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Recommender systems and high-frequency trading (Practical AI #126)

Podcast |: Changelog Master Feed
Publisher |: Changelog Media
Media Type |: audio
Podknife tags |: Business,; Entrepreneurship,; Interview,; Master Feeds,; Software How-To,; Tech News,; Technology,; Web Development
Categories Via RSS |: Technology
Publication Date |: Mar 23, 2021
Episode Duration |: 00:43:22

Description
Content Encoded

David Sweet, author of "Tuning Up: From A/B testing to Bayesian optimization", introduces Dan and Chris to system tuning, and takes them from A/B testing to response surface methodology, contextual bandit, and finally bayesian optimization. Along the way, we get fascinating insights into recommender systems and high-frequency trading!

David Sweet, author of “Tuning Up: From A/B testing to Bayesian optimization”, introduces Dan and Chris to system tuning, and takes them from A/B testing to response surface methodology, contextual bandit, and finally bayesian optimization. Along the way, we get fascinating insights into recommender systems and high-frequency trading!

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Featuring:

David Sweet – Twitter, LinkedIn
Chris Benson – Twitter, GitHub, LinkedIn, Website
Daniel Whitenack – Twitter, GitHub, Website

Show Notes:

Books

“Experimentation for Engineers” by David Sweet
- Tuning Up | GitHub
- Manning 40% discount code: podpracticalAI19

Something missing or broken? ai-126.md">PRs welcome!

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