TensorFlow, the open source software library developed by the Google Brain Team as a framework for building deep learning neural networks.
It is also considered one of the best ways to build deep learning models by machine learning practitioners across the globe.
TensorFlow is considered to be used quite significantly in for deep learning in computer resources and it relies on a lot of data. If you are someone who wishes to get into ML, then these best TensorFlow books would surely be a highway to get to your goal.
It is hugely popular among practitioners across the globe, and hence they are on a constant lookout for more and more work related to this library.
Table of Contents
10 Best TensorFlow Books
Here I have tried to enlist 10 such best TensorFlow books that would help you understand TensorFlow and make your concepts clear.
1) Learning TensorFlow: A Guide to Building Deep Learning Systems [Check details on Amazon]
If you are looking for a book which provides you access to sample codes, then this best TensorFlow book should be the perfect choice for you. It helps those who have a bare-bones understanding of TensorFlow and helps you navigate from the online TensorFlow documentation available. You can also refer to this book again and again and go back to it to gain a better understanding and appreciation of TensorFlow.
The authors of this book – Tom Hope, Yehezkel Resheff and Italy Lieder, have provided a hands-on approach to TensorFlow for a broad technical audience.
Pros
- Learn how to build deep learning models from the ground up
- Deploy TensorFlow in a production setting
- Use clusters to distribute model training
Cons
- Certain allegations have been placed on this book regarding topics to be picked up directly from online documents
2) Deep Learning [check details on Amazon]
This best TensorFlow book is considered to be the bible in the deep learning industry. It is practically difficult to write a blog post regarding the fundamentals of deep leaning without mentioning Goodfellow, Bengio and Courville’s Deep Learning Text. This book is entirely theoretical and is written specifically for an academic audience. It teaches the fundamentals and theory surrounding deep learning in a college-level classroom. This book also covers modern deep learning algorithms and techniques. This book is also available for viewing for free from the book’s webpage itself. You could purchase a hardcopy as well.
Pros
- It covers more concepts and fundamentals
- This book will be helpful for you if you enjoy academic writing
Cons
- It focuses more on the theoretical aspects rather than the implementation
3) TensorFlow Machine Learning Projects [Check details on Amazon]
TensorFlow Machine Learning Projects help you to exploit the benefits of using TensorFlow in various real-world projects. Benefits range from simplicity, efficiency, and flexibility. Using this book, you not only learn how to use build advanced projects using different datasets but also be able to tackle common challenges using a range of libraries from the TensorFlow ecosystem.
Pros
- Understand the TensorFlow ecosystem using various datasets and techniques
- Build projects using CNN’s, NLP and Bayesian neural networks
- Generate your book script using RNNs
Cons
- The source code used in this book is available on GitHub
4) Mastering TensorFlow 1.x: Advanced Machine learning and Deep learning concepts using TensorFlow [check details on Amazon]
Mastering TensorFlow is one of the best TensorFlow books is a comprehensive guide that lets you explore the advanced features of TensorFlow 1.x. It helps you to gain insight into TensorFlow Core, Keras, TF Estimators, TFLEarn, TF Slim, Pretty Tensor and Sonnet. You will also be able to learn the advanced features of TensorFlow 1.x such as deploying production models with TensorFlow serving, building and designing TensorFlow models for mobile and embedded devices. Once you are done reading this book, you will be enabled with the skills to build smarter, faster and more efficient machine learning and deep learning systems.
Pros
- Master advanced concepts of deep learning such as transfer learning, reinforcement learning, generative models and much more
- Build end-to-end deep learning models using TensorFlow
Cons
- Descriptions are limited and not extensive
5) Hands-On Machine Learning with Scikit-Learn and TensorFlow[check details on Amazon]
This book is also a good read and you could easily breeze through it. The title should not deter you from reading right through the book. This book has been organized into two parts. The first part covers basic machine learning algorithms such as Support Vector Machines, Decisions, Trees, Random Forests, ensemble methods, and basic unsupervised algorithms. The second part then covers deep learning concepts such as the TensorFlow library.
Pros
- The best TensorFlow book is a good read if you are starting out with machine learning and would like to understand the core principles behind it
- You want to quickly learn how to operate TensorFlow
Cons
- The title may scare you away at first
6) TensorFlow Deep learning Cookbook [check details on Amazon]
If you prefer learning about TensorFlow but in a cookbook style method, then this best TensorFlow book would be the perfect choice for you. This book is a great reference for TensorFlow users and is entirely hands-on. It is not necessarily meant to teach deep learning concepts. However, it will show you how to operate the TensorFlow library in the context of deep learning.
Pros
- The cookbook style method of explanation is a fresh read
- You have already studied the fundamentals of deep learning and need a brush-up
Cons
- There are several typos in the book which could be irritating at times
7) Natural Language Processing with TensorFlow: Teach language to machines using Python’s deep learning library [check details on Amazon]
Through this book, Thushan Ganegedara aims at giving you a basic idea on TensorFlow and NLP basics. Eventually, you will learn how to use Word2Vec, including several advanced extensions, to create word embeddings that turn sequences of words into vectors, which is accessible to deep learning algorithms. You will also learn how to apply high-performance RNN models, like short-term memory cells, to NLK tasks.
Pros
- Helps to write automatic translation programs and implement an actual neural machine translator from scratch
- Helps learn about the innovations that are paving the way for NLP
Cons
- It fails when it comes to explaining the matrix math or line algebra
Conclusion
These were some of the best TensorFlow books you can buy to get started. If you’re new to machine learning, these best TensorFlow books will be highly helpful to you.
All of these books start from the basic and goes to the practical implementation. If you have followed any of these books, please share your experience with us. You may also check the best laptops for machine learning. Here are some more best-selling TensorFlow Books for you.
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- TensorFlow - Artificial Intelligence, AI Software Library design is perfect for computer science students, AI software developers and programmers who code in Python, NumPy, SciPy, Javascript, Java, Ruby, PHP, C#, C++, TypeScript etc programming languages
- TensorFlow is an open-source software library for machine learning and artificial intelligence. TensorFlow focuses on training and inference of deep neural networks. TensorFlow can be used in many programming languages i.e. Python, Javascript, C++ and Java
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Best TensorFlow Books
Summary
Follow these TensorFlow books which will help you get started. These are the good to go books on TensorFlow and Deep Learning which will help you start from scratch and reach the expertise level.
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