Editor's top TensorFlow books
2Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow
Aurélien Géron · O'Reilly Media · 877 pages · ~22 hrs to read
The definitive practical guide to TensorFlow and Keras, now in its third edition covering TF 2.x fully. Géron builds from classical ML through CNNs, RNNs, and transformers with working code throughout. Nearly every ML Engineer hiring manager has read this book — it is the benchmark text for the field.
Deep Learning with Python
François Chollet · Manning Publications · 1157 pages · ~29 hrs to read
Written by the creator of Keras, this book explains not just how to use TensorFlow and Keras but why the abstractions are designed as they are. The second edition covers the modern Keras 2 API and includes a chapter on the limits of deep learning that no other text matches for intellectual honesty.
All TensorFlow Books
7Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow
Aurélien Géron · O'Reilly Media · 877 pages · ~22 hrs to read
The definitive practical guide to TensorFlow and Keras, now in its third edition covering TF 2.x fully. Géron builds from classical ML through CNNs, RNNs, and transformers with working code throughout. Nearly every ML Engineer hiring manager has read this book — it is the benchmark text for the field.
Deep Learning with Python
François Chollet · Manning Publications · 1157 pages · ~29 hrs to read
Written by the creator of Keras, this book explains not just how to use TensorFlow and Keras but why the abstractions are designed as they are. The second edition covers the modern Keras 2 API and includes a chapter on the limits of deep learning that no other text matches for intellectual honesty.
TensorFlow in Action
Thushan Ganegedara · Manning Publications · 678 pages · ~17 hrs to read
A project-driven companion to the official docs, covering NLP, computer vision, and time-series use cases end-to-end. Strong on the production aspects that other books skip — model serving, performance profiling, and TFX pipeline integration.
Deep Learning with TensorFlow and Keras
Amita Kapoor, Antonio Gulli & Sujit Pal · Packt · 699 pages · ~17 hrs to read
Now in its third edition, this is a genuinely comprehensive TensorFlow/Keras text covering supervised, unsupervised, and reinforcement learning models in one book — with a foreword from Keras creator François Chollet. A solid alternative to Géron for engineers who want TensorFlow-first coverage without the scikit-learn detour.
Machine Learning Design Patterns
Valliappa Lakshmanan, Sara Robinson & Michael Munn · O'Reilly · 404 pages · ~10 hrs to read
Three Google engineers catalogue 30 recurring patterns for data representation, model training, and MLOps, with TensorFlow among the primary frameworks used in the examples. This is the book for engineers who have already trained models and now need to reason about reproducibility, fairness, and production reliability at scale.
TinyML
Pete Warden & Daniel Situnayake · O'Reilly · 478 pages · ~12 hrs to read
Written by TensorFlow Lite's own technical lead, this is the definitive guide to deploying TensorFlow models onto microcontrollers with kilobytes of memory. A niche but genuinely unique corner of the TensorFlow ecosystem — no other book covers embedded ML deployment at this level of practical detail.
Natural Language Processing with TensorFlow
Thushan Ganegedara · Packt · 472 pages · ~12 hrs to read
Walks through CNNs, RNNs, and transformer architectures for NLP tasks specifically in TensorFlow, from the same author as TensorFlow in Action. Useful for engineers whose TensorFlow work is language-focused rather than vision-focused, a split that most general TensorFlow books gloss over.
TensorFlow resources
Learn next