Preface

The field of artificial intelligence is experiencing its most transformative period since its founding in the 1950s. Large language models can write code, generate images, and engage in sophisticated reasoning. Transformer architectures have unified approaches across text, vision, audio, and video. The demand for engineers who can build, deploy, and maintain AI systems has never been greater.

Yet a gap persists. Most practitioners fall into one of two camps: researchers who understand the theory but rarely ship production systems, and software engineers who call APIs without understanding what happens behind the curtain. This book is written for the engineer who refuses to accept that trade-off.

Why This Book Exists

When I began working in AI engineering, I found myself constantly switching between resources. Mathematical textbooks presented elegant theorems but rarely showed a line of code. Online tutorials gave quick recipes but crumbled when I needed to debug, adapt, or scale. Research papers assumed familiarity with notation and concepts that took weeks to backfill.

This textbook is the resource I wished I had. It starts where a programmer is — with Python and basic math — and builds systematically to the frontier of modern AI. Every formula is accompanied by an intuitive explanation. Every concept is implemented in working code. Every technique is grounded in a real-world application.

What Makes This Book Different

Rigorous but accessible. We present mathematical formulations with medium intensity: you will see the key equations and understand what each variable means, but we will not ask you to work through formal proofs. When a derivation illuminates the concept, we include it. When it does not, we point you to references.

Code-first. Every chapter includes three standalone Python examples, exercise solutions, and case study code. We use NumPy for foundations (Chapters 1–10), PyTorch for deep learning (Chapters 11+), and HuggingFace Transformers for NLP and LLMs (Chapters 20+). All code follows PEP 8, includes type hints and Google-style docstrings, and uses torch.manual_seed(42) for reproducibility.

Systems perspective. This is not just a machine learning textbook. Part VI covers the engineering that makes AI work in production: retrieval-augmented generation, agent architectures, inference optimization, MLOps, and distributed training. These chapters reflect the reality that most AI engineering work happens after the model is trained.

Exhaustive practice. Each chapter includes 25–40 exercises progressing from conceptual checks to research-level challenges, 20–30 quiz questions with hidden answers, and two detailed case studies. You learn AI by doing AI.

How This Book Is Organized

The book follows a deliberate progression:

Part I (Chapters 1–5) builds the mathematical and computational foundations. Even experienced programmers will benefit from seeing linear algebra, calculus, and probability through the lens of AI engineering.

Part II (Chapters 6–10) covers classical machine learning. These methods remain essential — both as practical tools and as the conceptual scaffolding for deep learning.

Part III (Chapters 11–17) introduces deep learning from the ground up. You will build neural networks from scratch, learn to train them reliably, and explore convolutional, recurrent, and generative architectures.

Part IV (Chapters 18–25) is the heart of the book. You will implement attention mechanisms, build transformers, understand pre-training, work with decoder-only language models, learn prompt engineering, fine-tune LLMs, and study alignment techniques including RLHF and DPO.

Part V (Chapters 26–30) extends beyond text to vision transformers, diffusion models, multimodal systems, speech, and video — the frontiers of generative AI.

Part VI (Chapters 31–35) covers AI systems engineering: RAG, agents, inference optimization, MLOps, and distributed training.

Part VII (Chapters 36–39) treats advanced topics: reinforcement learning, graph neural networks, interpretability, and AI safety.

Part VIII (Chapter 40) looks ahead to where AI engineering is going.

Part IX presents three capstone projects that integrate concepts across multiple parts into production-grade applications.

Conventions Used in This Book

  • Bold lowercase for vectors: $\mathbf{x}$, $\mathbf{w}$
  • Bold uppercase for matrices: $\mathbf{W}$, $\mathbf{X}$
  • Italic for scalars: $n$, $\alpha$, $\lambda$
  • Code appears in monospace font
  • Key terms appear in bold at first use
  • Cross-references use the format "as we saw in Section X.Y"
  • Callout boxes indicate intuitions (💡), real-world applications (📊), common pitfalls (⚠️), advanced material (🎓), and best practices (✅)

Acknowledgments

This book builds on the work of thousands of researchers, engineers, and educators. Special thanks to the open-source communities behind PyTorch, HuggingFace, and the countless libraries that make modern AI engineering possible.

This book is best read with a terminal open. The code is not decoration — it is the curriculum.