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Generative Deep Learning

Book Description

With Early Release ebooks, you get books in their earliest form—the author's raw and unedited content as he or she writes—so you can take advantage of these technologies long before the official release of these titles. You'll also receive updates when significant changes are made, new chapters are available, and the final ebook bundle is released.

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 a world model to understand 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