Self taught Machine Learning Researcher at Google Brain, on Interpretability & more - Sara Hooker

Sara is a research scholar at the Google-Brain team working on building interpretable machine learning models for reliability and robustness. We talk about how she transitioned from economics to now pure research at the Brain team. We also talk in detail about what interpretability means, what are the state-of-art techniques, and what are some of the most important things any machine learning researcher must know.

About the Host:
Jay is a Ph.D. student at Arizona State University, doing research on building Interpretable AI models for Medical Diagnosis.
Jay Shah: https://www.linkedin.com/in/shahjay22/

You can reach out to https://www.public.asu.edu/~jgshah1/ for any queries.
Stay tuned for upcoming webinars!

***Disclaimer: The information contained in this video represents the views and opinions of the speaker and does not necessarily represent the views or opinions of any institution. It does not constitute an endorsement by any Institution or its affiliates of such video content.***

Checkout these Podcasts on YouTube: https://www.youtube.com/c/JayShahml
About the author: https://www.public.asu.edu/~jgshah1/