In this post, we have prepared a curated top list of reading recommendations for beginners and experienced. This hand-picked list of the best Tensorflow books and tutorials can help fill your brain this July and ensure you’re getting smarter. We have also mentioned the brief introduction of each book based on the relevant Amazon or Reddit descriptions.
- Hands-On Machine Learning with Scikit-Learn and TensorFlow (2017)
- Learning TensorFlow: A Guide to Building Deep Learning Systems (2017)
- Pro Deep Intelligence in Python (2017)
- Deep Learning for Beginners: Practical Guide with Python and Tensorflow (Data Sciences) (2017)
- Reinforcement Learning (2017)
- Python Machine Learning (2017)
- TensorFlow for Deep Learning (2018)
- TensorFlow Machine Learning Cookbook (2017)
- Machine Learning with TensorFlow (2018)
- Machine Learning with TensorFlow (2017)
- TensorFlow Deep Learning Cookbook (2017)
- Getting Started with TensorFlow (2016)
Graphics in this book are printed in black and white. 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.
Author(s): Aurélien Géron
Roughly inspired by the human brain, deep neural networks trained with large amounts of data can solve complex tasks with unprecedented accuracy.
Author(s): Tom Hope, Yehezkel S. Resheff
Pro Deep Intelligence in Python (2017)
Deploy deep learning solutions in production with ease using TensorFlow. You’ll also develop the mathematical understanding and intuition required to invent new deep learning architectures and solutions on your own.
Author(s): Santanu Pattanayak
If you are looking for a book to help you understand how the deep learning works by using Python and Tensorflow, then this is a good book for you. Equations are great for really understanding every last detail of an algorithm. But to get a basic idea of how something works,this book contains several graphs which detail each neural networks and deep learning algorithms. It is contains also several graphs for practical examples.
Author(s): François Duval
Reinforcement Learning (2017)
Master reinforcement learning, a popular area of machine learning, starting with the basics: discover how agents and the environment evolve and then gain a clear picture of how they are inter-related. You’ll then work with theories related to reinforcement learning and see the concepts that build up the reinforcement learning process.
Author(s): Abhishek Nandy, Manisha Biswas
Python Machine Learning (2017)
Machine learning is eating the software world, and now deep learning is extending machine learning. Understand and work at the cutting edge of machine learning, neural networks, and deep learning with this second edition of Sebastian Raschka’s bestselling book, Python Machine Learning.
Author(s): Sebastian Raschka, Vahid Mirjalili
TensorFlow for Deep Learning (2018)
Learn how to solve challenging machine learning problems with Tensorflow, Google’s revolutionary new system for deep learning. If you have some background with basic linear algebra and calculus, this practical book shows you how to build—and when to use—deep learning architectures.
Author(s): Bharath Ramsundar, Reza Bosagh Zadeh
TensorFlow is an open source software library for Machine Intelligence. The independent recipes in this book will teach you how to use TensorFlow for complex data computations and will let you dig deeper and gain more insights into your data than ever before. You’ll work through recipes on training models, model evaluation, sentiment analysis, regression analysis, clustering analysis, artificial neural networks, and deep learning each using Google’s machine learning library TensorFlow.
Author(s): Nick McClure
Machine Learning with TensorFlow gives readers a solid foundation in machine-learning concepts plus hands-on experience coding TensorFlow with Python. TensorFlow, Google’s library for large-scale machine learning, simplifies often-complex computations by representing them as graphs and efficiently mapping parts of the graphs to machines in a cluster or to the processors of a single machine.
Author(s): Nishant Shukla
Tackle common commercial machine learning problems with Google’s TensorFlow 1.x library and build deployable solutions. This book is for data scientists and researchers who are looking to either migrate from an existing machine learning library or jump into a machine learning platform headfirst. The book is also for software developers who wish to learn deep learning by example. Particular focus is placed on solving commercial deep learning problems from several
Author(s): Quan Hua, Shams Ul Azeem
Take the next step in implementing various common and not-so-common neural networks with Tensorflow 1.x. Deep neural networks (DNNs) have achieved a lot of success in the field of computer vision, speech recognition, and natural language processing. The entire world is filled with excitement about how deep networks are revolutionizing artificial intelligence.
Author(s): Antonio Gulli, Amita Kapoor
Google’s TensorFlow engine, after much fanfare, has evolved in to a robust, user-friendly, and customizable, application-grade software library of machine learning (ML) code for numerical computation and neural networks. This book takes you through the practical software implementation of various machine learning techniques with TensorFlow. In the first few chapters, you’ll gain familiarity with the framework and perform the mathematical operations required for data analysis.
Author(s): Giancarlo Zaccone
Best Tensorflow Books to Read
We highly recommend you to buy all paper or e-books in a legal way, for example, on Amazon. But sometimes it might be a need to dig deeper beyond the shiny book cover. Before making a purchase, you can visit resources like Library Genesis and download some Tensorflow books mentioned below at your own risk. Once again, we do not host any illegal or copyrighted files, but simply give our visitors a choice and hope they will make a wise decision.
Deep Learning Pipeline: Building A Deep Learning Model With TensorFlow
Author(s): Hisham El-Amir, Mahmoud Hamdy
ID: 2452069, Publisher: 2020, Year: Apress, Size: 12 Mb, Format: pdf
Beginning Deep Learning with TensorFlow: Work with Keras, MNIST Data Sets, and Advanced Neural Networks
Author(s): Liangqu Long, Xiangming Zeng
ID: 3202862, Publisher: Apress, Year: 2022, Size: 42 Mb, Format: epub
Applied Deep Learning with TensorFlow 2: Learn to Implement Advanced Deep Learning Techniques with Python
Author(s): U. Michelucci
ID: 3250499, Publisher: , Year: 2022, Size: 6 Mb, Format: pdf
Please note that this booklist is not final. Some books are truly record-breakers according to The New York Times, others are written by unknown writers. On top of that, you can always find additional tutorials and courses on Coursera, Udemy or edX, for example. Are there any other relevant links you could recommend? Drop a comment if you have any feedback on the list.