Deep Learning and Transformers
A Graduate-Level Course
This textbook provides a comprehensive treatment of deep learning and transformer architectures, emphasizing mathematical rigor while maintaining practical relevance through complete derivations, concrete examples, and implementation guidance.
📚 34 Chapters
📄 429 Pages
🎯 10 Parts
Explore by Part
Part I
Mathematical Foundations
3 chapters
Linear Algebra • Calculus • Probability
Part II
Neural Network Fundamentals
3 chapters
FFN • CNN • RNN
Part III
Attention Mechanisms
3 chapters
Fundamentals • Self-Attention • Variants
Part IV
Transformer Architecture
3 chapters
Model • Training • Analysis
Part V
Modern Variants
4 chapters
BERT • GPT • T5 • Efficient
Part VI
Advanced Topics
4 chapters
Vision • Multimodal • Long Context
Part VII
Implementation
3 chapters
PyTorch • Hardware • Best Practices
Part VIII
Domain Applications
6 chapters
NLP • Code • Vision • Knowledge
Part IX
Industry Applications
3 chapters
Healthcare • Finance • Legal
Part X
Production Systems
2 chapters
Observability • DSL & Agents