SF Machine Learning

SF Bayarea Machine Learning meetup group

[SF Machine Learning @SFMachineLearn]

"Over time, the factors that matter change."

[SF Machine Learning @SFMachineLearn]

Tons of great ML / pipelines / systems talks at this year's (online) @ScaleByTheBay conference - 20% off registration with code "SFBAML20"

[AI by the Bay @ScaleByTheBay]

Automating boring ML stuff? Sign me up! The @SAPConcur team (Catherine Nelson @DrCatNelson, Hannes Hapke @hanneshapke) used @TensorFLow Extended and @Kubeflow Pipelines to increase the data quality that determines how accurate your ML actually is.

https://t.co/6DXBW40sb9

[AI by the Bay @ScaleByTheBay]

Early Bird is almost gone -- hurry up to get yours soon!

https://t.co/9vW3YtaoKR

[AI by the Bay @ScaleByTheBay]

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.

Instantly sponsor with your logo.

https://t.co/NaX17bArh6

[Anyscale @anyscalecompute]

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. https://t.co/UE8ZWGz1Av

[SF Machine Learning @SFMachineLearn]

Exciting summer series of online ML talks from @anyscalecompute kicking off May 13th w/ Profs Michael Jordan & Ion Stoica, free registration here: https://t.co/gtGeJgWLai

[AI by the Bay @ScaleByTheBay]

A couple of days left till Late Bird tickets go on sale. Do not miss out and book your ticket now. https://t.co/lRRsQzqa5k

[Pete Skomoroch @peteskomoroch]

Excited to moderate the panel on AI Products with @AnimaAnandkumar @pbailis @tnguyenvnut at @scalebythebay 2019 https://t.co/dgqU1wn55C #scaleSF

[David Andrzejewski @davidandrzej]

Highly recommend the excellent “Projects To Know” newsletter from @sarahcat21 & @AmplifyPartners: short, well-curated digests of interesting recent ML research papers, code repos, & blogs https://t.co/vGkdpZTc9o

[AI by the Bay @ScaleByTheBay]

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. https://t.co/DY6N5JlEIH

[Elvis Dohmatob @dohmatobelvis]

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) https://t.co/F6g6hmdI46
(Deep DPPs) https://t.co/Uh44KH1Htx

Plse RT

[SF Machine Learning @SFMachineLearn]

Discount code "UGSFBAML" for 20% off #OReillyAI in San Jose Sept 9-12 (early price ends June 14): https://t.co/cCLf8C3UOQ

[AI by the Bay @ScaleByTheBay]

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!

[SF Machine Learning @SFMachineLearn]

Event of interest: O'Reilly #VelocityConf in San Jose (June 10-13) - save 20% w/ code "UGSFBAML", early price ends May 3 https://t.co/r0ubbuLXTX

[SF Machine Learning @SFMachineLearn]

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 https://t.co/tvPKHWCnPb

[Masters of Data @mastersofdata]

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. https://t.co/J2s3A8r5Rv

[SF Machine Learning @SFMachineLearn]

Save 20% on upcoming O'Reilly conferences in San Jose with code "UGSFBAML":

#OReillySACon https://t.co/tHRNiYNuBx

#VelocityConf https://t.co/ofR25Gvoae

[SF Machine Learning @SFMachineLearn]

Thought-provoking blog post by @alexwg about the whether datasets or algorithms have a bigger impact on major AI/ML breakthroughs: "Datasets over Algorithms" https://t.co/q2YfrIVjTj

[SF Machine Learning @SFMachineLearn]

"Training data becomes your biggest expense in many serious machine learning projects."

[SF Machine Learning @SFMachineLearn]

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.

[SF Machine Learning @SFMachineLearn]

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.