A comprehensive, self-contained guide to TensorFlow and Keras, the framework with which much of the world builds and ships deep-learning models.

Welcome to TensorFlow and Keras: Practical Deep Learning.

A comprehensive, self-contained guide to TensorFlow and Keras, the framework with which much of the world builds and ships deep-learning models. Where the companion Scikit-Learn book covers classical machine learning, this book picks up exactly where scikit-learn deliberately stops: neural networks — the deep models behind modern computer vision, language, audio, and generative AI. It teaches the practical craft — tensors and automatic differentiation, the three Keras model-building APIs, training loops, tf.data input pipelines, CNNs and sequence models, transfer learning, and the full road to deployment on servers, mobile, and the web. It blends intuition, the concepts behind the API, and runnable code.

This title is part of the ShriIra library and is free to read in full, right here — our small contribution to making world-class knowledge easy to reach.

A note on reading it: open the Contents menu at the top of the reader to jump between chapters, use the Aa menu to set a comfortable text size, theme (light, sepia, or night), and single- or two-page layout. Your place is saved automatically, so you can always pick up where you left off.

We hope it serves you well.

— Shriira Press

Contents

1. Chapter 1 — What Is TensorFlow and Keras?
2. Chapter 2 — Tensors and the TensorFlow Core
3. Chapter 3 — Your First Neural Network with Keras
4. Chapter 4 — The Keras APIs: Sequential, Functional, and Subclassing
5. Chapter 5 — Layers, Activations, and Building Blocks
6. Chapter 6 — Training: Losses, Optimizers, Metrics, and Loops
7. Chapter 7 — Data Pipelines with tf.data
8. Chapter 8 — Convolutional Networks for Images
9. Chapter 9 — Sequence Models: RNNs, LSTMs, and Attention
10. Chapter 10 — Regularization, Tuning, and Avoiding Overfitting
11. Chapter 11 — Transfer Learning and Pretrained Models
12. Chapter 12 — Saving, Serving, and Deployment
13. Chapter 13 — The Ecosystem, Scaling, and the Profession
14. Appendix A — Glossary and API Quick Reference
15. Appendix B — Further Reading and Resources