Generative Deep Learning:2nd Edition
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Generative Deep Learning:2nd Edition
Generative Deep Learning: Teaching Machines To Paint, Write, Compose, and Play
$39.90
$66.90
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Detailed Overview:
Generative Deep Learning: Teaching Machines To Paint, Write, Compose, and Play by David Foster is a comprehensive and hands-on guide to the exciting world of generative artificial intelligence and deep learning. Designed for developers, data scientists, AI enthusiasts, and machine learning practitioners, this book explains how modern generative models create images, music, text, and other forms of creative content using advanced neural network architectures.
The book introduces readers to core deep learning concepts before diving into powerful generative models such as Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), Transformers, diffusion models, and autoregressive architectures. David Foster combines theory with practical coding examples using Python and TensorFlow, helping readers understand both the mathematical foundations and real-world implementation of generative AI systems.
One of the major strengths of the book is its project-based learning approach. Readers build practical applications such as image generators, music composition systems, style transfer models, text generators, and deepfake-related architectures while gaining insight into modern AI creativity tools. The second edition also includes updated coverage of transformers and advanced generative techniques that are widely used in today’s AI landscape.
Ideal for intermediate learners and AI professionals, this book serves as both a technical learning resource and a practical guide to building innovative generative deep learning applications.
Product Details:
Title:Generative Deep Learning: Teaching Machines To Paint, Write, Compose, and Play
Author:David Foster
ISBN-13:9781098134181
ISBN-10:1098134184
Publisher:O'Reilly Media
Binding:Paperback
No of Pages:660 Pages
Language:English
Publisher Date:6 june 2023
Generative Deep Learning explores how artificial intelligence systems can generate creative outputs such as images, music, text, and game content using advanced deep learning techniques. The book provides a practical introduction to generative AI through hands-on coding projects and modern neural network architectures.
Key topics include autoencoders, GANs, transformers, diffusion models, recurrent neural networks, and reinforcement learning. David Foster explains how these models learn patterns from data and generate realistic outputs across multiple creative domains. The book also covers practical implementation using Python, TensorFlow, and deep learning frameworks commonly used in AI development.
Through real-world projects and clear explanations, readers gain valuable experience building generative AI applications while understanding the theory behind modern deep learning systems. It is highly recommended for machine learning engineers, AI researchers, developers, and data science professionals interested in generative artificial intelligence and creative machine learning applications.
David Foster is a data scientist, machine learning expert, and educator specializing in deep learning and artificial intelligence. He has extensive experience developing AI applications and teaching advanced machine learning concepts through practical projects and industry-focused training. David Foster is widely recognized for simplifying complex generative AI techniques for developers and technical learners. His expertise in neural networks, generative models, and applied deep learning has made his work popular among AI practitioners, researchers, and software engineers exploring modern artificial intelligence technologies.
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