Deep Dive Into LLMs

Go beyond basic prompting by mastering transformer internals and fine-tuning techniques to build sophisticated, production-ready AI agents integrated directly into your engineering workflows. * **Architectural mastery** of Transformer blocks and attention mechanisms * **Fine-tuning expertise** using LoRA and QLoRA techniques * **Custom agent construction** with advanced reasoning and tool-use * **Open-source integration** into production software engineering workflows

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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