093. Chapter 17. Text-to-image models.en.srt
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094. Chapter 17. Summary.en.srt
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095. Chapter 18. Best practices for the real world.en.srt
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096. Chapter 18. Scaling up model training with multiple devices.en.srt
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097. Chapter 18. Speeding up training and inference with lower-precision computation.en.srt
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098. Chapter 18. Summary.en.srt
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099. Chapter 19. The future of AI.en.srt
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100. Chapter 19. Scale isn t all you need.en.srt
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101. Chapter 19. How to build intelligence.en.srt
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101. Chapter 19. How to build intelligence.mp4
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MP4
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56.3 MB
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102. Chapter 19. The missing ingredients - Search and symbols.en.srt
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SRT
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36.1 KB
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102. Chapter 19. The missing ingredients - Search and symbols.mp4
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MP4
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70.4 MB
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103. Chapter 20. Conclusions.en.srt
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SRT
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31 KB
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103. Chapter 20. Conclusions.mp4
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MP4
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66.7 MB
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104. Chapter 20. Limitations of deep learning.en.srt
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SRT
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4.6 KB
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104. Chapter 20. Limitations of deep learning.mp4
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MP4
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8.6 MB
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105. Chapter 20. What might lie ahead.en.srt
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SRT
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3.3 KB
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105. Chapter 20. What might lie ahead.mp4
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MP4
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7 MB
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106. Chapter 20. Staying up to date in a fast-moving field.en.srt
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SRT
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5.6 KB
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106. Chapter 20. Staying up to date in a fast-moving field.mp4
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MP4
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11.5 MB
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107. Chapter 20. Final words.en.srt
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SRT
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716.8 B
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107. Chapter 20. Final words.mp4
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MP4
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1.5 MB
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Bonus Resources.txt
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TXT
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102.4 B
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Get Bonus Downloads Here.url
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URL
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204.8 B
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