1
Front Matter
6 chapters2
Part I: Mathematical and Computational Foundations
6 chapters3
Part II: Machine Learning Fundamentals
6 chapters- Part II: Machine Learning Fundamentals
- Chapter 6: Supervised Learning: Regression and Classification
- Chapter 7: Unsupervised Learning and Dimensionality Reduction
- Chapter 8: Model Evaluation, Selection, and Validation
- Chapter 9: Feature Engineering and Data Pipelines
- Chapter 10: Probabilistic and Bayesian Methods
4
Part III: Deep Learning Foundations
8 chapters- Part III: Deep Learning Foundations
- Chapter 11: Neural Networks from Scratch
- Chapter 12: Training Deep Networks
- Chapter 13: Regularization and Generalization
- Chapter 14: Convolutional Neural Networks
- Chapter 15: Recurrent Neural Networks and Sequence Modeling
- Chapter 16: Autoencoders and Representation Learning
- Chapter 17: Generative Adversarial Networks
5
Part IV: Attention, Transformers, and Language Models
9 chapters- Part IV: Attention, Transformers, and Language Models
- Chapter 18: The Attention Mechanism
- Chapter 19: The Transformer Architecture
- Chapter 20: Pre-training and Transfer Learning for NLP
- Chapter 21: Decoder-Only Models and Autoregressive Language Models
- Chapter 22: Scaling Laws and Large Language Models
- Chapter 23: Prompt Engineering and In-Context Learning
- Chapter 24: Fine-Tuning Large Language Models
- Chapter 25: Alignment: RLHF, DPO, and Beyond
6
Part V: Beyond Text — Multimodal and Generative AI
6 chapters7
Part VI: AI Systems Engineering
6 chapters8
Part VII: Advanced and Emerging Topics
5 chapters9
Part VIII: The Frontier
2 chapters10
Part IX: Capstone Projects
4 chapters11
Appendices
8 chaptersExplore Related Books
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