And we are live!
Scale By the Bay 2020 program is live and registration is open!
Early bird through September 30.
Become a Patron By the Bay so we run the best online event this year and return by the bay next year.
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scale.bythebay.io
Sign up for #RaySummit—A FREE virtual event about Ray, the open-source Python framework for building distributed applications that run at any scale. You'll hear from experts such as @wesmckinn, founder of Ursa Labs and the creator of the pandas project. buff.ly/31ipI9J
Highly recommend the excellent “Projects To Know” newsletter from @sarahcat21 & @AmplifyPartners: short, well-curated digests of interesting recent ML research papers, code repos, & blogs mailchi.mp/amplifypartner…
We've revealed the speaker line-up! Join us at #ScaleByTheBay in San Francisco this November and hear from the top minds in functional programming, service architectures, data pipelines, and AI/ML at scale. scale.bythebay.io/speakers
Determinant Point Processes are a simple way to model probabilities on subsets (combinatorial objects), e.g playlists, shopping baskets, Ad banners, etc. Here are 2 preprints on the subject:
(Non symmetric DPPs) arxiv.org/pdf/1905.12962…
(Deep DPPs) arxiv.org/abs/1811.07245
Plse RT
We are happy to announce the keynote speakers for SBTB 2019:
@helenaedelson@jbeda@heathercmiller
The CFP opens tomorrow and runs until May 31.
Submit your best talks early on
— functional programming
— service architectures
— data pipelines (including for ML/AI)
— &more!
Thanks everyone for coming out to the meetup earlier this week, and especially @amlakhan and @l2k for the great talks, and @Nextdoor for hosting, food/drink, and video! Will post here and on the meetup page when videos are up meetup.com/SF-Bayarea-Mac…
On #MastersOfData, @BenoitNewton chats with @sarahcat21, a Principal at @amplifypartners who focuses on startups that apply tech advances in machine intelligence & enterprise infrastructure to solve real-world problems. Listen in. bit.ly/2WcQA5P
Thought-provoking blog post by @alexwg about the whether datasets or algorithms have a bigger impact on major AI/ML breakthroughs: "Datasets over Algorithms" spacemachine.net/views/2016/3/d…
Back to @l2k's garage robots: a known pitfall of vision ML in robotics where the "target" object is not perfectly centered in the camera field of view (whereas they usually are big training corpora), causing degraded performance.
Meta-problem of ML projects: very hard to know what's actually going to be hard. Progress (or lack thereof) on ML problems can be unpredictable, and human intuitions about "hardness" may be misleading.
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