Deep Dive Into LLMs

A course with 20 lessons.

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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