Deep Dive Into LLMs Like ChatGPT

Master the architecture and training pipelines behind modern transformers to bridge the gap between high-level prompting and low-level model optimization for production-grade engineering and research. * **Master pre-training** and tokenization strategies for massive datasets * **Implement SFT and RLHF** to align model behavior * **Quantify model limits** through rigorous benchmarking and evaluation * **Architect RAG pipelines** to mitigate hallucinations and latency

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
  7. Training Pipeline

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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