Generative Deep Learning: Teaching Machines To Paint, Write, Compose, and Play

Foster, David

ISBN 10: 1098134184 / ISBN 13: 9781098134181

Verlag: O'Reilly Media, 2023

Beschreibung

Bestandsnummer des Verkäufers 26395237120

Bibliografische Details

• Titel: Generative Deep Learning: Teaching Machines To Paint, Write, Compose, and Play
• Verlag: O'Reilly Media
• Erscheinungsjahr: 2023
• Sprache: Englisch
• ISBN-10: 1098134184
• ISBN-13: 9781098134181
• Einband: Softcover
• Auflage: 2. Auflage
• Zustand: New

Über diesen Titel

Inhaltsangabe

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.

Über die Autorin bzw. den Autor

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, including the Innocentive Predicting Product Purchase challenge and for delivering a process to enable a pharmaceutical company in the US to optimize site selection for clinical trials. He is a member of the Machine Learning Institute Faculty and has given talks internationally on topics related to the application of cutting-edge data science and AI within industry and academia.