Rezultati pretraživanja
  1. 26. pro 2018.

    Days 25,26 In these days I learned more about optimizer algorithms like , and the optimizer. Also I gain a lot of intuition about manipulating hyperparameters.

  2. 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!

  3. 10. ruj 2019.
  4. 8. lis 2015.

    Jordan is training a neural network model using , , and !

  5. 18. sij
  6. 9. lis 2018.

    “Intro to optimization in : Momentum, and Adam” by

  7. 13. stu 2019.

    Continuation of . is the same as Adagrad except for the equation where the cache for every weight is adjusted.

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  8. 13. stu 2019.
  9. 4. srp 2019.

    The Role of Memory in Stochastic Optimization by Antonio Orvieto et al. including

  10. 22. tra 2019.
  11. 24. lip 2019.

    "One fun fact about (RMS Propagation) optimization algorithm) it was actually first proposed not in an academic research paper, but in a course that taught :) From there it become widely known." :) Source:

  12. 5. ruj 2019.
  13. 7. ruj 2018.
  14. 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.

  15. 17. sij

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

  16. 16. sij
  17. 8. sij

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

  18. 2. ruj 2019.

    From the Machine Learning & Data Science glossary: Adaptive Subgradient Methods (AdaGrad)

  19. 5. srp 2019.

    سبق وتكلمت عن Loss function و backpropagation و التي تقوم بحساب نسبة الخطأ لغرض تحسين قيم الاوزان من أشهر الدوال التي تقوم بذلك ويوجد غيرها ايضاً كل واحدة منهم لها طريقة مختلفة في العمل

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

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