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AI/ML Textbook
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  • Textbook Content
  • Part 1 Mathematical Foundations for Machine Learning
    • Ch 1: Linear Algebra for ML
    • Ch 2: Calculus and Optimization
    • Ch 3: Probability and Statistics
  • Part 2 Python Ecosystem and Classical Machine Learning
    • Ch 4: Python Data Science Stack
    • Ch 5: Supervised Learning Fundamentals
    • Ch 6: Classification Algorithms
    • Ch 7: Ensemble Methods
    • Ch 8: Unsupervised Learning
  • Part 3 Deep Learning Fundamentals
    • Ch 9: Neural Network Foundations
    • Ch 10: Training Deep Networks
    • Ch 11: Convolutional Neural Networks
    • Ch 12: Sequence Models
  • Part 4 Modern Architectures and Large Language Models
    • Ch 13: Transformer Architecture
    • Ch 14: Pre-trained Language Models
    • Ch 15: Fine-tuning and Alignment
    • Ch 16: Efficient Transformer Architectures
    • Ch 17: Vision Transformers
  • Part 5 Generative Models
    • Ch 18: Variational Autoencoders
    • Ch 19: Generative Adversarial Networks
    • Ch 20: Diffusion Models
  • Part 6 Multimodal AI and Vision-Language Models
    • Ch 21: Vision-Language Foundations
    • Ch 22: Text-to-Image and Beyond
  • Part 7 Applied AI Systems
    • Ch 23: Retrieval-Augmented Generation
    • Ch 24: Agentic AI Systems
    • Ch 25: Reinforcement Learning
    • Ch 26: MLOps and Production Systems
    • Ch 27: AI Ethics and Responsible Development
  • Appendices
  • Appendix A: Environment Setup Guide
  • Appendix B: GPU Computing Fundamentals
  • Appendix C: Cloud Platform Quick Start
  • Appendix D: Mathematics Reference
  • Appendix E: Library Quick Reference
  • Resources
  • Library Reference
  • Learning Paths
Home / Part 4

Part 4: Modern Architectures and Large Language Models

Explore transformer architecture, pre-trained language models, fine-tuning techniques, and efficient inference.


Chapters in This Part

Chapter 13: Transformer Architecture

Self-attention, multi-head attention, positional encoding, and transformer blocks....

Intermediate Advanced
120 min
Chapter 14: Pre-trained Language Models

BERT, GPT, tokenization, and the Hugging Face ecosystem....

Intermediate Advanced
90 min
Chapter 15: Fine-tuning and Alignment

LoRA, RLHF, DPO, and parameter-efficient fine-tuning....

Advanced Expert
120 min
Chapter 16: Efficient Transformer Architectures

Flash attention, MoE, state space models, and inference optimization....

Advanced Expert
105 min
Chapter 17: Vision Transformers

ViT, Swin Transformer, and vision-only applications....

Intermediate Advanced
90 min
Part 3: Deep Learning Fundamentals Part 5: Generative Models

© 2026 AI/ML Engineering Textbook

A comprehensive guide to AI/ML engineering, from mathematical foundations to agentic systems.