Understand how neural networks work and learn how to implement them using TensorFlow 2.0 and Keras. This new edition focuses on the fundamental concepts and at the same time on practical aspects of implementing neural networks and deep learning for your research projects.

This book is designed so that you can focus on the parts you are interested in. You will explore topics as regularization, optimizers, optimization, metric analysis, and hyper-parameter tuning. In addition, you will learn the fundamentals ideas behind autoencoders and generative adversarial networks.

You will:

• Understand the fundamental concepts of how neural networks work

• Learn the fundamental ideas behind autoencoders and generative adversarial networks

• Be able to try all the examples with complete code examples that you can expand for your own projects

• Have available a complete online companion book with examples and tutorials.

This book is for:

Readers with an intermediate understanding of machine learning, linear algebra, calculus, and basic Python programming.