https://insidebigdata.com/2020/08/04/book-review-deep-learning-with-tensorflow-2-and-keras/

If you’re a data scientist who has been wanting to break into the deep learning realm, here is a great learning resource that can guide you through this journey. It’s pretty much an all-inclusive resource that includes all the popular methodologies upon which deep learning depends: CNNs, RNNs, RL, GANs, and much more. The glue that makes it all work is represented by the two most popular frameworks for deep learning pratcitioners, TensorFlow and Keras. This book was a real team effort by a group of consummate professionals: Antonio Gulli (Engineering Director for the Office of the CTO at Google Cloud), Amita Kapoor (Associate Professor in the Department of Electronics at the University of Delhi), and Sujit Pal (Technology Research Director at Elsevier Labs). The resulting text, Deep Learning with TensorFlow 2 and Keras, Second Edition, is an obvious example of what happens when you enlist talented people to write a quality learning resource. I’ve already recommended this book to my newbie data science students, as I enjoy providing them with good tips for ensuring their success in the field. This book is for Python-based data scientists who have a need to build AI solutions using machine learning and deep learning with the TensorFlow framework. Having a background in Python–based machine learning will help you progress through the chapters, but this book also provides the theory behind the use of TensorFlow 2, Keras, and AutoML to develop machine learning applications. To demonstrate the bread of coverage of the subject, here are the chapters included in the book: Chapter 1 – Neural Network Foundations with TensoFlow 2.0 Chapter 2 – TensorFlow 1.x and 2.x Chapter 3 – Regression Chapter 4 – Convolutional Neural Networks Chapter 5 – Advanced Consolutional Neural Networks Chapter 6- Generative Adversarial Networks Chapter 7 – Word Embeddings Chapter 8 – Recurrent Neural Networks Chapter 9 – Autoencoders Chapter 10 – Unsupervised Learning Chapter 11 – Reinforcement Learning Chapter 12 – TensorFlow and Cloud Chapter 13 – TensorFlow for Mobile and IoT and TensorFlow.js Chapter 14 – An Introduction to AutoML Chapter 15 – The Math Behind Deep Learning Chapter 16 – Tensor Processing Unit The book introduces the TensorFlow and Keras frameworks and then uses them throughout. It teaches key machine learning and deep learning methodologies and provides a firm understand of the supporting fundamentals through clear explanations and extensive code examples. Deep Learning with TensorFlow 2 and Keras provides a clear perspective for neural networks and deep learning techniques alongside the TensorFlow and Keras frameworks. You’ll learn how to write deep learning applications in the most widely used and scalable data science stack available. TensorFlow is the machine learning library of choice for data scientists, while Keras offers a simple yet powerful Python API for accessing TensorFlow. TensorFlow 2 (officially available in September 2019) provides a full Keras integration, making advanced deep learning simpler and more convenient than ever. This book also introduces neural networks with TensorFlow, runs through the main applications areas of regression, CNNs, GANs, RNNs, and NLP, and then does a deep dive into TensorFlow in production, TensorFlow mobile, TensorFlow cloud, and using TensorFlow with automated machine learning (AutoML). Here is a comprehensive list of what you’ll learn: Build machine learning and deep learning systems with TensorFlow 2 and the Keras API Use Regression analysis, the workhorse of data science Understand convolutional neural networks (CNNs) and how they are essential for deep learning applications such as image classifiers Use generative adversarial networks (GANs) to create new data that fits with existing patterns Discover how recurrent neural networks (RNNs) can process sequences of input intelligently, using one part of a sequence to correctly interpret another Apply the methodologies of deep learning to natural language processing (NLP) See how to train your models on the cloud and put TensorFlow to work in real-life environments Explore how Google AutoML tools can automate simple machine learning workflows without the need for complex modeling One of my favorite chapters is Chapter 15 on the math behind deep learning. It is imperative to have a firm understanding of the mathematical foundations for AI in order to gain a real benefit from the technology, especially when discussions of explainability and interpretability come up. The book comes with a series of Jupyter notebooks containing the Python code discussed in the chapters. The code provides the reader with a significant head-start with building a qualify toolbox of code for future deep learning projects. I would recommend this book without hesitation.