Rezultati pretraživanja
  1. 22. ožu 2019.

    Great intro in with easy to follow examples written in and Flux.jl .

  2. 5. sij 2018.

    feels like a pretty good description of the day-to-day practice of . I think about functions, objects, variables, modules, conditionals, loops, tests, API, tuning... and datasets, overfitting, etc. Neurons? ¯\_(ツ)_/¯ Well played

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  3. 1. lis 2019.

    Here's an article in about how allows scientists to apply and in new unprecedented ways thanks to its capabilities (aka write a program, take its derivative):

  4. 5. sij 2018.
  5. 22. lis 2018.

    Want to improve the speed of your HM-LSTM training? See our paper with and Bjorn De Sutter to get a 4x speedup over on Volta using

  6. 5. sij 2018.

    Ao now is dead, long live the new buzz word:

  7. 6. ožu 2019.
  8. 10. sij 2018.
  9. 9. svi 2019.
  10. 5. lip 2019.

    Missed the Event in Elba. Are you talking on another event in Europe this year with topic or ?

  11. 11. srp 2019.
  12. ..in practice training data is scarce for all but a small set of problems, a core question is how to incorporate prior knowledge into a model. I really like where deep learning is going.

  13. 30. pro 2019.

    You want to fix our broken society? How about swapping the House of Representatives for a ***House of Representations***?. You w. me ? ?

  14. 16. srp 2016.

    seems like the wrong term for where that area is going. seems more apt.

  15. 9. srp 2019.

    It was great to speak in Hong Kong at about . The audience loved the Trebuchet demo!

  16. 24. tra 2019.

    On TV this week we conclude our short series by looking at some of the potential use cases of and how it can be applied throughout a supply chain. via

  17. 10. tra 2019.

    In this episode of TV we tackle , the latest descendant of deep learning, and learn how this exciting new development means we can now tackle challenges which previously were seen as “unsolvable”. via

  18. 17. lis 2019.
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  19. 18. ruj 2019.

    It was a really fun panel to participate in and talk about .

  20. 29. srp 2019.

    A research team from and the propose that extensive scientific computing and machine learning domains both require linear algebra support on their underlying structures.

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