About this Handbook: This comprehensive resource guides you through the exciting world of foundation models—the large-scale pre-trained systems transforming artificial intelligence. From architectural fundamentals to cutting-edge applications, this handbook provides a structured path to understanding how these models are built, trained, optimized, and applied across domains.

Learning Path Suggestion:

• 1 Begin with the fundamentals of foundation models, their applications, and early architectures including RNNs and CNNs (Sections 1-3).
• 2 Master transformer architectures, from early variants to self-attention mechanisms and efficient designs (Sections 4-6).
• 3 Explore parameter-efficient tuning and language model pretraining techniques (Sections 7-8).
• 4 Dive into large language models (LLMs), scaling laws, and advanced training approaches (Sections 9-12).
• 5 Learn about inference optimization, compression, and effective prompting strategies (Sections 13-15).
• 6 Discover vision transformers, diffusion models, and multimodal approaches (Sections 16-20).
• 7 Investigate advanced augmentation techniques using tools and retrieval methods (Sections 21-22).

This handbook is a living document, regularly updated to reflect the latest research and industry best practices. Last major review: May 2025.