David Foster

Generative Deep Learning (eBook, PDF)

• Format: PDF

Produktbeschreibung

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 photosTrain GANs to generate images based on your own datasetBuild diffusion models to produce new varieties of flowersTrain your own GPT for text generationLearn how large language models like ChatGPT are trainedExplore state-of-the-art architectures such as StyleGAN2 and ViT-VQGANCompose polyphonic music using Transformers and MuseGANUnderstand how generative world models can solve reinforcement learning tasksDive into multimodal models such as DALL.E 2, Imagen, and Stable DiffusionThis 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.

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Produktdetails

• Verlag: O'Reilly Media
• Seitenzahl: 456
• Erscheinungstermin: 28. Juni 2022
• Englisch
• ISBN-13: 9781098134150
• Artikelnr.: 72360154

Autorenporträt

David Foster is a data scientist, entrepreneur, and educator specializing in AI applications within creative domains. As cofounder of Applied Data Science Partners (ADSP), he inspires and empowers organizations to harness the transformative power of data and AI. He holds an MA in Mathematics from Trinity College, Cambridge, an MSc in Operational Research from the University of Warwick, and is a faculty member of the Machine Learning Institute, with a focus on the practical applications of AI and real-world problem solving. His research interests include enhancing the transparency and interpretability of AI algorithms, and he has published literature on explainable machine learning within healthcare.