Preface

If you're aware of the buzz surrounding terms such as machine learning, artificial intelligence, or deep learning, you might know what neural networks are. Ever wondered how they help solve complex computational problems efficiently, or how to train efficient neural networks? This book will teach you both of these things, and more.

You will start by getting a quick overview of the popular TensorFlow library and see how it is used to train different neural networks. You will get a thorough understanding of the fundamentals and basic math for neural networks and why TensorFlow is a popular choice. Then, you will proceed to implement a simple feedforward neural network. Next, you will master optimization techniques and algorithms for neural networks using TensorFlow. Further, you will learn how to implement some more complex types of neural networks such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and Deep Belief Networks 0;(DBNs). In the course of the book, you will be working on real-world datasets to get a hands-on understanding of neural network programming. You will also get to train generative models and will learn the applications of autoencoders.

By the end of this book, you will have a fair understanding of how to leverage the power of TensorFlow to train neural networks of varying complexities, without any hassle.