Generative Deep Learning

• Verfasst von: Foster, David [VerfasserIn]
• Titel: Generative Deep Learning
• Titelzusatz: teaching machines to paint, write, compose, and play
• Mitwirkende: Friston, Karl [MitwirkendeR]
• Verf.angabe: David Foster ; foreword by Karl Friston
• Ausgabe: Second edition.
• Verlagsort: Sebastopol, CA
• Verlag: O'Reilly Media, Incorporated
• Jahr: 2023
• Umfang: 1 online resource (xxvi, 426 pages)
• Illustrationen: illustrations.
• Fussnoten: Masked Convolutional Layers. - Includes bibliographical references and index. - Description based on online resource; title from digital title page (viewed on May 22, 2023)
• ISBN: 978-1-0981-3414-3
• ISBN: 1-0981-3414-1
• ISBN: 978-1-0981-3415-0
• ISBN: 1-0981-3415-X
• Datenträger: Online-Ressource
• Sprache: eng
• Bibliogr. Hinweis: Erscheint auch als : Druck-Ausgabe
• K10plus-PPN: 1846839483
• K10plus-PPN:
  Universitätsbibliothek
• Lokale URL UB: Zum Volltext
• Lokale URL UB: Bibliothek der Medizinischen Fakultät Mannheim der Universität Heidelberg
• Bibliothek/Idn: UW / m4326718129
• Lokale URL Inst.: Zum Volltext

Abstract

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.