# Best TensorFlow Books to Build Machine Learning Models

TensorFlow is the most popular library for working with machine learning. It is equally useful for individuals and businesses ranging from startups to companies as large as Google. TensorFlow is currently being used for natural language processing, artificial intelligence, computer vision, and predictive analytics.

If you want to master this technology, reading good books is crucial. Today in this article, I've gathered a number of best TensorFlow books using a machine learning algorithm as well. So that you can be assured to choose them without any concern. So hurry up and scroll down the list!

** Hands-On Machine Learning with Scikit-Learn and TensorFlow** is a very good book to start your journey with machine learning. If you don't know much about deep learning, don't worry.

This practical book shows you how you can use simple and efficient tools to implement programs that are capable of learning from data. With simple and concrete examples, minimal theory and two production-ready Python frameworks, it helps you to understand the basic concepts and tools for building an intelligent system.

To make this book worth reading, you just need a little programming experience.

**What You'll learn:**

- Fundamental machine learning concepts
- Basics of neural network
- Using scikit-learn to track an example machine learning project
- Working with support vector machine and decision trees
- Using random forests and ensemble methods
- Using the TensorFlow library to train neural nets
- Creating neural network architectures, including convolutional nets, recurrent nets, and deep reinforcement learning
- Useful techniques for training and scaling deep neural networks

**What you'll learn:**

- Basics of the TensorFlow machine learning library including variables, matrices, and various data sources
- Exploring Linear Regression techniques with TensorFlow
- Working with Support Vector Machines
- Building training models
- model evaluation
- Applying sentiment analysis, regression analysis, and clustering analysis
- Building neural networks to improve predictions
- Natural Language Processing
- Building Convolutional Neural Networks
- Taking TensorFlow to Production

**Author:**Sam Abrahams,Danijar Hafner,Erik Erwitt,Ariel Scarpinelli

**Published at:**10/11/2016

**ISBN:**1939902452

This TensorFlow book gives a proper overview of TensorFlow in a clear and concise manner. It's suitable for anyone from beginner to the advanced level machine learning practitioner.

By using concrete and real-world examples, it introduces you to the TensorFlow framework and the underlying machine learning concepts that are important to work with machine intelligence.

Completing this book, you'll have a deep understanding of the core TensorFlow API.

**What you'll learn:**

- Introduction to deep learning and TensorFlow library
- Basics of machine learning
- Object recognition and classification with convolutional neural networks
- Building recurrent neural networks
- Natural language processing
- Deploying models in production
- Writing helper function
- Code structure and classes

** Learning TensorFlow** is a hands-on guide that will teach you all the aspects of Tensorflow--from basics to advanced level concepts.

It begins with some basic examples in TensorFlow and gradually dives deeper into topics such as neural network architecture, TensorBoard visualization, and multithreaded input pipelines. With this book, you'll be able to build and deploy production-ready deep learning systems in TensorFlow.

It's suitable for a broad technical audience—from data scientists and engineers to students and researchers.

**What you'll learn:**

- Understanding TensorFlow fundamentals
- Using TensorFlow to build deep learning models from scratch
- Convolutional neural networks
- Working with text and sequence using TensorBoard visualization
- Word vectors, advanced RNN, and embedding visualization
- Using TensorFlow abstraction to make development easier and faster
- Using clusters to distribute model training
- Exporting and serving models

**What You'll learn:**

- Understanding full stack deep learning using TensorFlow
- Deploying complex deep learning solutions
- Working with different deep learning architectures
- A solid mathematical foundation to understand the concepts better
- Required skills to carry out research on deep learning
- Performing experiments using TensorFlow

If you want to understand deep learning from the ground up and build working deep learning models from scratch, then read this book.

This practical book shows you how to and where to use deep learning architectures. With this book, you'll be able to design and build systems capable of detecting objects in images, understanding human speech, analyzing video and predicting the properties of potential medicines.

**What you'll learn:**

- Fundamentals of deep learning and TensorFlow
- Working with TensorFlow API and primitives
- Linear regression with TensorFlow
- Building a fully connected Deep network with TensorFlow
- Building convolutional neural networks
- Training and tuning machine learning systems with TensorFlow on large datasets
- Distributed training and reinforcement learning

** R Deep Learning Cookbook **provides powerful and independent recipes to build deep learning models in different application areas using R and TensorFlow libraries.

With practical and working examples to build systems for text, image and speech recognition. You'll also be able to set-up deep learning models using CPU and GPU.

**What you'll learn:**

- Building deep learning models using TensorFlow, H2O, and MXnet
- Analyzing a Deep Boltzmann machine
- Building a supervised model using various machine learning algorithms
- Setting up and Analysing Deep belief networks
- Representing data using Autoencoders
- Building sequence modeling using Recurrent nets
- Applying Deep Learning in text mining
- Using generative models available in Deep Learning
- Understanding basic convolution function
- Fundamentals of Reinforcement Learning
- Applying deep learning in signal processing
- Training a deep learning model on a GPU

**What you'll learn:**

- Installing and using TensorFlow for CPU and GPU options
- Implementing Deep neural networks
- Using the TensorBoard to understand the architecture of the neural network
- Using different regression techniques for the task of prediction and classification
- Implementing convolutional neural networks
- Building recurrent neural networks to perform the task of text generation
- Understanding the implementation of Autoencoders, and deep belief networks
- Different Reinforcement Learning methods and their implementation

** Mastering TensorFlow** is a comprehensive guide that helps you to master all the core and advanced machine learning concepts using the TensorFlow book.

Covering basic uses of TensorFlow library, including TensorFlow Core, TensorBoard, and TensorFlow Serving it'll take you through machine learning algorithms from Classification, Regression, and Clustering, and many more domains.

Completing this book, you will be able to build and scale neural network models and deep learning applications of your own.

**What you'll learn:**

- Advanced concepts of Tensorflow 1.x like Ensembles, Transfer Learning, Reinforcement Learning and GAN
- Performing unsupervised learning (Clustering) with TensorFlow
- Performing supervised learning (Classification, Regression) with TensorFlow
- Building end-to-end Deep Learning (CNN, RNN, Autoencoders) models with TensorFlow
- Working with varied datasets such as MNIST, CIFAR-10, and the latest YouTube-8M database
- Building TensorFlow models with Keras, TFLearn, and R
- Scaling and deploying production models with distributed and high-performance Tensorflow on GPU and Clusters
- Building TensorFlow models on iOS and Android devices

* Machine Learning with TensorFlow* will teach you machine learning algorithms and how to implement solutions with TensorFlow.

It starts with an overview of machine learning concepts and moves on to the essentials needed to begin using TensorFlow. Each chapter zooms into a prominent example of machine learning.

You can cover them all to master the basics.

**What you'll learn:**

- Fundamental machine learning concepts
- Basics of TensorFlow
- Building neural networks
- Solving various classification and prediction problems in the real world
- Regression analysis
- Building machine learning models to solve various classification and prediction problems in the real world
- Using cluster for distributed model training