Rezultati pretraživanja
  1. 6. pro 2013.

    Congrats to Heath Jackson on scoring in the MAC championship game

  2. Nice person ISO very-decent-bordering-on-fantastic place to dedicated hard work to - do you know of one? My contract is up soonish and I’m looking around. RT appreciated 🔥 , , ,

  3. 16. sij

    If you've wondered - "Which optimizer should I use? ? ? ?" This blogpost by is the best explanation I've seen. It's a surprisingly easy read! Definitely a great / project!

  4. 30. ožu 2019.

    Day 17: Model Selection and Model Boosting, Gradient Descent, Loss Functions, Learning Rate, Regularization etc in & @AlgoquantSavvy

    Prikaži ovu nit
  5. 29. sij
  6. 13. sij

    ¡NUEVO VIDEO! Aprende a usar Adagrad para entrenar una red neuronal en Keras en el video de hoy:

  7. 11. sij
  8. 8. sij

    ¡NUEVO VIDEO! Un optimizador es una pieza crucial en deep learning. Aprende más en el video de hoy.

  9. 】図で理解!”学習”の基盤:ニューラルネットワークの”逆伝搬”と”更新” すごい!!

  10. 20. lip 2019.

    Minimizing cost function is a holy grail in . Here’s a brilliant 10 minute summary of the optimization methods in literature. “Optimizers for Training Neural Networks” etc.

  11. 学習率の最適化の検討。 【学習メモ】ゼロから作るDeep Learning【6章】

  12. 7. ruj 2018.
  13. 数学でもこうだから、ニューラルネット○○要らない論が繰り返されるのは必然

  14. 9. ožu 2018.

    Evening recommendation: my model has significantly improved its performance☺ by exploring some adaptative gradient descent methods like and (specially this one). Don't forget about them!

  15. 25. lis 2017.
  16. 1. velj 2017.
  17. 7. lis 2016.
  18. 3. velj 2016.

    an interesting read on writing fast asynchronous / with ...

  19. 15. ruj 2015.

    What are limitations of architecture BP-MML, what are possible alternatives?

  20. 23. svi 2012.

    If you're not a cadet, watch the President speaking live at the now via :

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