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Description
Published by The MIT Press, Understanding Deep Learning by Simon J. D. Prince is a comprehensive textbook bridging mathematical principles and practical deep learning implementation. Organized across 21 chapters and three mathematical appendices, the text builds progressively from supervised learning fundamentals and loss functions to deep neural networks. Prince details essential training mechanics, including backpropagation, parameter initialization, gradient descent variants, model fitting, performance evaluation, and regularization methods, accompanied by extensive visual illustrations.
Beyond foundational architectures, the volume addresses modern models including convolutional networks, residual networks, transformers, and graph neural networks. It offers rigorous coverage of unsupervised and generative approaches—such as GANs, normalizing flows, variational autoencoders, and diffusion models—alongside reinforcement learning. The book concludes with theoretical perspectives on model generalization and a dedicated chapter on AI ethics, complemented by 68 hands-on Python programming notebooks.
Institutional Use
Understanding Deep Learning serves as an essential resource across multiple institutional settings:
- University Computer Science & Data Science Programs: Adopted as a primary textbook for upper-division undergraduate and graduate courses in neural networks and applied AI, integrating the accompanying Python coding notebooks into lab curricula.
- Engineering & Applied Mathematics Departments: Utilized as a rigorous reference for graduate coursework covering optimization, transformers, and generative modeling.
- Research Institutions & Corporate R&D Labs: Maintained in technical library collections for machine learning scientists and engineers designing modern AI architectures.
Product Details
| ISBN-13 | 9780262048644 |
|---|---|
| ISBN-10 | 0262048647 |
| Authors | Simon J. D. Prince |
| Publisher | The MIT Press |
| Publication date | 2023-12-05 |
| Format | Hardcover |
| Language | English |
| Category | Computer Science & Data |
| List price | $63.99 / copy |
| Min. order | 5 copies |
| Condition | New |
Table of Contents
- Chapter 1: Introduction
- Chapter 2: Supervised learning
- Chapter 3: Shallow neural networks
- Chapter 4: Deep neural networks
- Chapter 5: Loss functions
- Chapter 6: Fitting models
- Chapter 7: Gradients and initialization
- Chapter 8: Measuring performance
- Chapter 9: Regularization
- Chapter 10: Convolutional networks
- Chapter 11: Residual networks
- Chapter 12: Transformers
- Chapter 13: Graph neural networks
- Chapter 14: Unsupervised learning
- Chapter 15: Generative Adversarial Networks
- Chapter 16: Normalizing flows
- Chapter 17: Variational autoencoders
- Chapter 18: Diffusion models
- Chapter 19: Reinforcement learning
- Chapter 20: Why does deep learning work?
- Chapter 21: Deep learning and ethics
- Appendix A: Notation
- Appendix B: Mathematics
- Appendix C: Probability
Key Topics
- Supervised learning principles and loss function formulation
- Shallow and deep neural network architectures
- Model fitting, gradients, backpropagation, and initialization
- Performance measurement, double descent, and regularization techniques
- Convolutional neural networks (CNNs) and residual networks (ResNets)
- Transformers and dot-product self-attention mechanisms
- Graph neural networks (GNNs)
- Unsupervised learning and deep generative models (GANs, normalizing flows, VAEs, diffusion models)
- Deep reinforcement learning foundations
- Generalization theory and theoretical underpinnings of deep learning
- Artificial intelligence ethics, alignment, and societal impact
Who This Is For
- Undergraduate and graduate students in computer science, data science, and applied mathematics
- University faculty and academic instructors teaching artificial intelligence and machine learning courses
- Machine learning engineers and AI research scientists seeking theoretical grounding
- Technical professionals and quantitative analysts implementing modern deep learning architectures
Institutional & Bulk Ordering
- Minimum order: 5 copies, with tiered pricing at higher quantities
- Purchase orders and Net-30 terms accepted for qualifying institutions
- Tax-exempt certificates accepted for qualifying accounts
- Free worldwide shipping, no sales tax
Edition & Identifier Check
Always confirm this exact ISBN-13 and edition (9780262048644) against your syllabus or procurement request before ordering — editions of this title are not interchangeable.
Frequently Asked Questions
Q: What practical exercises accompany this textbook?
A: The textbook is supported by an open-access repository containing 68 Python Jupyter notebooks that provide structured coding exercises directly aligned with the book's theoretical concepts and model implementations.
Q: What prerequisite background is expected for students using this text?
A: The book requires an introductory background in applied mathematics, specifically linear algebra, multivariate calculus, and basic probability. To ensure accessibility, three dedicated appendices review notation, mathematical foundations, and probability concepts.
Q: Which modern deep learning architectures are covered?
A: In addition to multilayer perceptrons, convolutional neural networks, and residual networks, the text covers contemporary architectures including transformers with self-attention, graph neural networks, generative adversarial networks, normalizing flows, variational autoencoders, and diffusion models.
Q: What is the physical format and publication date of this MIT Press volume?
A: This title is published by The MIT Press as a hardcover edition, released on December 5, 2023 (ISBN 9780262048644).
Q: What is the minimum order quantity for this title?
A: The minimum order is 5 copies. Tiered pricing applies at 5, 10, and 25 copies; orders of 100 or more copies are quoted directly by email.
Q: Is this the correct edition for my program or syllabus?
A: This listing is ISBN 9780262048644 (ISBN-10 0262048647). Please confirm this exact identifier and edition against your syllabus or procurement request before ordering, as editions of this title are not interchangeable.
Q: Do you accept purchase orders and offer Net-30 terms?
A: Yes. Purchase orders and Net-30 terms are accepted for qualifying institutions, and tax-exempt certificates are accepted at checkout.
Q: Do you ship internationally?
A: Yes, Global Academic Supply offers free worldwide shipping with no sales tax on qualifying orders.