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