Generative modelling is one of the hottest topics in AI. It’s now possible to teach a machine to excel at human endeavours such as painting, writing, and composing music. With this practical book, machine-learning engineers and data scientists will discover how to re-create some of the most impressive examples of generative deep learning models, such as variational autoencoders, generative adversarial networks (GANs), encoder-decoder models, and world models.
Author David Foster demonstrates the inner workings of each technique, starting with the basics of deep learning before advancing to some of the most cutting-edge algorithms in the field. Through tips and tricks, you’ll understand how to make your models learn more efficiently and become more creative.
David Foster holds an MA in mathematics from Trinity College, Cambridge, UK, and an MSc in Operational Research from the University of Warwick.
He has won several international machine learning competitions and is the author of the bestselling book "Generative Deep Learning: Teaching Machines to Paint, Write, Compose and Play".
✅ Introduction to Generative Modelling and Deep Learning
✅ Variational Autoencoders
✅ Generative Adversarial Networks
✅ CycleGAN and Neural Style Transfer
✅ RNNs, Encoder-Decoder models and Attention
✅ World Models
✅ Analysis of BERT, GPT-2, ProGAN, StyleGAN and more!
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Learn how to build your very own creative generative models with this practical guide - available in electronic or print format.