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