A comprehensive roadmap for mastering Large Language Models (LLMs) – from core mathematics and computing principles to production deployment, advanced applications, and emerging research trends.
This curriculum provides a structured learning path for becoming proficient in LLM Engineering. Each module builds upon previous knowledge, taking you from fundamental concepts to advanced applications and production deployment.
- Module 1: Mathematical Foundations 🧮
- Module 2: Neural Networks & Deep Learning Fundamentals 🔄
- Module 3: Natural Language Processing Fundamentals 📝
- Module 4: Understanding Transformer Architectures 🔍
- Module 5: Modern Large Language Model Architectures 🏗️
- Module 6: Data Processing & Preparation Pipeline 🔤
- Module 7: Training Infrastructure 💻
- Module 8: Pre-Training Large Language Models 📊
- Module 9: Post-Training Techniques & Fine-Tuning 🔬
- Module 10: Model Evaluation & Testing ✅
- Module 11: Model Optimization for Inference ⚡
- Module 12: Production Infrastructure & Deployment 🏭
- Module 13: LLMOps & Model Management 🛠️
- Module 14: Prompt Engineering & Retrieval Augmented Generation 💭
- Module 15: Function Calling & AI Agents 🤖
- Module 16: AI Safety & Security Considerations 🔒
- Module 17: Working with Multimodal Language Models 🚀
- Module 18: Model Performance & Optimization ⚡
- Module 19: Production Monitoring & Maintenance 📈
- Module 20: Enterprise Integration & Best Practices 🏢
- Module 21: Future Trends & Research Directions 🔮
Each module contains:
- Detailed theoretical explanations
- Practical examples and code samples
- Hands-on exercises and projects
- Curated learning resources
- Best practices and industry insights
- Follow the modules sequentially
- Complete all exercises and projects
- Refer to provided resources for deeper understanding
- Join discussions and contribute to the community
Special thanks to all contributors and the AI/ML community for their valuable insights and feedback.
Happy learning and building innovative AI systems!