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Best Tensorflow Books to Read

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1. Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems (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 an intuitive understanding of the concepts and tools for building intelligent systems. You’ll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help you apply what you’ve learned, all you need is programming experience to get started.

  • Explore the machine learning landscape, particularly neural nets
  • Use scikit-learn to track an example machine-learning project end-to-end
  • Explore several training models, including support vector machines, decision trees, random forests, and ensemble methods
  • Use the TensorFlow library to build and train neural nets
  • Dive into neural net architectures, including convolutional nets, recurrent nets, and deep reinforcement learning
  • Learn techniques for training and scaling deep neural nets
  • Apply practical code examples without acquiring excessive machine learning theory or algorithm details

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 for a broad technical audience—from data scientists and engineers to students and researchers. You’ll begin by working through some basic examples in TensorFlow before diving deeper into topics such as neural network architectures, TensorBoard visualization, TensorFlow abstraction libraries, and multithreaded input pipelines. Once you finish this book, you’ll know how to build and deploy production-ready deep learning systems in TensorFlow.

  • Get up and running with TensorFlow, rapidly and painlessly
  • Learn how to use TensorFlow to build deep learning models from the ground up
  • Train popular deep learning models for computer vision and NLP
  • Use extensive abstraction libraries to make development easier and faster
  • Learn how to scale TensorFlow, and use clusters to distribute model training
  • Deploy TensorFlow in a production setting

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 in any industry are emphasized in this book. You will be able to use the prototypes demonstrated to build new deep learning applications. The code presented in the book is available in the form of iPython notebooks and scripts which allow you to try out examples and extend them in interesting ways.
You will be equipped with the mathematical foundation and scientific knowledge to pursue research in this field and give back to the community. 
What You’ll Learn
  • Understand full stack deep learning using TensorFlow and gain a solid mathematical foundation for deep learning
  • Deploy complex deep learning solutions in production using TensorFlow
  • Carry out research on deep learning and perform experiments using TensorFlow
Who This Book Is For
Data scientists and machine learning professionals, software developers, graduate students, and open source enthusiasts

Author(s): Santanu Pattanayak

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

Are you thinking of learning more about Deep Learning?

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. 

 Several Visual Illustrations and Examples

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 Is a Practical Guide Book

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, techniques, and applications of deep learning algorithms with the practical case studies and walk-through examples on which to practice.

This book takes a different approach that is based on providing simple examples of how deep learning algorithms work, and building on those examples step by step to encompass the more complicated parts of the algorithms. 

Python and Tensorflow Codes for the Examples Shown In the Book

You will build your Deep Learning Model by using Python and Tensorflow

There are many ways to build a deep learning model. However, it can also be overwhelming when you start, because there are so many tools to choose. In this book, we choose only these two tools: Tensorflow and Python.

Target Users

The book designed for a variety of target audiences. The most suitable users would include: 

  • Newbies in computer science techniques and deep learning
  • Professionals in data science and social sciences
  • Professors, lecturers or tutors who are looking to find better ways to explain the content to their students in the simplest and easiest way 
  • Students and academicians, especially those focusing on neural networks and deep learning

What’s inside this book?

  • Overview in Deep Learning
  • Quick Example to start
  • Popular Open Source Library
  • Pre-requisite for Deep Learning
  • Deep Learning Presentation
  • Deep Neural Networks Applications with Tensorflow and Python
  • Autoencoders Algorithms
  • Deep Learning for Computer Games
  • Anomaly Detection
  • Glossary of Some Useful Terms in Deep Learning
  • Useful References

Author(s): François Duval

5. Reinforcement Learning: With Open AI, TensorFlow and Keras Using Python (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 AI Gym. You’ll then learn about Swarm Intelligence with Python in terms of reinforcement learning.
The last part of the book starts with the TensorFlow environment and gives an outline of how reinforcement learning can be applied to TensorFlow. There’s also coverage of Keras, a framework that can be used with reinforcement learning. Finally, you’ll delve into Google’s Deep Mind and see scenarios where reinforcement learning can be used. 
What You’ll Learn 
  • Absorb the core concepts of the reinforcement learning process
  • Use advanced topics of deep learning and AI
  • Work with Open AI Gym, Open AI, and Python 
  • Harness reinforcement learning with TensorFlow and Keras using Python
Who This Book Is For

Data scientists, machine learning and deep learning professionals, developers who want to adapt and learn reinforcement learning.

Author(s): Abhishek Nandy, Manisha Biswas

6. Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow, 2nd Edition (2017)

Key Features

  • Second edition of the bestselling book on Machine Learning
  • A practical approach to key frameworks in data science, machine learning, and deep learning
  • Use the most powerful Python libraries to implement machine learning and deep learning
  • Get to know the best practices to improve and optimize your machine learning systems and algorithms

Book Description

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, and modern data analysis.

Fully extended and modernized, Python Machine Learning Second Edition now includes the popular TensorFlow deep learning library. The scikit-learn code has also been fully updated to include recent improvements and additions to this versatile machine learning library.

Sebastian Raschka and Vahid Mirjalili’s unique insight and expertise introduce you to machine learning and deep learning algorithms from scratch, and show you how to apply them to practical industry challenges using realistic and interesting examples. By the end of the book, you’ll be ready to meet the new data analysis opportunities in today’s world.

If you’ve read the first edition of this book, you’ll be delighted to find a new balance of classical ideas and modern insights into machine learning. Every chapter has been critically updated, and there are new chapters on key technologies. You’ll be able to learn and work with TensorFlow more deeply than ever before, and get essential coverage of the Keras neural network library, along with the most recent updates to scikit-learn.

What you will learn

  • Understand the key frameworks in data science, machine learning, and deep learning
  • Harness the power of the latest Python open source libraries in machine learning
  • Explore machine learning techniques using challenging real-world data
  • Master deep neural network implementation using the TensorFlow library
  • Learn the mechanics of classification algorithms to implement the best tool for the job
  • Predict continuous target outcomes using regression analysis
  • Uncover hidden patterns and structures in data with clustering
  • Delve deeper into textual and social media data using sentiment analysis

Table of Contents

  1. Giving Computers the Ability to Learn from Data
  2. Training Simple Machine Learning Algorithms for Classification
  3. A Tour of Machine Learning Classifiers Using Scikit-Learn
  4. Building Good Training Sets – Data Preprocessing
  5. Compressing Data via Dimensionality Reduction
  6. Learning Best Practices for Model Evaluation and Hyperparameter Tuning
  7. Combining Different Models for Ensemble Learning
  8. Applying Machine Learning to Sentiment Analysis
  9. Embedding a Machine Learning Model into a Web Application
  10. Predicting Continuous Target Variables with Regression Analysis
  11. Working with Unlabeled Data – Clustering Analysis
  12. Implementing a Multilayer Artificial Neural Network from Scratch
  13. Parallelizing Neural Network Training with TensorFlow
  14. Going Deeper – The Mechanics of TensorFlow
  15. Classifying Images with Deep Convolutional Neural Networks
  16. Modeling Sequential Data using Recurrent Neural Networks

Author(s): Sebastian Raschka, Vahid Mirjalili

7. TensorFlow for Deep Learning: From Linear Regression to Reinforcement 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 and builds understanding of deep learning foundations from the ground up. It’s ideal for practicing developers comfortable with designing software systems, but not necessarily with creating learning systems. This book is also useful for scientists and other professionals who are comfortable with scripting, but not necessarily with designing learning algorithms.

  • Gain in-depth knowledge of the TensorFlow API and primitives.
  • Understand how to train and tune machine learning systems with TensorFlow on large datasets.
  • Learn how to use TensorFlow with convolutional networks, recurrent networks, LSTMs, and reinforcement learning.

Author(s): Bharath Ramsundar, Reza Bosagh Zadeh

8. TensorFlow Machine Learning Cookbook (2017)

Key Features

  • Your quick guide to implementing TensorFlow in your day-to-day machine learning activities
  • Learn advanced techniques that bring more accuracy and speed to machine learning
  • Upgrade your knowledge to the second generation of machine learning with this guide on TensorFlow

Book Description

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.

This guide starts with the fundamentals of the TensorFlow library which includes variables, matrices, and various data sources. Moving ahead, you will get hands-on experience with Linear Regression techniques with TensorFlow. The next chapters cover important high-level concepts such as neural networks, CNN, RNN, and NLP.

Once you are familiar and comfortable with the TensorFlow ecosystem, the last chapter will show you how to take it to production.

What you will learn

  • Become familiar with the basics of the TensorFlow machine learning library
  • Get to know Linear Regression techniques with TensorFlow
  • Learn SVMs with hands-on recipes
  • Implement neural networks and improve predictions
  • Apply NLP and sentiment analysis to your data
  • Master CNN and RNN through practical recipes
  • Take TensorFlow into production

About the Author

Nick McClure is currently a senior data scientist at PayScale, Inc. in Seattle, WA. Prior to this, he has worked at Zillow and Caesar’s Entertainment. He got his degrees in Applied Mathematics from The University of Montana and the College of Saint Benedict and Saint John’s University.

He has a passion for learning and advocating for analytics, machine learning, and artificial intelligence. Nick occasionally puts his thoughts and musings on his Twitter account, @nfmcclure.

Table of Contents

  1. Getting Started with TensorFlow
  2. The TensorFlow Way
  3. Linear Regression
  4. Support Vector Machines
  5. Nearest Neighbor Methods
  6. Neural Networks
  7. Natural Language Processing
  8. Convolutional Neural Networks
  9. Recurrent Neural Networks
  10. Taking TensorFlow to Production
  11. More with 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.

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 representing them as graphs and efficiently mapping parts of the graphs to machines in a cluster or to the processors of a single machine.

About the Book

Machine Learning with TensorFlow gives readers a solid foundation in machine-learning concepts plus hands-on experience coding TensorFlow with Python. You’ll learn the basics by working with classic prediction, classification, and clustering algorithms. Then, you’ll move on to the money chapters: exploration of deep-learning concepts like autoencoders, recurrent neural networks, and reinforcement learning. Digest this book and you will be ready to use TensorFlow for machine-learning and deep-learning applications of your own.

What’s Inside

  • Matching your tasks to the right machine-learning and deep-learning approaches
  • Visualizing algorithms with TensorBoard
  • Understanding and using neural networks

About the Reader

Written for developers experienced with Python and algebraic concepts like vectors and matrices.

About the Author

Author Nishant Shukla is a computer vision researcher focused on applying machine-learning techniques in robotics.

Senior technical editor, Kenneth Fricklas, is a seasoned developer, author, and machine-learning practitioner.

Table of Contents


  1. A machine-learning odyssey
  2. TensorFlow essentials

  4. Linear regression and beyond
  5. A gentle introduction to classification
  6. Automatically clustering data
  7. Hidden Markov models

  9. A peek into autoencoders
  10. Reinforcement learning
  11. Convolutional neural networks
  12. Recurrent neural networks
  13. Sequence-to-sequence models for chatbots
  14. Utility landscape

Author(s): Nishant Shukla

10. Machine Learning with TensorFlow 1.x: Second generation machine learning with Google’s brainchild – TensorFlow 1.x (2017)

Tackle common commercial machine learning problems with Google’s TensorFlow 1.x library and build deployable solutions.

About This Book

  • Enter the new era of second-generation machine learning with Python with this practical and insightful guide
  • Set up TensorFlow 1.x for actual industrial use, including high-performance setup aspects such as multi-GPU support
  • Create pipelines for training and using applying classifiers using raw real-world data

Who This Book Is For

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 industries using TensorFlow’s unique features. No commercial domain knowledge is required, but familiarity with Python and matrix math is expected.

What You Will Learn

  • Explore how to use different machine learning models to ask different questions of your data
  • Learn how to build deep neural networks using TensorFlow 1.x
  • Cover key tasks such as clustering, sentiment analysis, and regression analysis using TensorFlow 1.x
  • Find out how to write clean and elegant Python code that will optimize the strength of your algorithms
  • Discover how to embed your machine learning model in a web application for increased accessibility
  • Learn how to use multiple GPUs for faster training using AWS

In Detail

Google’s TensorFlow is a game changer in the world of machine learning. It has made machine learning faster, simpler, and more accessible than ever before. This book will teach you how to easily get started with machine learning using the power of Python and TensorFlow 1.x.

Firstly, you’ll cover the basic installation procedure and explore the capabilities of TensorFlow 1.x. This is followed by training and running the first classifier, and coverage of the unique features of the library including data flow graphs, training, and the visualization of performance with TensorBoard—all within an example-rich context using problems from multiple industries. You’ll be able to further explore text and image analysis, and be introduced to CNN models and their setup in TensorFlow 1.x. Next, you’ll implement a complete real-life production system from training to serving a deep learning model. As you advance you’ll learn about Amazon Web Services (AWS) and create a deep neural network to solve a video action recognition problem. Lastly, you’ll convert the Caffe model to TensorFlow and be introduced to the high-level TensorFlow library, TensorFlow-Slim.

By the end of this book, you will be geared up to take on any challenges of implementing TensorFlow 1.x in your machine learning environment.

Style and approach

This comprehensive guide will enable you to understand the latest advances in machine learning and will empower you to implement this knowledge in your machine learning environment.

Author(s): Quan Hua, Shams Ul Azeem

11. TensorFlow 1.x Deep Learning Cookbook: Over 90 unique recipes to solve artificial-intelligence driven problems with Python (2017)

Take the next step in implementing various common and not-so-common neural networks with Tensorflow 1.x

Key Features

  • Skill up and implement tricky neural networks using Google’s TensorFlow 1.x
  • An easy-to-follow guide that lets you explore reinforcement learning, GANs, autoencoders, multilayer perceptrons and more.
  • Hands-on recipes to work with Tensorflow on desktop, mobile, and cloud environment

Book Description

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 problems in artificial intelligence domain.

In this book, you will learn how to efficiently use TensorFlow, Google’s open source framework for deep learning. You will implement different deep learning networks such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Deep Q-learning Networks (DQNs), and Generative Adversarial Networks (GANs) with easy to follow independent recipes. You will learn how to make Keras as backend with TensorFlow.

With a problem-solution approach, you will understand how to implement different deep neural architectures to carry out complex tasks at work. You will learn the performance of different DNNs on some popularly used data sets such as MNIST, CIFAR-10, Youtube8m, and more. You will not only learn about the different mobile and embedded platforms supported by TensorFlow but also how to set up cloud platforms for deep learning applications. Get a sneak peek of TPU architecture and how they will affect DNN future.

By using crisp, no-nonsense recipes, you will become an expert in implementing deep learning techniques in growing real-world applications and research areas such as reinforcement learning, GANs, autoencoders and more.

What you will learn

  • Install TensorFlow and use it for CPU and GPU operations
  • Implement DNNs and apply them to solve different AI-driven problems.
  • Leverage different data sets such as MNIST, CIFAR-10, and Youtube8m with TensorFlow and learn how to access and use them in your code.
  • Use TensorBoard to understand neural network architectures, optimize the learning process, and peek inside the neural network black box.
  • Use different regression techniques for prediction and classification problems
  • Build single and multilayer perceptrons in TensorFlow
  • Implement CNN and RNN in TensorFlow, and use it to solve real-world use cases.
  • Learn how restricted Boltzmann Machines can be used to recommend movies.
  • Understand the implementation of Autoencoders and deep belief networks, and use them for emotion detection.
  • Master the different reinforcement learning methods to implement game playing agents.
  • GANs and their implementation using TensorFlow.

Table of Contents

  1. Initial steps in Tensorflow 1.x
  2. Regression
  3. Neural Networks: Perceptrons
  4. Convolutional Neural Network
  5. CNN in Action
  6. Recurrent Neural Networks
  7. Unsupervised Learning
  8. Autoencoders
  9. Reinforcement Learning
  10. Tensorflow Mobile
  11. Generative Adverasial Networks
  12. Deep Learning on Cloud
  13. Appendix
  14. Appendix B : Learning to Learn with AutoML (or what is Meta-Learning)

Author(s): Antonio Gulli, Amita Kapoor

12. Getting Started with TensorFlow (2016)

Key Features

  • Get the first book on the market that shows you the key aspects TensorFlow, how it works, and how to use it for the second generation of machine learning
  • Want to perform faster and more accurate computations in the field of data science? This book will acquaint you with an all-new refreshing library—TensorFlow!
  • Dive into the next generation of numerical computing and get the most out of your data with this quick guide

Book Description

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. As you progress further, you’ll learn to implement various machine learning techniques such as classification, clustering, neural networks, and deep learning through practical examples.

By the end of this book, you’ll have gained hands-on experience of using TensorFlow and building classification, image recognition systems, language processing, and information retrieving systems for your application.

What you will learn

  • Install and adopt TensorFlow in your Python environment to solve mathematical problems
  • Get to know the basic machine and deep learning concepts
  • Train and test neural networks to fit your data model
  • Make predictions using regression algorithms
  • Analyze your data with a clustering procedure
  • Develop algorithms for clustering and data classification
  • Use GPU computing to analyze big data

About the Author

Giancarlo Zaccone has more than 10 years of experience managing research projects in both the scientific and industrial domains. He worked as researcher at the C.N.R, the National Research Council, where he was involved in projects related to parallel numerical computing and scientific visualization.

Currently, he is a senior software engineer at a consulting company developing and maintaining software systems for space and defence applications.

Giancarlo holds a master’s degree in physics from the Federico II of Naples and a 2nd level postgraduate master course in scientific computing from La Sapienza of Rome.

He has already been a Packt author for the following book: Python Parallel Programming Cookbook.

You can contact him at https://it.linkedin.com/in/giancarlozaccone

Table of Contents

  1. TensorFlow – Basic Concepts
  2. Doing Math with TensorFlow
  3. Starting with Machine Learning
  4. Introducing Neural Networks
  5. Deep Learning
  6. GPU Programming and Serving with TensorFlow

Author(s): Giancarlo Zaccone