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Best Tensorflow Books to Read

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 April and ensure you’re getting smarter. We have also mentioned the brief introduction of each book based on the relevant Amazon or Reddit descriptions.

1. Hands-On Machine Learning with Scikit-Learn and TensorFlow (2017)

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. By using concrete examples, minimal theory, and two production-ready Python frameworks—scikit-learn and TensorFlow—author Aurélien Géron helps you gain…

Author(s): Aurélien Géron

2. Learning TensorFlow: A Guide to Building Deep Learning Systems (2017)

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 analytics. Authors Tom Hope, Yehezkel Resheff, and Itay Lieder provide a hands-on approach to TensorFlow fundamentals…

Author(s): Tom Hope, Yehezkel S. Resheff

3. Pro Deep Learning with TensorFlow: A Mathematical Approach to Advanced Artificial 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. Pro Deep Learning with TensorFlow provides practical, hands-on expertise so you can learn deep learning from scratch and deploy meaningful deep learning solutions. This book will allow you to get up to speed quickly using TensorFlow and to optimize different deep learning architectures. All of the practical aspects of deep learning that are relevant…

Author(s): Santanu Pattanayak

4. Deep Learning for Beginners: Practical Guide with Python and Tensorflow (Data Sciences) (2017)

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. This book will help you explore exactly what deep learning is and will also teach you about why it is so revolutionary and fascinating. The chapters will introduce the reader to the concepts…

Author(s): François Duval

5. 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. Reinforcement Learning discusses algorithm implementations important for reinforcement learning, including Markov’s Decision process and Semi Markov Decision process. The next section shows you how to get started with Open AI  before looking at Open…
Author(s): Abhishek Nandy, Manisha Biswas

6. 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. Thoroughly updated using the latest Python open source libraries, this book offers the practical knowledge and techniques you need to create and contribute to machine learning, deep learning…

Author(s): Sebastian Raschka, Vahid Mirjalili

7. 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. You’ll learn how to design systems capable of detecting objects in images, understanding human speech, analyzing video, and predicting the properties of potential medicines. TensorFlow for Deep Learning teaches concepts through practical examples…

Author(s): Bharath Ramsundar, Reza Bosagh Zadeh

8. TensorFlow Machine Learning Cookbook (2017)

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

9. Machine Learning with TensorFlow (2018)

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. Machine Learning with TensorFlow gives readers a solid foundation in machine…

Author(s): Nishant Shukla

10. Machine Learning with TensorFlow (2017)

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

11. TensorFlow Deep Learning Cookbook (2017)

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. This exciting recipe-based guide will take you from the realm of DNN theory to implementing them practically to solve the real-life…

Author(s): Antonio Gulli, Amita Kapoor

12. Getting Started with TensorFlow (2016)

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

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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 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.

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

Author(s): Ahmed Fawzy Gad
Publisher: Apress, Year: 2019, Size: 10 Mb, Ext: pdf
ID: 2295779

Python Deep Learning: Exploring deep learning techniques, neural network architectures and GANs with PyTorch, Keras and TensorFlow

Author(s): Ivan Vasilev, Daniel Slater, Gianmario Spacagna, Peter Roelants, Valentino Zocca
Publisher: Packt Publishing, Year: 2019, Size: 24 Mb, Ext: pdf
ID: 2319010

Машинное обучение и TensorFlow

Author(s): Нишант Шакла
Publisher: Питер, Year: 2019, Size: 47 Mb, Ext: pdf
ID: 2322908

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

Author(s): Ahmed Fawzy Gad
Publisher: Apress, Year: 2019, Size: 14 Mb, Ext: epub
ID: 2329578

Reinforcement Learning : With Open AI, TensorFlow and Keras Using Python

Author(s): Abhishek Nandy,Manisha Biswas
Publisher: Apress, Year: 2018, Size: 11 Mb, Ext: pdf
ID: 2178978

Machine Learning with TensorFlow

Author(s): Nishant Shukla
Publisher: Manning Publications, Year: 2018, Size: 7 Mb, Ext: pdf
ID: 2196910

Deep Learning for Computer Vision: Expert techniques to train advanced neural networks using TensorFlow and Keras

Author(s): Rajalingappaa Shanmugamani
Publisher: Packt Publishing, Year: 2018, Size: 58 Mb, Ext: epub
ID: 2199952

Deep Learning with TensorFlow

Author(s): Giancarlo Zaccone, Md. Rezaul Karim
Publisher: Packt Publishing, Year: 2018, Size: 17 Mb, Ext: epub
ID: 2209443

Machine Learning with TensorFlow

Author(s): Nishant Shukla with Kenneth Fricklas
Publisher: Manning Publications, Year: 2018, Size: 11 Mb, Ext: pdf
ID: 2213928

Machine Learning with TensorFlow

Author(s): Nishant Shukla, Kenneth Fricklas
Publisher: Manning Publications, Year: 2018, Size: 11 Mb, Ext: pdf
ID: 2216934

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To discover the 7 best books for studying deep learning, just keep reading! The 7 best deep learning books you should be reading right now. This deep learning book is entirely hands-on and is a great reference for TensorFlow users. Again, this book is not meant to necessarily teach deep learning, The 10 Best Deep Learning Books You Need To Read Right Now! So without further ado, here are the 12 best Deep Learning books on the interwebs at the moment. Enjoy. Hands-On Machine Learning with Scikit-Learn and TensorFlow. First up, and the best one in this roundup in my opinion, is a book that takes a “hands-on” approach to Deep Learning Books Advanced Search New Releases Amazon Charts Best Sellers & More The New York Times® Best Sellers Children’s Books Textbooks Textbook Rentals Sell Us Your Books Best Books of the Month Kindle eBooks What are some good books on TensorFlow? Update Cancel. a d b y L a m b d a L a b s. What’s the best hands-on TensorFlow book? What are some good books? it helps you to understand the basic concepts and tools for building intelligent system. To make this book worth reading, you just need a little programming experience. Book Reviews. We read a lot of books on TensorFlow. On this page you’ll find a reviews on them, growing as we review more books. If you have any suggestions, please let us know at contact@datapipeline.com.au