Quickly get up and running on GenAI for quants

Do you want to become a sophisticated consumer of GenAI applications? Or build a GenAI prototype? The links below will get you going. Parts 2 & 3 are for my fellow geeks with some knowledge of math and statistics and Python coding. If you are primarily interested in the big ideas about GenAI, skip over the technical portions.

Part 1 - The big picture

Andrewj Karpathy's [1hr Talk] Intro to Large Language Models. It is a nice mostly non-technical introduction.

Max Leykin , another data science OG, shared this link. Per Max "I really enjoyed 3Blue1Brown’s latest video GPTs. Great balance between technical but in a quick and visual presentation"

Part 2 - Underlying theory

Once you have the big picture, build out a basic understanding of neural networks and then how NLP methods work in the neural network framework. In particular, get an understanding of the transformer architecture, which underlies the current gen of Large Language Models. Andrew Ng has a great sequence of courses on deep learning on Coursera. View the material for Neural Networks and Deep Learning and Sequence Models.

Part 3 - GenAI in practice

Now that you have the basic building blocks in place, learn how to put them together to solve some practical use cases, and the Python packages you will need to do so.

Deeplearning.ai has several short courses and videos about practical GenAI applications. Get started on these:

LangChain: Chat with your Data

Introduces Python's LangChain package - a popular package for building out GenAI applications. It has built-in integrations for lots of LLMs and other components. Start with this course as the others mentioned below also use LangChain.

Advanced Retrieval for AI with Chroma

Introduces the RAG pipeline as well as several related algorithms. RAG has become the default architecture for lots of enterprise applications.

Knowledge Graphs for RAG

Knowledge Graphs are way to improve RAG performance. This course introduces what knowledge graphs are, and how to use them in practice using Python with Neo4j.

Direct Preference Optimization

This hour-long video explains how an off-the-shelf Large Language Model can be fine-tuned to provide responses based on your preferences.

To wrap up your whirlwind tour, review enterprise-level use cases, what can go wrong and how to fix it. Below are a couple of real-world examples I found useful:

1. OpenAI - video
2. Snorkel AI - video & video