20 min listen
Bias in Machine Learning with Rachel Thomas
Bias in Machine Learning with Rachel Thomas
ratings:
Length:
20 minutes
Released:
Aug 15, 2017
Format:
Podcast episode
Description
Most of us have come across a form of bias when we interact with others. These biases can make their way to a machine learning system, leading to unfair decisions. Rachel Thomas, co-founder of fast.ai and researcher in residence at The University of San Francisco explains the origins and implications of bias in machine learning. We also talked about solutions to limit bias.
Rachel also explained the role of linear algebra in machine learning and how to teach it effectively for people working in ML applications. We talked about the fundamental concepts and how they are applied in machine learning.
Rachel also explained the role of linear algebra in machine learning and how to teach it effectively for people working in ML applications. We talked about the fundamental concepts and how they are applied in machine learning.
Released:
Aug 15, 2017
Format:
Podcast episode
Titles in the series (100)
CTO Insights with Yvette Pasqua: Yvette Pasqua is CTO at Meetup and has been in leadership roles for more than a decade. We talked about the process of growing and evolving the Engineering Organization at Meetup. Yvette explained key strategies to align the company with new technology solutions and how she involves people from different divisions at Meetup. We also talked about the experiences that prepared her to take on the role of CTO and how she stays up to date with technology trends. by The Women in Tech Show: A Technical Podcast