Deep Dive Into LLMs

Master the architectural intricacies of transformers and attention mechanisms to design, train, and innovate cutting-edge generative models for advanced data science research. * **Master transformer architectures** and attention mechanisms from scratch * **Implement custom training loops** for fine-tuning large models * **Optimize model inference** using quantization and pruning techniques * **Architect novel generative models** for complex data science tasks

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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