Title: Generative Deep Learning: Teaching Machines To Paint, Write, Compose, and Play (eng)
Publisher: O'Reilly Media
Publication Date: 2023
Language: English
ISBN 10: 1098134184
ISBN 13: 9781098134181
Binding: Soft cover
Edition: 2nd Edition
Condition: new
Weight: 0.86 kilograms

About this title

Synopsis

Generative AI is the hottest topic in tech. This practical book teaches machine learning engineers and data scientists how to use TensorFlow and Keras to create impressive generative deep learning models from scratch, including variational autoencoders (VAEs), generative adversarial networks (GANs), Transformers, normalizing flows, energy-based models, and denoising diffusion models.

The book starts with the basics of deep learning and progresses to cutting-edge architectures. Through tips and tricks, you'll understand how to make your models learn more efficiently and become more creative.

Discover how VAEs can change facial expressions in photos
Train GANs to generate images based on your own dataset
Build diffusion models to produce new varieties of flowers
Train your own GPT for text generation
Learn how large language models like ChatGPT are trained
Explore state-of-the-art architectures such as StyleGAN2 and ViT-VQGAN
Compose polyphonic music using Transformers and MuseGAN
Understand how generative world models can solve reinforcement learning tasks
Dive into multimodal models such as DALL.E 2, Imagen, and Stable Diffusion

This book also explores the future of generative AI and how individuals and companies can proactively begin to leverage this remarkable new technology to create competitive advantage.

About the Author

David Foster is a Founding Partner of ADSP, a consultancy delivering bespoke data science and AI solutions. He holds an MA in Mathematics from Trinity College, Cambridge and an MSc in Operational Research from the University of Warwick.
Through ADSP, David leads the delivery of high-profile data science and AI projects across the public and private sectors. He has won several international machine-learning competitions and is a faculty member of the Machine Learning Institute. He has given talks internationally on topics related to the application of cutting-edge data science and AI within industry and academia.

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