Inside the Black Box: Attention, Transformers, and Large Language Models

Inside the Black Box: Transformers, Attention Mechanisms, and Large Language Models Demystify the architecture powering modern AI. Large language models have transformed what's possible with artificial intelligence. But how do they actually work? This book takes you inside the transformer architecture, building your understanding from first principles to production-ready intuition. What You'll Learn Scaled Dot-Product Attention - Understand why we divide by the square root of the key dimension, with complete mathematical derivations Multi-Head Attention - Learn how parallel attention heads capture different relationship types, and why the parameter count stays constant Positional Encodings - Master sinusoidal embeddings, Rotary Position Embeddings (RoPE), and ALiBi, including context extension techniquesTransformer Blocks - Build encoder, decoder, and encoder-decoder architectures with pre-norm residual connections Large Language Models - Explore training objectives, scaling laws, KV caching, and post-training methods like RLHF and DPO A Practitioner's Approach Every chapter includes worked numerical examples you can verify by hand, complete PyTorch implementations, and exercises with difficulty ratings and time estimates. Common misconceptions are addressed explicitly, and intuition boxes connect the mathematics to real-world understanding. Who This Book Is For Engineers and researchers who want to understand transformers deeply, not just use them. Prerequisites include linear algebra, basic calculus, and familiarity with Python and PyTorch. Part of The AI Engineer's Library Book 4 in a comprehensive series bringing rigorous, practitioner-focused coverage to the foundations of modern AI systems. Read more