LLM Deep Dive

Master the inner workings of transformers and fine-tuning techniques to build production-ready AI applications. Transition from traditional software engineering to architecting custom large language model integrations. * **Core architecture mastery** of Transformers and attention mechanisms * **Fine-tuning techniques** for specialized domain performance * **API integration strategies** for production-ready software * **Custom application development** using advanced RAG and prompting

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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