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We're standardizing OpenAI's deep learning framework on PyTorch to increase our research productivity at scale on GPUs (and have just released a PyTorch version of Spinning Up in Deep RL): https://openai.com/blog/openai-pytorch/ …pic.twitter.com/lgvqDdWDoB
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Introducing
#MeenaBot, a 2.6B-param open-domain chatbot with near-human quality. Remarkably, we show strong correlation between perplexity & humanlikeness! Paper: https://arxiv.org/abs/2001.09977 Sample conversations: https://github.com/google-research/google-research/tree/master/meena … https://twitter.com/GoogleAI/status/1222230622355087360 …pic.twitter.com/3xNSV4r4uB
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If you want to go beyond the stuff you will learn in
#MachineLearning MOOCs follow this class from@AnimaAnandkumar (not an easy class if you take it seriously). Video lectures: https://www.youtube.com/playlist?list=PLVNifWxslHCDlbyitaLLYBOAEPbmF1AHg …https://twitter.com/AnimaAnandkumar/status/1222114841386577921 …Hvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
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New paper: Towards a Human-like Open-Domain Chatbot. Key takeaways: 1. "Perplexity is all a chatbot needs" ;) 2. We're getting closer to a high-quality chatbot that can chat about anything Paper: https://arxiv.org/abs/2001.09977 Blog: https://ai.googleblog.com/2020/01/towards-conversational-agent-that-can.html …pic.twitter.com/5SOBa58qx3
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Starting the 2020 edition of @ylecun's Deep Learning class with ~200 students!
This year we *will* end up with annotated
video recordings
and publishable lecture notes
, as we're putting the extra effort to renew
and reorganise
the material.pic.twitter.com/8ph7OBnEBu – mjesto: NYU Courant Institute of Mathematical Sciences
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New NLP News: NLP Progress, Restrospectives and look ahead, New NLP courses, Independent research initiatives, Interviews, Lots of resources (via
@revue)http://newsletter.ruder.io/archive/217744Hvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
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#TransferLearning is one of the most important#AI techniques I don't often recommend PhD Thesis' -@seb_ruder's is exceptional. He's a brilliant writer! Check out this taxonomy / table of contents!!!

#100DaysOfMLCode#100DaysOfCode https://ruder.io/thesis/neural_transfer_learning_for_nlp.pdf …pic.twitter.com/ZSbZSwD9DT
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If you've wondered - "Which
#DeepLearning optimizer should I use?#SGD?#Adagrad?#RMSProp?" This blogpost by@seb_ruder is the best explanation I've seen. It's a surprisingly easy read! http://ruder.io/optimizing-gradient-descent/ … Definitely a great#100DaysOfMLCode /#100DaysOfCode project!pic.twitter.com/P4VWcL55eQHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
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Introducing "Neural Module Networks for Reasoning over Text" accepted to ICLR 2020 w/ Kevin Lin (
@nlpkevinl), Dan Roth (@dannydanr), Sameer Singh (@sameer_) and Matt Gardner (@nlpmattg). Paper: https://arxiv.org/abs/1912.04971 Code: Coming soon! [1/4]pic.twitter.com/i1bMnKjXIg
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I completed my 1st data science project ~30 years ago. Since then I've been continuously developing a questionnaire I use for all new data projects, to ensure the right info is available from the start. I'm sharing it publicly today for the first time.https://www.fast.ai/2020/01/07/data-questionnaire/ …
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NLP Year in Review — 2019 An extensive list of interesting publications, creative and societal applications, tools and datasets, articles, and resources of 2019 by
@omarsar0.https://medium.com/dair-ai/nlp-year-in-review-2019-fb8d523bcb19 …Hvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
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Nice list of references on using deep nets and models of brain and behavior, specifically using ConvNets as models of the visual cortex. https://docs.google.com/document/d/1qil2ylAnw6XrHPymYjKKYNDJn2qZQYA_Qg2_ijl-MaQ …https://docs.google.com/document/d/1qil2ylAnw6XrHPymYjKKYNDJn2qZQYA_Qg2_ijl-MaQ …
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Machine Learning in a company is 10% Data Science & 90% other challenges It's VERY hard. Everything in this guide is ON POINT, and it's stuff you won't learn in an ML book "Best Practices of ML Engineering" This is a lifesaver
#100DaysOfMLCode project http://martin.zinkevich.org/rules_of_ml/rules_of_ml.pdf …pic.twitter.com/eYJ5KpQmVt
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Altair 4.0 is released! http://altair-viz.github.io Try it with: pip install -U altair The full list of changes is at https://github.com/altair-viz/altair/releases/tag/v4.0.0 … ...read on for some highlights.pic.twitter.com/vWJ0ZveKbZ
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The first full paper on
@pytorch after 3 years of development. It describes our goals, design principles, technical details uptil v0.4 Catch the poster at#NeurIPS2019 Authored by@apaszke ,@colesbury et. al. https://arxiv.org/abs/1912.01703Prikaži ovu nitHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
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Our new paper, Deep Learning for Symbolic Mathematics, is now on arXiv https://arxiv.org/abs/1912.01412 We added *a lot* of new results compared to the original submission. With
@f_charton (1/7)pic.twitter.com/GrhQRT5WRW
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For anyone who has ever thought - "Can I learn the math needed for Deep Learning all in one place (& maybe skip the other stuff)?" - this is quite a nice resource! "The Matrix Calculus You Need For Deep Learning" http://explained.ai/matrix-calculus/index.html …
#100DaysOfMLCode (Table of Contents
)pic.twitter.com/RJTJgxwXw4
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I'm sad that the funding we were successfully granted has been revoked because I am going on maternity leave and apparently an extension to account for this is beyond the remit of the funders. It's hard to win funding, to have it taken away again after so much work is gutting
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I wrote an 8k word doc on machine learning systems design. This covers: 1. Project setup 2. Data pipeline 3. Training & debugging 4. Serving with case studies, resources, and 27 exercises. This is the 1st draft so feedback is much needed. Thank you!https://github.com/chiphuyen/machine-learning-systems-design/blob/master/build/build1/consolidated.pdf …
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I'm uncomfortable when papers make claims like "we beat SOTA by ..." or "our results are better than []". They turn research into a competition, not collaboration. Wouldn't it be nice if we can say: "Built upon the work of [], we're able to improve the results by ..."?
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