This will be provide some useful information and content as I learn more and more about Generative AI space. Gather information from different sources and also showing some sample work which I have done so that you can learn and get deeper as you would like.
Where Generative AI sits within AI
Created some mind maps to keep the knowledge graph in your minds and would also aid in easy ramp ups. Will keep updating it.
Some starters
- Understanding Large Language Models - Gentle intro
- Generative AI with Large Language Models - Free course on coursera, one of the best to start with
- LLM University
- Prompt Engineering Complete Guide
- Introduction to Large Language Models and the Transformer Architecture
- Choosing the Right Embedding Model: A Guide for LLM Applications
- What is a vector database?
- Vector Databases: A Beginner’s Guide!
- RAG vs Finetuning — Which Is the Best Tool to Boost Your LLM Application?
Curated Medium Links
- https://medium.com/@nitin.eusebius/list/llm-knowledge-dae5c631c05f
- https://medium.com/@nitin.eusebius/list/llmops-a4a86d4f2ce8
Below are some handpicked resources I found useful where you can learn some fundamentals
- How ChatGPT actually works
- Use Amazon SageMaker to Build Generative AI Applications- AWS Virtual Workshop
- MIT Introduction to Deep Learning | 6.S191
- Introduction to Facebook AI Similarity Search (Faiss)
- What is Similarity Search?
- What is a Vector Database?
- Vector Embeddings for Developers: The Basics