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annamaneni sriharsha

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Everything posted by annamaneni sriharsha

  1. RT @MikeTamir: A Feature Selection Tool for Machine Learning in Python https://t.co/C0JkeUjTuP #AI #DeepLearning #MachineLearning #DataScie…

  2. RT @AndrewYNg: The new UN Climate report shows our planet is nearing crisis. While individuals tweaking their carbon footprint is a good st…

  3. RT @haipinglu: "How to write a great/any research paper" slides in PDF https://t.co/q10dR5b0qI by @NandoDF, Ulrich Paquet and @sgouws, @De…

  4. RT @StatMLPapers: A Unified Analysis of Stochastic Momentum Methods for Deep Learning. (arXiv:1808.10396v1 [cs.LG]) https://t.co/IxdKhV5UP7

  5. RT @MelMitchell1: After watching this great talk by Percy Liang, I wondered if anyone is using GANs to try to generate test examples that a…

  6. RT @yaroslavvb: Notes on optimization-related papers from @icmlconf last week https://t.co/oZpN8VFQID

  7. RT @karpathy: most common neural net mistakes: 1) you didn't try to overfit a single batch first. 2) you forgot to toggle train/eval mode f…

  8. @RanjayKrishna @Stanford Will be the lecture be available online later

  9. @sytelus @DmitryUlyanovML Doesn't pytorch's nn.DataParallel take care of that?

  10. @ngutten Thanks for the explanation :)

  11. RT @slashML: Anyone having trouble reading a particular paper? Post it here and we'll help figure out any parts you are stuck on. https://t…

  12. RT @AlfredoCanziani: Very pretty notes by @TessFerrandez. Check them out! https://t.co/4jtbOKuHhX

  13. RT @MSFTResearch: One of the main challenges for #AI remains unsupervised learning, at which humans are much better than machines. Yoshua B…

  14. RT @michael_at_work: Bayesian optimisation, how to implement the basics yourself, some tricks of the trade, and introduce the scikit-optimi…

  15. RT @jeremyphoward: Really thrilled to be able to release today, with @the_antlr_guy (who did all the work): The Matrix Calculus You Need Fo…

  16. RT @yaroslavvb: This is a package that me and @TimSalimans created to fit much larger nets into GPU memory. I've put detailed explanation o…

  17. RT @hardmaru: Learning to Segment Everything. Segmentation mask labelling is an expensive task. They propose semi-supervised training metho…

  18. https://t.co/5mO4dzcOcj slides & Videos of Interpretable ML available

  19. RT @DrSidMukherjee: This "blame the victim" nonsense has GOT to stop https://t.co/3EMQiHo2BH

  20. @sivareddyg @stanfordnlp @uwnlp will it be telecasted online?

  21. RT @fchollet: Unfortunately it's much easier to predict which jobs will get automated than it is to imagine which jobs will be created...

  22. Why Apple Needs Samsung https://t.co/A2dBf86x1N via @YouTube

  23. #GDDEurope @Firebase @api.ai Amazing to see a demo app developed in 20 mins

  24. RT @johnmyleswhite: Pretty awesome that "Computer Age Statistical Inference" is freely available as a PDF now: https://t.co/5nCAx3DMaK

  25. RT @AndrewYNg: Announcing deeplearning.ai! Deep Learning on Coursera: Master Deep Learning, and build a career in AI! https://t.co/DIDCYAKB…

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