Generative AI

Description

A comprehensive course on generative AI covering transformers, GPT architectures, prompt engineering, fine-tuning (LoRA/QLoRA), retrieval-augmented generation (RAG), diffusion models for image generation, evaluation and safety (hallucination, alignment, RLHF), and LLM agents. Uses free tools: HuggingFace, Google Colab, ChromaDB, Groq API, Stable Diffusion.

Table of contents

1. Chapter 1 Foundations (language models, tokenization, embeddings)
2. Chapter 2 Transformer architecture
3. Chapter 3 GPT and autoregressive generation
4. Chapter 4 Prompt engineering
5. Chapter 5 Fine-tuning (LoRA, QLoRA, PEFT)
6. Chapter 6 Retrieval-augmented generation (RAG)
7. Chapter 7 Diffusion models and image generation
8. Chapter 8 Evaluation, safety, and alignment
9. Chapter 9 LLM agents and tool use
10. Chapter 10 Capstone projects

Prerequisites

Python proficiency and basic ML/deep learning knowledge. Familiarity with PyTorch helpful.

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