Deep Dive Into LLMs Like ChatGPT

Master the full lifecycle of large language models from pre-training to alignment while uncovering the technical boundaries and genuine capabilities of these transformative neural networks. * **Master pre-training and supervised fine-tuning** workflows * **Optimize model alignment** using RLHF and PPO * **Analyze architectural bottlenecks** and scaling law constraints * **Evaluate inference capabilities** versus stochastic pattern matching

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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