François Chollet and Matthew WatsonDeep Learning with Python, 3rd Edition
1.
Chapter 1. What is deep learning
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Chapter 2. The mathematical building blocks of neural networks
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Chapter 3. Introduction to TensorFlow, PyTorch, JAX, and Keras
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Chapter 4. Classification and regression
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Chapter 5. Fundamentals of machine learning
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Chapter 6. The universal workflow of machine learning
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Chapter 7. A deep dive on Keras
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Chapter 8. Image classification
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Chapter 9. ConvNet architecture patterns
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Chapter 10. Interpreting what ConvNets learn
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Chapter 11. Image segmentation
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Chapter 12. Object detection
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Chapter 13. Timeseries forecasting
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Chapter 14. Text classification
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Chapter 15. Language models and the Transformer
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Chapter 16. Text generation
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Chapter 17. Image generation
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Chapter 18. Best practices for the real world
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Chapter 19. The future of AI
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Chapter 20. Conclusions
