Nguồn ngoài
Ancillary Materials for The Shallow and the Deep by M. Biehl
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.
- Tác giả
- Hei-Chi Chan
- Nhà xuất bản
- University of Illinois Springfield
- Nguồn
- LibreTexts
- Giấy phép
- CC BY-NC-SA
- Ngôn ngữ
- EN
Mục lục· 33 mục
- 01: From Neurons to Networks
- 1.1: Chapter 1 Summary
- 1.2: A Brief History of Neural Networks and Deep Learning
- 1.3: Hebbian Learning
- 1.4: The Hopfield Model
- 1.5: New Page 1
- 1.6: A Brief Discussion
- 02: New Chapter vv
- 2.1: Chapter 2 Summary
- 2.2: New Page 1
- 2.3: New Page 2
- 2.4: Annoy from Spotify
- 2.5: Distance Concentration
- 2.6: New Page 5
- 03: New Chapter 1
- 3.1: Chapters 3 and 4 Summary
- 3.2: A Brief Introduction on Rademacher Complexity
- 04: New Chapter
- 05: Feed-forward Networks for Regression and Classification
- 5.1: Chapter 5 Summary
- 5.2: Universal Approximation Theorem
- 5.3: Attention MechanismTransformers
- 06: Distance-based Classifiers
- 6.1: Chapter 6 Summary and a Discussion on the Neural Gas Model
- 07: Model Evaluation and Regularization
- 7.1: Chapter 7 Summary
- 7.2: The Bias-Variance Trade Off
- 7.3: Explainable AI (XAI)
- 08: Preprocessing and Unsupervised Learning
- 8.1: Chapter 8 Summary
- 8.2: Dimensionality Reduction Methods
- 8.3: Clustering Methods
- 8.4: Vector DBs Similarity Search at Scale (The Big Picture)