Chapters

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