Lecture 5: ML Projects (Full Stack Deep Learning - Spring 2021)

New course announcement ✨

We're teaching an in-person LLM bootcamp in the SF Bay Area on November 14, 2023. Come join us if you want to see the most up-to-date materials building LLM-powered products and learn in a hands-on environment.

https://www.scale.bythebay.io/llm-workshop

Hope to see some of you there!

--------------------------------------------------------------------------------------------- In this video, you will learn how to set up Machine Learning projects like a pro. This includes an understanding of the ML lifecycle, an acute mind of the feasibility and impact, an awareness of the project archetypes, and an obsession with metrics and baselines.

An extensive summary of this lecture can be accessed in this document: https://docs.google.com/document/d/1AA-QEMxsPTygvBhrrrm_0KTYGBZ-dtHlyGY-00iN65s/edit?usp=sharing

00:00 - Introduction
01:45 - Why Do ML Projects Fail?
02:53 - Lecture Overview and Running Case Study
06:14 - Lifecycle (Thinking about the activities in an ML project)
12:53 - Prioritizing Projects (Assessing the feasibility and impact of the projects)
36:51 - Archetypes (Knowing the main categories of projects and implications for project management)
49:06 - Metrics (Picking a single number to optimize)
01:02:36 - Baselines (Figuring out if your model is performing well)
01:10:21 - Conclusion

Видео Lecture 5: ML Projects (Full Stack Deep Learning - Spring 2021) канала The Full Stack