Recommender systems and high-frequency trading (Practical AI #126)
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English - March 23, 2021 18:35 - 43 minutes - 39.8 MB - ★★★★ - 28 ratingsTechnology Education How To changelog open source oss software development developer hacker Homepage Download Apple Podcasts Google Podcasts Overcast Castro Pocket Casts RSS feed
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, LinkedInChris Benson – Twitter, GitHub, LinkedIn, WebsiteDaniel 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? PRs welcome!