What this book covers

Chapter 1, Neural Networks Foundations, teaches the basics of neural networks.

Chapter 2, Keras Installation and API, shows how to install Keras on AWS, Microsoft Azure, Google Cloud, and your own machine. In addition to that, we provide an overview of the Keras APIs.

Chapter 3, Deep Learning with ConvNets, introduces the concept of convolutional networks. It is a fundamental innovation in deep learning that has been used with success in multiple domains, from text to video to speech, going well beyond the initial image processing domain where it was originally conceived.

Chapter 4, Generative Adversarial Networks and WaveNet, introduces generative adversarial networks used to reproduce synthetic data that looks like data generated by humans. And we will present WaveNet, a deep neural network used for reproducing human voice and musical instruments with high quality.

Chapter 5, Word Embeddings, discusses word embeddings, a set of deep learning methodologies for detecting relationships between words and grouping together similar words.

Chapter 6, Recurrent Neural Networks – RNN, covers recurrent neural networks, a class of network optimized for handling sequence data such as text.

Chapter 7, Additional Deep Learning Models, gives a brief look into the Keras functional API, regression networks, autoencoders, and so on.

Chapter 8, AI Game Playing, teaches you deep reinforcement learning and how it can be used to build deep learning networks with Keras that learn how to play arcade games based on reward feedback.

Appendix, Conclusion, is a crisp refresher of the topics covered in this book and walks the users through what is new in Keras 2.0.