25 Best Tensorflow Books of All Time

We've ranked the best tensorflow books using expert recommendations, sales data, and millions of reader ratings. At Shortform, we know books. Our book guides are the best in the world. Learn why.

1

Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems

Geron

5.0
Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how.

By using concrete examples, minimal theory, and two production-ready Python frameworks-scikit-learn and

Mark Tabladillo portrait Recommended by Mark Tabladillo.
Hands-On Machine Learning with Scikit-Learn, Keras, and Tensorflow: Concepts, Tools, and Techniques to Build Intelligent Systems book cover2

Hands-On Machine Learning with Scikit-Learn, Keras, and Tensorflow: Concepts, Tools, and Techniques to Build Intelligent Systems

Aurélien Géron

4.9
3

Deep Learning with Python

François Chollet

4.8
Deep learning is applicable to a widening range of artificial intelligence problems, such as image classification, speech recognition, text classification, question answering, text-to-speech, and optical character recognition. It is the technology behind photo tagging systems at Facebook and Google, self-driving cars, speech recognition systems on your smartphone, and much more.

In particular, Deep
Tinyml: Machine Learning with Tensorflow Lite on Arduino and Ultra-Low-Power Microcontrollers book cover4

Tinyml: Machine Learning with Tensorflow Lite on Arduino and Ultra-Low-Power Microcontrollers

Pete Warden

4.8
Neural networks are getting smaller. Much smaller. The OK Google team, for example, has run machine learning models that are just 14 kilobytes in size--small enough to work on the digital signal processor in an Android phone. With this practical book, you'll learn about TensorFlow Lite for Microcontrollers, a miniscule machine learning library that allows you to run machine learning algorithms on
5

Machine Learning with Python Cookbook: Practical Solutions from Preprocessing to Deep Learning

Chris Albon

4.8
This practical guide provides nearly 200 self-contained recipes to help you solve machine learning challenges you may encounter in your daily work. If you're comfortable with Python and its libraries, including pandas and scikit-learn, you'll be able to address specific problems such as loading data, handling text or numerical data, model selection, and dimensionality reduction and many other topi
Dj Patil portraitKirk Borne portrait Recommended by Dj Patil and Kirk Borne.
Dj Patil portrait
Dj Patil

Because @chrisalbon is too humble to promote his book, I'm going to step in and say you should really go out and get it. Built on top of his awesome flash cards (https://t.co/ZbvZumC9Kq). It's a great way to get going on machine learning https://t.co/G3kKKErgIb [source]

Kirk Borne portrait
Kirk Borne

✨🎉🌟Must see this >> Free #Python #DataScience Coding book series for #DataScientists ...via @DataScienceCtrl ——————————————— #abdsc #Coding #MachineLearning #BigData #BeDataBrilliant #DataLiteracy 👇👇👇 https://t.co/l14zcnYlb7 [source]

6

Advanced Deep Learning with TensorFlow 2 and Keras: Apply DL, GANs, VAEs, deep RL, unsupervised learning, object detection and segmentation, and more, 2nd Edition

Rowel Atienza

4.8
7

Deep Learning with TensorFlow 2 and Keras: Regression, ConvNets, GANs, RNNs, NLP, and more with TensorFlow 2 and the Keras API, 2nd Edition

Antonio Gulli, Amita Kapoor, et al.

4.8
8

Probabilistic Deep Learning: With Python, Keras and TensorFlow Probability

Oliver Duerr, Beate Sick, et al.

4.8
Probabilistic Deep Learning shows how probabilistic deep learning models gives readers the tools to identify and account for uncertainty and potential errors in their results.

Starting by applying the underlying maximum likelihood principle of curve fitting to deep learning, readers will move on to using the Python-based Tensorflow Probability framework, and set up Bayesian neural networks that can
9

Make Your First GAN With PyTorch

Tariq Rashid

4.8
A gentle introduction to Generative Adversarial Networks, and a practical step-by-step tutorial on making your own with PyTorch.
GANs are one of the most exciting areas of machine learning, able to create entirely synthetic but surprising realistic images.
This beginner-friendly guide will give you hands-on experience:
learning PyTorch basics
developing your first PyTorch neural network
exploring neura
Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow 2, 3rd Edition book cover10

Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow 2, 3rd Edition

Sebastian Raschka

4.8
Link to the GitHub Repository containing the code examples and additional material: https://github.com/rasbt/python-machi...

Many of the most innovative breakthroughs and exciting new technologies can be attributed to applications of machine learning. We are living in an age where data comes in abundance, and thanks to the self-learning algorithms from the field of machine learning, we can turn thi
Practical Deep Learning for Cloud, Mobile, and Edge: Real-World AI & Computer-Vision Projects Using Python, Keras & Tensorflow book cover11

Practical Deep Learning for Cloud, Mobile, and Edge: Real-World AI & Computer-Vision Projects Using Python, Keras & Tensorflow

Anirudh Koul, Siddha Ganju, et al.

4.8
Whether you're a software engineer aspiring to enter the world of deep learning, a veteran data scientist, or a hobbyist with a simple dream of making the next viral AI app, you might have wondered where to begin. This step-by-step guide teaches you how to build practical deep learning applications for the cloud, mobile, browsers, and edge devices using a hands-on approach.

Relying on years of indu
12

Deep Learning with JavaScript: Neural networks in TensorFlow.js

Shanqing Cai, Stan Bileschi, et al.

4.7
Summary

Deep learning has transformed the fields of computer vision, image processing, and natural language applications. Thanks to TensorFlow.js, now JavaScript developers can build deep learning apps without relying on Python or R. Deep Learning with JavaScript shows developers how they can bring DL technology to the web. Written by the main authors of the TensorFlow library, this new book provi
Artificial Intelligence Basics: A Non-Technical Introduction book cover13

Artificial Intelligence Basics: A Non-Technical Introduction

Tom Taulli

4.7
Artificial intelligence touches nearly every part of your day. While you may initially assume that technology such as smart speakers and digital assistants are the extent of it, AI has in fact rapidly become a general-purpose technology, reverberating across industries including transportation, healthcare, financial services, and many more. In our modern era, an understanding of AI and its possibi
14

Natural Language Processing with TensorFlow: Teach language to machines using Python's deep learning library

Thushan Ganegedar

4.7
15

Applied Deep Learning with Python: Use scikit-learn, TensorFlow, and Keras to create intelligent systems and machine learning solutions

Alex Galea and Luis Capelo

4.7
16

Machine Learning with TensorFlow

Nishant Shukla

4.6
Summary

Machine Learning with TensorFlow gives readers a solid foundation in machine-learning concepts plus hands-on experience coding TensorFlow with Python.

Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.

About the Technology

TensorFlow, Google's library for large-scale machine learning, simplifies often-complex computations by representi
17

Mastering Computer Vision with TensorFlow 2.x: Build advanced computer vision applications using machine learning and deep learning techniques

Krishnendu Kar

4.6
18

Practical Computer Vision Applications Using Deep Learning with Cnns: With Detailed Examples in Python Using Tensorflow and Kivy

Ahmed Fawzy Gad

4.6
Deploy deep learning applications into production across multiple platforms. You will work on computer vision applications that use the convolutional neural network (CNN) deep learning model and Python. This book starts by explaining the traditional machine-learning pipeline, where you will analyze an image dataset. Along the way you will cover artificial neural networks (ANNs), building one from
19

Learning Tensorflow: A Guide to Building Deep Learning Systems

Tom Hope, Yehezkel S. Resheff, Itay Lieder

4.6
Roughly inspired by the human brain, deep neural networks trained with large amounts of data can solve complex tasks with unprecedented accuracy. This practical book provides an end-to-end guide to TensorFlow, the leading open source software library that helps you build and train neural networks for computer vision, natural language processing (NLP), speech recognition, and general predictive ana
20

Tensorflow for Dummies

Matthew Scarpino

4.6
Become a machine learning pro!

Google TensorFlow has become the darling of financial firms and research organizations, but the technology can be intimidating and the learning curve is steep. Luckily, TensorFlow For Dummies is here to offer you a friendly, easy-to-follow book on the subject. Inside, you'll find out how to write applications with TensorFlow, while also grasping the concepts underlyin
21

Pro Deep Learning with TensorFlow: A Mathematical Approach to Advanced Artificial Intelligence in Python

Santanu Pattanayak

4.6
22

TensorFlow：实战Google深度学习框架（第2版）

郑泽宇，梁博文，顾思宇

4.6
23

Tensorflow for Deep Learning: From Linear Regression to Reinforcement Learning

Bharath Ramsundar, Reza Bosagh Zadeh

4.6
Learn how to solve challenging machine learning problems with TensorFlow, Google's revolutionary new software library for deep learning. If you have some background in basic linear algebra and calculus, this practical book introduces machine-learning fundamentals by showing you how to design systems capable of detecting objects in images, understanding text, analyzing video, and predicting the pro
24

TensorFlow 1.x Deep Learning Cookbook

Antonio Gulli and Amita Kapoor

4.6
25

Generative Adversarial Networks Projects: Build next-generation generative models using TensorFlow and Keras

Kailash Ahirwar

4.6