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Prikvačeni tweet
13 months. 250 pages. I wrote an ML book! Want to learn how to ship ML in practice? Check it out! Includes tips from
@WWRob,@mrogati,@cdubhland and more! It'll be out in winter & you can preorder it now. Amazon: http://bit.ly/mlpowered O'Reilly: http://bit.ly/mlpowered-oreilly …pic.twitter.com/PRD5PLfg5S
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Emmanuel Ameisen proslijedio/la je TweetHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi
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Emmanuel Ameisen proslijedio/la je Tweet
Just got my copy of Building Machine Learning Powered Applications by Emmanuel Ameisen Having worked with
@mlpowered as Head of AI at@InsightFellows, I vouch for how fantastic his guidance is on this topic — Congrats Manu!pic.twitter.com/J86KyygDb7
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Some of the first readers have tweeted or dm’d me photos of their books when they’ve received them. It warms my heart every time, please feel free to keep doing so
!Hvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Copies of BMLPA (http://bit.ly/mlpowered ) are starting to arrive! Definitely the first part of this writing process that has been ahead of schedule
https://twitter.com/shrikar84/status/1222691157299220480 …
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It’s ready (ish), my blog is live at http://mlpowered.com It will be where I post my writing going forward. For now, I’ve ported my 2019 predictions over from Medium, and they were surprisingly accurate! Check them out and subscribe for updates!
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I am being told the slide in question did not make the editing cut... oops Here is the entire slide deck, all slides includedhttp://bit.ly/nlp-breakfast
Prikaži ovu nitHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Last week the
@feedly team had me over to chat about some of the practical ML tips I’ve been writing about. The recording is available now. It’s a short video about why and how you should look at your data, including a slide copied from@karpathy :)https://youtu.be/xNGI0B_Sn1EPrikaži ovu nitHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Much of the success of ML is conditioned on how you represent data. This is just as true for deep learning, especially in domains outside of vision. If you can encode your data in a format usable by SotA models, you are in a good spot. If you can’t, modeling gets harder.
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Just gave a talk on vectorization to generate features and inspect mode errors at
@feedly’s NLP breakfast. Nice and engaging group, and a relatively short talk (20-30 minutes) I’ll share the video when it’s out but if you want the takeaways ahead of time, check out the book!Hvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
At the inaugural
@InsightFellows x@fastdotai, and it is starting off strong! First, a talk about using transformers to predict results of chemical reactions by Karl Heyer. The model works by translating text representations of molecules, crazy! Next up,@mcleavey and MuseNet!Hvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Working on spinning up a blog and instantly deactivated all options to comment. I love hearing feedback or recommendations but comments feel like the worst medium for that. Even big news sites’ comment sections are a dumpster fire.
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BMLPA (http://bit.ly/mlpowered ) sometimes uses arbitrary names for prod ML concepts: Have a model screen inputs to another model -> filtering model Have a model in prod but don’t use it -> shadow mode May not be the best names, but it beats naming each concept after a muppet.
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Emmanuel Ameisen proslijedio/la je Tweet
We will have a new
#NLPBreakfast this Thursday in our@feedly2 office! An exciting discussion on how to build intuitions before building models with@mlpowered , whose new ML book just made it to the top of the charts! How to join & learn more: https://blog.feedly.com/nlp-breakfast-13-building-intuitions-before-building-models/ …pic.twitter.com/axOoYoTnGy
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The goal of BMLPA (http://bit.ly/mlpowered ) is to be practical, so it comes with: - Notebooks demonstrating methods to explore data, debug models, and more - A Flask app serving models built throughout the book! Check it out, PRs and suggestions welcome!https://github.com/hundredblocks/ml-powered-applications …
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This morning when I tried to play a song, my Google Play Rick rolled me so I’m definitely not trusting anyone or anything anymore.
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Getting into spaced repetition for memory thanks to
@michael_nielsen and@andy_matuschak ’s work. It feels like unsupervised vs supervised learning Normal reading is unsupervised Spaced repetition provides labels you get tested on at successive epochs, to minimize memory lossHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Amazing articles on ML in production, and constraints of applied systems and organizations. I particularly enjoyed reading twelve truths of ML for the real world: http://deliprao.com/archives/227 https://twitter.com/deliprao/status/1217614370680631296 …
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One open problem in ML is designing interfaces between models. Having a model as part of your application is a challenge. Building a new model that depends on the previous one is even harder. Now you can’t train the first one without retraining the second...
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For the http://bit.ly/mlpowered book I built four successive versions of an ML app: First a heuristic with a bunch of rules. Then a simple model. Then, a much more complex model. Finally, a model that simplified the previous approach. Same lifecycle I’ve seen in industry!
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Emmanuel Ameisen proslijedio/la je Tweet
I think self-supervised learning is probably one of the most interesting, feasible & useful (and maybe a bit under-appreciated) approaches in deep learninghttps://twitter.com/jeremyphoward/status/1216882509692059650 …
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