Generative modeling is one of the hottest topics in artificial intelligence. Recent advances in the field have shown how it’s possible to teach a machine to excel at human endeavors—such as drawing, composing music, and completing tasks—by generating an understanding of how its actions affect its environment.

With this practical book, machine learning engineers and data scientists will learn how to recreate some of the most famous examples of generative deep learning models, such as variational autoencoders and generative adversarial networks (GANs). You’ll also learn how to apply the techniques to your own datasets.

David Foster, cofounder of Applied Data Science, demonstrates the inner workings of each technique, starting with the basics of deep learning before advancing to the most cutting-edge algorithms in the field. Through tips and tricks, you’ll learn how to make your models learn more efficiently and become more creative.

Get a fundamental overview of deep learning

Learn about libraries such as Keras and TensorFlow

Discover how variational autoencoders work

Get practical examples of generative adversarial networks (GANs)

Understand how autoregressive generative models function

Apply generative models within a reinforcement learning setting to accomplish tasks

David Foster is the co-founder of Applied Data Science, a data science consultancy delivering bespoke solutions for clients. He holds an MA in Mathematics from Trinity College, Cambridge, UK and an MSc in Operational Research from the University of Warwick.

David has won several international machine learning competitions, including the Innocentive Predicting Product Purchase challenge and was awarded first prize for a visualisation that enables a pharmaceutical company in the US to optimize site selection for clinical trials.

He is an active participant in the online data science community and has authored several successful blog posts on deep reinforcement learning including ‘How To Build Your Own AlphaZero AI’.