Designing Large Language Model Applications

Experienced ML researcher Suhas Pai provides practical advice on dealing with commonly observed failure modes and counteracting the current limitations of state-of-the-art models. You'll take a comprehensive deep dive into the Transformer architecture and its variants. And you'll get up-to-date with the taxonomy of language models, which can offer insight into which models are better at which tasks.
You'll learn
Clever ways to deal with failure modes of current state-of-the-art language models, and methods to exploit their strengths for building useful products
How to develop an intuition about the Transformer architecture and the impact of each architectural decision
Ways to adapt pretrained language models to your own domain and use cases
How to select a language model for your domain and task from among the choices available, and how to deal with the build-versus-buy conundrum
Effective fine-tuning and parameter efficient fine-tuning, and few-shot and zero-shot learning techniques
How to interface language models with external tools and integrate them into an existing software ecosystem