Artificial Intelligence Engineering

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40 chapters ~53 hours total 307 pages

1

Front Matter

6 chapters

• Artificial Intelligence Engineering
• Preface
• Acknowledgments
• How to Use This Book
• Prerequisites
• Artificial Intelligence Engineering: Foundational Concepts and Advanced Methods

2

Part I: Mathematical and Computational Foundations

6 chapters

• Part I: Mathematical and Computational Foundations
• Chapter 1: The Landscape of AI Engineering
• Chapter 2: Linear Algebra for AI
• Chapter 3: Calculus, Optimization, and Automatic Differentiation
• Chapter 4: Probability, Statistics, and Information Theory
• Chapter 5: Python for AI Engineering

3

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 chapters

• Part V: Beyond Text — Multimodal and Generative AI
• Chapter 26: Vision Transformers and Modern Computer Vision
• Chapter 27: Diffusion Models and Image Generation
• Chapter 28: Multimodal Models and Vision-Language AI
• Chapter 29: Speech, Audio, and Music AI
• Chapter 30: Video Understanding and Generation

7

Part VI: AI Systems Engineering

6 chapters

• Part VI: AI Systems Engineering
• Chapter 31: Retrieval-Augmented Generation (RAG)
• Chapter 32: AI Agents and Tool Use
• Chapter 33: Inference Optimization and Model Serving
• Chapter 34: MLOps and LLMOps
• Chapter 35: Distributed Training and Scaling

8

Part VII: Advanced and Emerging Topics

5 chapters

• Part VII: Advanced and Emerging Topics
• Chapter 36: Reinforcement Learning for AI Engineers
• Chapter 37: Graph Neural Networks and Structured Data
• Chapter 38: Interpretability, Explainability, and Mechanistic Understanding
• Chapter 39: AI Safety, Ethics, and Governance

9

Part VIII: The Frontier

2 chapters

• Part VIII: The Frontier
• Chapter 40: The Future of AI Engineering

10

Part IX: Capstone Projects

4 chapters

• Part IX: Capstone Projects
• Capstone Project 1: Build a Production RAG System with Guardrails
• Capstone Project 2: Fine-Tune and Deploy a Domain-Specific LLM
• Capstone Project 3: End-to-End Multimodal AI Application

11

Appendices

8 chapters

• Appendix A: Mathematical Foundations
• Appendix B: Statistical Tables
• Appendix C: Python Reference for AI Engineering
• Appendix D: Data Sources and Datasets
• Appendix E: Glossary
• Appendix F: Notation Guide
• Appendix G: Answers to Selected Exercises
• Appendix H: Bibliography

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