Best TensorFlow Books 2022 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 2022 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!
HandsOn Machine Learning with ScikitLearn 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 productionready 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 will learn:

Fundamental machine learning concepts

Basics of neural network

Using scikitlearn 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
You'll begin by working through some basic examples in TensorFlow.js before diving deeper into neural network architectures, DataFrames, TensorFlow Hub, model conversion, transfer learning, and more. Once you finish this book, you'll know how to build and deploy productionreadydeep learning systems with TensorFlow.js.
 Explore tensors, the most fundamental structure of machine learning
 Convert data into tensors and back with a realworld example
 Combine AI with the web using TensorFlow.js
 Use resources to convert, train, and manage machine learning data
 Build and train your own training models from scratch
Throughout, Ekman provides concise, wellannotated code examples using TensorFlow with Keras. Corresponding PyTorch examples are provided online, and the book thereby covers the two dominating Python libraries for DL used in industry and academia. He concludes with an introduction to neural architecture search (NAS), exploring important ethical issues and providing resources for further learning.
 Explore and master core concepts: perceptrons, gradientbased learning, sigmoid neurons, and back propagation
 See how DL frameworks make it easier to develop more complicated and useful neural networks
 Discover how convolutional neural networks (CNNs) revolutionize image classification and analysis
 Apply recurrent neural networks (RNNs) and long shortterm memory (LSTM) to text and other variablelength sequences
 Master NLP with sequencetosequence networks and the Transformer architecture
 Build applications for natural language translation and image captioning
Data scientists, machine learning engineers, and DevOps engineers will discover how to go beyond model development to successfully productize their data science projects, while managers will better understand the role they play in helping to accelerate these projects.
 Understand the steps that make up a machine learning pipeline
 Build your pipeline using components from TensorFlow Extended
 Orchestrate your machine learning pipeline with Apache Beam, Apache Airflow and Kubeflow Pipelines
 Work with data using TensorFlow Data Validation and TensorFlow Transform
 Analyze a model in detail using TensorFlow Model Analysis
 Examine fairness and bias in your model performance
 Deploy models with TensorFlow Serving or convert them to TensorFlow Lite for mobile devices
 Understand privacypreserving machine learning techniques
You'll start off with the basics – learning how to load data into TensorFlow, perform tensor operations, and utilize common optimizers and activation functions. As you progress, you'll experiment with different TensorFlow development tools, including TensorBoard, TensorFlow Hub, and Google Colab, before moving on to solve regression and classification problems with sequential models. Building on this solid foundation, you'll learn how to tune models and work with different types of neural network, getting handson with realworld deep learning applications such as text encoding, temperature forecasting, image augmentation, and audio processing.
What you will learn
 Get to grips with TensorFlow's mathematical operations
 Preprocess a wide variety of tabular, sequential, and image data
 Understand the purpose and usage of different deep learning layers
 Perform hyperparametertuning to prevent overfitting of training data
 Use pretrained models to speed up the development of learning models
 Generate new data based on existing patterns using generative models
The book covers key emerging areas such as generating text for use in sentence completion and text summarization, bridging images and text by generating captions for images, and managing dialogue aspects of chatbots. You will learn how to apply transfer learning and finetuning using TensorFlow 2. Further, it covers practical techniques that can simplify the labelling of textual data. The book also has a working code that is adaptable to your use cases for each tech piece.
What you will learn
 Grasp important presteps in building NLP applications like POS tagging
 Use transfer and weakly supervised learning using libraries like Snorkel
 Do sentiment analysis using BERT
 Apply encoderdecoder NN architectures and beam search for summarizing texts
 Use Transformer models with attention to bring images and text together
 Build apps that generate captions and answer questions about images using custom Transformers
 Use advanced TensorFlow techniques like learning rate annealing, custom layers, and custom loss functions to build the latest DeepNLP models
Pete Warden and Daniel Situnayake explain how you can train models small enough to fit into any environment. Ideal for software and hardware developers who want to build embedded systems using machine learning, this guide walks you through creating a series of TinyML projects, stepbystep. No machine learning or microcontroller experience is necessary.
 Build a speech recognizer, a camera that detects people, and a magic wand that responds to gestures
 Work with Arduino and ultralowpower microcontrollers
 Learn the essentials of ML and how to train your own models
 Train models to understand audio, image, and accelerometer data
 Explore TensorFlow Lite for Microcontrollers, Google’s toolkit for TinyML
 Debug applications and provide safeguards for privacy and security
 Optimize latency, energy usage, and model and binary size
With six new chapters, on topics including movie recommendation engine development with Naive Bayes, recognizing faces with support vector machine, predicting stock prices with artificial neural networks, categorizing images of clothing with convolutional neural networks, predicting with sequences using recurring neural networks, and leveraging reinforcement learning for making decisions, the book has been considerably updated for the latest enterprise requirements. This book provides actionable insights on the key fundamentals of ML with Python programming. Hayden applies his expertise to demonstrate implementations of algorithms in Python, both from scratch and with libraries.
Each chapter walks through an industryadopted application. With the help of realistic examples, you will gain an understanding of the mechanics of ML techniques in areas such as exploratory data analysis, feature engineering, classification, regression, clustering, and NLP.
What you will learn
 Understand the important concepts in ML and data science
 Use Python to explore the world of data mining and analytics
 Scale up model training using varied data complexities with Apache Spark
 Delve deep into text analysis and NLP using Python libraries such NLTK and Gensim
 Select and build an ML model and evaluate and optimize its performance
 Implement ML algorithms from scratch in Python, TensorFlow 2, PyTorch, and scikitlearn
This is a quick starter guide to learn how to work with the TensorFlow library effectively. It follows a recipebased approach that practically explains all the concepts of machine learning and TensorFlow library.
This book will teach how you can use TensorFlow for complex data computations and help you gain more insights into your data than ever before.
It also provides advanced techniques that bring more accuracy and speed to machine learning.
What You Will 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
This 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 realworld 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 Will 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 handson guide that will teach you all the aspects of Tensorflowfrom 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 productionready deep learning systems in TensorFlow.
It's suitable for a broad technical audience—from data scientists and engineers to students and researchers.
What You Will 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
This TensorFlow book is ideal for anyone who wants to become an expert in deep learning. Using the popular deep learning library, TensorFlow, it provides practical expertise to make you understand how to build and deploy meaningful deep learning solutions from scratch.
With this book, you'll be able to use TensorFlow to optimize different deep learning architectures. You can build a new deep learning application using the prototypes demonstrated in the book. You'll also develop the mathematical understanding and intuition required to invent new deep learning architectures and solutions on your own.
What You Will 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 Will 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 setup deep learning models using CPU and GPU.
What You Will 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
If you want to develop your skill to implement advanced techniques in deep learning using TensorFlow, then try this book.
This practical book full of realworld examples will teach you how to apply deep learning techniques in various applications. You will understand how to implement different deep neural architectures in Tensorflow.
You'll also learn how to use TensorFlow in different mobile and embedded platforms and set up cloud platforms for deep learning applications.
What You Will 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
Mastering TensorFlow 1.x 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 Will 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 endtoend Deep Learning (CNN, RNN, Autoencoders) models with TensorFlow

Working with varied datasets such as MNIST, CIFAR10, and the latest YouTube8M database

Building TensorFlow models with Keras, TFLearn, and R

Scaling and deploying production models with distributed and highperformance 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 Will 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
You'll learn how to write deep learning applications in the most powerful, popular, and scalable machine learning stack available. TensorFlow is the machine learning library of choice for professional applications, while Keras offers a simple and powerful Python API for accessing TensorFlow. TensorFlow 2 provides full Keras integration, making advanced machine learning easier and more convenient than ever before. This book also introduces neural networks with TensorFlow, runs through the main applications (regression, ConvNets (CNNs), GANs, RNNs, NLP), covers two working example apps, and then dives into TF in production, TF mobile, and using TensorFlow with AutoML.
What you will learn
 Build machine learning and deep learning systems with TensorFlow 2 and the Keras API
 Use Regression analysis, the most popular approach to machine learning
 Understand ConvNets (convolutional neural networks) and how they are essential for deep learning systems such as image classifiers
 Use GANs (generative adversarial networks) to create new data that fits with existing patterns
 Discover RNNs (recurrent neural networks) that can process sequences of input intelligently, using one part of a sequence to correctly interpret another
 Apply deep learning to natural human language and interpret natural language texts to produce an appropriate response
 Train your models on the cloud and put TF to work in real environments
 Explore how Google tools can automate simple ML workflows without the need for complex modeling