Description
Deep Learning: Foundations and Concepts
Deep Learning: Foundations and Concepts is Christopher M. Bishop, who also wrote Pattern Recognition and Machine Learning, and Hugh Bishop’s textbook for students, researchers, and practitioners who want to understand why deep learning methods work, not just how to run them. Rather than code-heavy tutorials, the book covers the mathematics and concepts that stay relevant as tools change.
It starts with the basics of neural networks and optimization, then works through the architectures behind current AI systems, illustrated throughout with diagrams and visualizations that connect linear algebra and probability theory to practical applications.
Transformers, LLMs, and graph networks
The book gives detailed coverage of Transformers, attention mechanisms, and Graph Neural Networks (GNNs), the architectures behind large language models and graph based AI systems.
Generative models
Chapters cover deep generative models including variational autoencoders (VAEs), generative adversarial networks (GANs), and diffusion models.
A Bayesian approach
The book integrates probabilistic thinking and uncertainty quantification throughout, an approach the authors carry over from Bishop’s earlier work in machine learning.
Diagrams that connect theory to practice
Figures and visualizations run through the book, aimed at bridging the gap between the linear algebra and probability theory in the early chapters and the practical AI applications covered later.
Book details
- Title: Deep Learning: Foundations and Concepts
- Authors: Christopher M. Bishop, Hugh Bishop
- ISBN-13: 9783031454677
Questions buyers ask
Who is this book for?
Students, researchers, and practitioners who want to understand the mathematics and concepts behind deep learning, not just how to use the tools.
Does it focus on code or on concepts?
Concepts and mathematics. The authors chose this focus because code-heavy tutorials go out of date as tools change.
Does it cover large language models?
Yes. It gives detailed coverage of Transformers, attention mechanisms, and the architectures behind LLMs.
Does it cover generative AI?
Yes. Chapters cover VAEs, GANs, and diffusion models.
Is Christopher M. Bishop the same author as Pattern Recognition and Machine Learning?
Yes. He wrote that earlier textbook and co-authors this one with Hugh Bishop.
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