TensorFlow is an end-to-end open-source platform for Machine Learning (ML). It comes with a complex, comprehensive library for distributed numerical computation.
TensorFlow was created by Google company and supports many of its large-scale applications. As told earlier it is open source and has been open-source since 2015 with its latest version 2.0 coming out in October 2019.
You can build and train ML models easily using intuitive high-level APIs such as Keras with eager execution, which makes for immediate model iteration and easy debugging.
You can easily train and deploy models in the cloud, on-premises, in the browser, or on-device no matter what language you are using.
TensorFlow provides a simple and flexible architecture to take your new ideas from concept stage to code, to state-of-the-art models, and to publication faster and quicker.
Some of the big companies that are using TensorFlow are Coca-Cola, Google, DeepMind, and Airbnb.
Here is the best collection of TensorFlow books to master TensorFlow.
Best TensorFlow Books for Beginners and Advanced
1. Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow
In recent times, deep learning has really opened up the whole field of machine learning.
What it has done – it has allowed even those programmers who really knew little about this technology can use simple and efficient tools to implement programs that are capable of learning from the data.
This book Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow contains plenty of examples to help you gain an understanding of the basic concepts and tools for building intelligent systems.
In this book, you’ll learn a range of techniques such as simple linear regression and then you will progress to learn about deep neural networks.
This book is written by Aurélien Géron who is a machine learning consultant and trainer.
What You Will Learn
- You will learn about the machine learning landscape in particular neural nets.
- You will learn how to use Scikit-Learn to track an example machine-learning project from end-to-end.
- Know about the several training models such as support vector machines, decision trees, random forests, and ensemble methods.
- Learn how to use the TensorFlow library for building and training neural nets.
- Deep dive into the neural net architectures like convolutional nets, recurrent nets, and deep reinforcement learning.
- Learn all the techniques for training and scaling the deep neural nets.
A very good book on machine learning and deep learning and is highly recommended.
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2. TinyML: Machine Learning with TensorFlow Lite on Arduino and Ultra-Low-Power Microcontrollers
TinyML: Machine Learning with TensorFlow Lite is a practical book that is written by Pete Warden & Daniel Situnayke.
This book by Peter Warden on Machine Learning will let you deep dive into the field of TinyML.
This book is most ideal for software and hardware developers who are looking to build embedded systems by using Machine Learning (ML).
This book assumes no machine learning or microcontroller experience.
What You Will Learn
- You will learn how to build a speech recognizer, a camera that detects people easily, and also a magic wand that will respond to gestures.
- You will work with Arduino and ultra-low-power microcontrollers.
- Learn all the essentials of Machine Learning and how to train your own models.
- How to train your models to understand audio, image, and accelerometer data.
- Know about TensorFlow Lite for microcontrollers and Google’s toolkit for TinyML.
- Learn how to debug applications and also provide safeguards for privacy and security.
- You will learn how to optimize latency, energy usage, and model and binary size.
Good entry-level training for machine learning and AI.
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3. TensorFlow Machine Learning Projects: Build 13 real-world projects with advanced numerical computations using the Python ecosystem
TensorFlow Machine Learning Projects will help you to implement TensorFlow’s offerings such as TensorBoard, TensorFlow.js, TensorFlow Probability, and TensorFlow Lite to build smart automation projects.
This book is written by Ankit Jain, Armando Fandango, and Amita Kapoor, and by using this book you will be able to learn how to build advanced projects.
You will also learn how to tackle common challenges by using a range of libraries from the TensorFlow ecosystem.
This book on TensorFlow is suitable for you if you are a data analyst, data scientist, machine learning professional, and a deep learning enthusiast who has some basic knowledge of TensorFlow.
After reading this book, you’ll have acquired the expertise for building a full-fledged machine learning project at work.
What You Will Learn
- You will learn about the TensorFlow ecosystem using various datasets and techniques.
- Ability to create recommendation systems for quality product recommendations.
- Easily build projects using NLP, CNNs, and Bayesian neural networks.
- How to play Pac-Man using deep reinforcement learning.
- How to deploy scalable TensorFlow based machine learning systems.
- Learn how to generate your own book script using RNNs.
It is a good book for the ones who want to learn TensorFlow even without any background.
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4. Hands-On Computer Vision with TensorFlow 2: Leverage deep learning to create powerful image processing apps with TensorFlow 2.0 and Keras
Hands-On Computer Vision with TensorFlow 2 is a practical book for building high-performance systems for object detection, segmentation, video processing, smartphone applications, and more.
It is written by Benjamin Planche and Eliot Andres and they will teach you how to build, train, and serve your own deep neural networks with TensorFlow 2 and Keras.
You will learn how to get benefits from using convolutional neural networks (CNNs) for visual tasks.
This book is ideal if you are new to deep learning and some experience in Python programming.
After completion of this book, you’ll have both the theoretical understanding and practical skills for solving advanced computer vision problems with TensorFlow 2.0.
What You Will Learn
- Learn how to create your own neural networks from scratch.
- You can easily classify images with modern architectures including Inception and ResNet.
- How to detect and segment objects in images with YOLO, Mask R-CNN, and U-Net.
- How to tackle problems in developing self-driving cars and facial emotion recognition systems.
- Learn how to enhance your application’s performance with transfer learning, GANs, and domain adaptation.
- How to optimize and deploy your networks on mobile devices and in the browser.
It is great reference material for you.
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5. Natural Language Processing with TensorFlow: Teach language to machines using Python’s deep learning library
Natural Language Processing with TensorFlow is a book that will teach you how to write modern natural language processing applications using deep learning algorithms and TensorFlow.
This book is written by Hushan Ganegedar and you will learn how to apply high-performance RNN models, short-term memory (LSTM) cells to NLP tasks.
It covers NLP as a field in its own right in order to improve understanding for choosing TensorFlow tools and other deep learning approaches.
This book is ideal for Python developers who have a strong inclination towards deep learning and who want to simplify NLP tasks using TensorFlow.
After the completion of this book, you will learn about NLP and you’ll learn how to perform specific NLP tasks.
What You Will Learn
- Learn about the core concepts of NLP and various approaches to natural language processing.
- Learn how to solve various NLP tasks by applying TensorFlow functions to create neural networks.
- You will learn about the techniques for performing sentence classification and language generation using CNNs and RNNs.
- Skills to write automatic translation programs and implement an actual neural machine translator from scratch.
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6. Intelligent Mobile Projects with TensorFlow: Build 10+ Artificial Intelligence apps using TensorFlow Mobile and Lite for iOS, Android, and Raspberry Pi
Intelligent Mobile Projects with TensorFlow will teach you how to create Deep Learning and Reinforcement Learning apps for multiple platforms with TensorFlow.
This book is ideal for an iOS/Android developer who is interested in building and retraining others’ TensorFlow models and running them in your mobile apps.
You will learn how to build TensorFlow-powered AI applications for mobile and embedded devices using this book.
This book covers more than 10 complete iOS, Android, and Raspberry Pi apps powered by TensorFlow and are built from scratch.
You’ll learn how to use or retrain existing TensorFlow models, build your own models, and develop intelligent mobile apps running on those TensorFlow models.
What You Will Learn
- How to classify images with transfer learning.
- How to detect objects and their locations.
- Easily transform pictures with amazing art styles.
- Understand simple speech commands and describe images in a natural language.
- Predict stock prices with Recurrent Neural Network in TensorFlow and Keras.
- Build AlphaZero-like mobile game apps in TensorFlow and Keras.
- Learn to use TensorFlow Lite and Core ML on mobile.
- How to develop TensorFlow apps on Raspberry Pi that can move, see, listen, speak, and learn.
A very practical book and is really worth it!
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7. Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow 2
Python Machine Learning is a comprehensive book to learn machine learning and deep learning with Python.
This book is targeted towards people who know some Python and want to use machine learning and deep learning to enhance their code. So if you are a developer and data scientist who is looking to enrich your knowledge of deep learning and ML, then this book is for you.
The book is filled with plenty of clear explanations, visualizations, and practical examples to teach you about the essential machine learning techniques in depth.
What You Will Learn
- You will be able to master the frameworks, models, and techniques that enable machines to learn from data.
- You will use Scikit-Learn for machine learning and TensorFlow for deep learning.
- How to apply machine learning to image classification, sentiment analysis, and intelligent web applications.
- Learn how to build and train neural networks, GANs, and various other models.
- How to predict continuous target outcomes using the regression analysis.
- Go deeper into textual and social media data using sentiment analysis.
All in all, a great book with a perfect mix of mathematical concepts and lots of practical examples.
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Conclusion
So these are the 7 Best TensorFlow Books for Beginners and Advanced users to learn TensorFlow from scratch.
We hope these books on TensorFlow will help you in getting mastery over ML and Deep learning.
You can learn more about TensorFlow from their official website.
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