Technology leaders are adopting neural networks to enhance their products, making them smarter or, in marketing words, AI-powered. This book is a handy guide to TensorFlow, its inner structure, the new features of version 2.0 and how to use them to create neural-networks-based applications. By the end of this book, you will be well-versed in the TensorFlow architecture and its new features. You will be able to solve machine learning problems easily, using the power of neural networks.
This book starts with a theoretical overview of machine learning and neural networks, followed by a description of the TensorFlow library, in both its 1.x and 2.0 versions. Reading this book, you will become well-versed in the required theory for understanding how neural networks work, using easy-to-follow examples. Next, you will learn how to master optimization techniques and algorithms to build a wide range of neural network architectures using the new modules offered by TensorFlow 2.0. Furthermore, after having analyzed the TensorFlow structure, you will learn how to implement more complex neural network architectures such as CNNs for classification, semantic segmentation networks, generative adversarial networks, and others in your research work and projects.
By the end of this book, you will master the TensorFlow structure and will be able to leverage the power of this machine learning framework to train and use neural networks of varying complexities without much effort.
