Ancillary Materials for The Shallow and the Deep by M. Biehl

by Hei-Chi Chan

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The following ancillary materials supplement the open textbook, The Shallow and the Deep (2023), by M. Biehl published by the University of Groningen Press. This companion suite features chapter summaries and deep-dive analyses that explore selected themes of each chapter. It highlights prominent and recent trends shaping these subjects. Designed for both educators and students, these resources offer valuable context and specialized insights to enrich the core reading experience.

Description from LibreTexts.

Publisher
University of Illinois Springfield
Subjects
Engineering
Licence
CC BY-NC-SA 4.0, as LibreTexts states it. Non-commercial use only.
Who can read
Tweens profiles and older, judged from its title and subjects

Contents

LibreTexts writes equations in TeX, and here they show as TeX; the PDF has them typeset.

  1. Front Matter
  2. TitlePage
  3. InfoPage
  4. Table of Contents
  5. Acknowledgement
  6. Licensing
  7. 1: From Neurons to Networks
  8. 1.1: Chapter 1 Summary
  9. 1.2: A Brief History of Neural Networks and Deep Learning
  10. 1.3: Hebbian Learning
  11. 1.4: The Hopfield Model
  12. 1.5: Modern Hopfield Networks
  13. 1.6: A Brief Discussion
  14. 2: Learning from Data
  15. 2.1: Chapter 2 Summary
  16. 2.2: Types of Learning
  17. 2.3: A List of Supervised Learning Models
  18. 2.4: Annoy from Spotify
  19. 2.5: Distance Concentration
  20. 2.6: Shannon's Source Coding Theorem
  21. 3: The Perceptron & Beyond Linear Separability
  22. 3.1: Chapters 3 and 4 Summary
  23. 3.2: A Brief Introduction on Rademacher Complexity
  24. 4: Beyond Linear Separability
  25. 5: Feed-forward Networks for Regression and Classification
  26. 5.1: Chapter 5 Summary
  27. 5.2: Universal Approximation Theorem
  28. 5.3: Attention Mechanism/Transformers
  29. 6: Distance-based Classifiers
  30. 6.1: Chapter 6 Summary and a Discussion on the Neural Gas Model
  31. 7: Model Evaluation and Regularization
  32. 7.1: Chapter 7 Summary
  33. 7.2: The Bias-Variance Trade Off
  34. 7.3: Explainable AI (XAI)
  35. 8: Preprocessing and Unsupervised Learning
  36. 8.1: Chapter 8 Summary
  37. 8.2: Dimensionality Reduction Methods
  38. 8.3: Clustering Methods
  39. 8.4: Vector DBs & Similarity Search at Scale (The Big Picture)
  40. Back Matter
  41. Index
  42. Glossary
  43. Detailed Licensing