Section outline

• Select section Day 1: AI Foundations and Neural Networks (Optional)

  Day 1: AI Foundations and Neural Networks (Optional)

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  • Basics of training and prediction using linear regression.
  • Neural network fundamentals and artificial neural network architecture.
  • Lab: Build and train an ANN using Keras and TensorFlow
• Select section Day 2: Generative AI and Prompt Engineering

  Day 2: Generative AI and Prompt Engineering

  • Introduction to Generative AI and ChatGPT
    • Overview of LLMs and ChatGPT applications.
    • Lab: Practical tasks with OpenAI API.
  • Prompt Engineering
    • Techniques: Iterative refinement, chain-of-thought prompting, and self-feedback.
    • Lab: Apply advanced prompting techniques.
• Select section Day 3: Advanced Tools and Frameworks

  Day 3: Advanced Tools and Frameworks

  • Transformers and HuggingFace (Optional)
    • Transformer architecture and model applications.
    • Lab: Using HuggingFace library for task-specific models.
  • LangChain Basics
    • Create and manage chains, and implement memory techniques.
    • Lab: Develop AI-driven workflows using LangChain.
• Select section Day 4: Embeddings, RAG, and AI Tools

  Day 4: Embeddings, RAG, and AI Tools

  • Embeddings and Vector Stores
    • Embedding flows and visualizations with ChromaDB and Pinecone.
    • Lab: Build a retriever chain with RAG.
  • Tools and Agents
    • Configure and integrate tools for databases and web searches.
    • Lab: Develop applications using tools and agent execution.
• Select section Day 5: Observability and Advanced Implementations

  Day 5: Observability and Advanced Implementations

  • Tracing and Observability
    • Explore LangSmith and LangGraph.
    • Lab: Implement tracing techniques in AI systems.
  • Advanced Applications
    • Combine tools, agents, and tracing for robust AI solutions.