LLM Architecture Deep Dive

Master the internal mechanics of transformers and training pipelines to build more efficient, scalable AI-driven applications. This course bridges the gap between high-level APIs and low-level engineering. * **Deconstruct transformer components** including attention mechanisms and embeddings * **Implement training pipelines** for fine-tuning and optimization * **Optimize model performance** for production software environments * **Evaluate architectural trade-offs** between speed, cost, and accuracy

5 sections ยท 20 lessons

Course outline

Training Pipeline

  1. Three Training Stages
  2. Internet Data Curation
  3. Tokenization Fundamentals
  4. Neural Network Training
  5. Base Models Explained
  6. Text Generation Process

Post-Training

  1. Supervised Fine-Tuning
  2. Conversation Tokenization

Limitations

  1. Hallucinations
  2. Tool Use Integration
  3. Computational Limits
  4. Tokenization Blind Spots

Reinforcement Learning

  1. Reinforcement Learning Basics
  2. RL in Math & Code
  3. Chain of Thought
  4. RLHF for Subjective Tasks
  5. RLHF Limitations

Practical Insights

  1. Mental Model of AI
  2. LLM Ecosystem
  3. Future Capabilities

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