TensorFlow is an open-source deep-learning library that is developed and maintained by Google. It offers dataflow programming which performs a range of machine learning tasks. It was built to run on multiple CPUs or GPUs and even mobile operating systems, and it has several wrappers in languages like Python, C++, or Java.
Here is a curated list of Top 10 Books for Tensor Flow that should be part of any beginner to advanced Deep learning/machine learning Scienctists Learners library.
Learn TensorFlow is a book written by Pramod Singh and Avish Manure. The book begins by introducing TensorFlow 2.0 framework and the major changes from its last release. The book also focuses on building Supervised Machine Learning models using TensorFlow.
The book also teaches how you can build models using customer estimators. You will also learn how to use TensorFlow to build machine learning and deep learning models. All the code given in this book will be available in the form of executable scripts at Github.
Advanced Deep Learning with TensorFlow 2 and Keras is a book written by Rowel Atienza. The book teaches you some advanced deep learning techniques available today.
This book also teaches you about deep learning, unsupervised learning using mutual information, object detection (SSD). The book also shows how to create effective AI with the most up-to-date techniques. In this book, you will learn about GANs and how they can unlock new levels of AI performance.
Tensorflow in 1 Day is a book written by Krishna Rungta. The book teaches you this complex subject in easy to understand English language. It has a fantastic graph, computation feature. It helps data scientist to visualize his designed neural network using TensorBoard.
The book covers topic like What is Deep learning?, Machine Learning vs. Deep Learning, What is TensorFlow?, and advanced topics like Jupyter Notebook, Tensorflow on AWS, and more.
TinyML: Machine Learning with TensorFlow Lite is a book written by Pete Warden and Daniel Situnayke. With this practical learning reference book, you'll enter the field of TinyML. The book covers deep learning, and embedded systems combine to make astounding things possible with tiny devices.
This book is ideal for software and hardware developers who want to build embedded systems using machine learning.
5) Natural Language Processing with TensorFlow: Teach language to machines using Python's deep learning library
Natural Language Processing with TensorFlow is a book written by Hushan Ganegedara. In this book, you will also learn how to apply high-performance RNN models, short-term memory (LSTM) cells, to NLP tasks. You will also be able to explore neural machine translation and implement a neural machine translator.
After reading this book, you will understand about the NLP technology. You will also be able to apply TensorFlow in deep learning NLP applications, and how to perform specific NLP tasks.
6) TensorFlow Machine Learning Projects: Build 13 real-world projects with advanced numerical computations using the Python ecosystem
TensorFlow Machine Learning Projects is a book written by Ankit Jain, Armando Fandango, and Amita Kapoor. This book also teaches how to build advanced projects. You will also be able to tackle common challenges by using libraries from the TensorFlow ecosystem.
This book also teaches how you can build projects in various real-world domains, autoencoders, recommender systems, reinforcement learning, etc. By the end of this reference book, you'll have gained the required expertise to build machine learning projects.
7) 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 book written by Benjamin Planche and Eliot Andres. This book will help you explore Google's open-source framework for machine learning. You will also understand how to benefit from using convolutional neural networks (CNNs) for visual tasks.
The book starts with the fundamentals of computer vision and deep learning. The book also teaches you how to build a neural network from scratch. The book helps you to teaches how to classify images with modern solutions, such as Inception and ResNet, and extract specific content using the You Only Look Once (YOLO) method.
At the end of this study material book, you will have both the theoretical understanding and practical skills. It also helps you to solve advanced computer vision problems.
8) Pro Deep Learning with TensorFlow: A Mathematical Approach to Advanced Artificial Intelligence in Python
Pro Deep Learning with TensorFlow is a book written by Santanu Pattanayak. You'll also be able to understand mathematical understanding and intuition. It helps you to invent new deep learning architectures and solutions on your own.
The book offers hands-on expertise so you can learn deep learning from scratch. This TensorFlow book will allow you to get up to speed quickly using TensorFlow. It helps you to optimize different deep learning architectures.
The book covers many practical concepts of deep learning that are relevant in any industry are emphasized in this book. The code given in this reference material is available in the form of iPython notebooks and scripts.
9) Practical Deep Learning for Cloud, Mobile, and Edge: Real-World AI & Computer-Vision Projects Using Python, Keras & TensorFlow
Practical Deep Learning for Cloud, Mobile, and Edge is a book written by Anirudh Koul, Siddha Ganju, and Meher Kasam. This book teaches you how to build practical deep learning applications for the cloud, mobile, browsers.
The book teaches you the process of converting an idea into something that people in the real world can use. This book also teaches how you can develop Artificial Intelligence for a range of devices, including Raspberry Pi, and Google Coral. You will also get many practical tips for maximizing model accuracy and speed.
Deep Learning is a book written by Josh Patterson and Adam Gibson. This hands-on guide not only provides the most practical information available on the subject. It also helps you get started building efficient deep learning networks.
You will learn about the theory of deep learning before introducing their open-source Deeplearning4j (DL4J). It is a library for developing production-class workflows. By using real-world examples, you'll learn methods and strategies easily.