Rezultati pretraživanja
  1. 26. kol 2010.

    NEW: Neural Networks - Part I: A simple handwriting recognition system in .NET - http://is.gd/eEXsm,

  2. 28. velj 2017.

    This is handy to keep, so I’ll put it here (credits to Leon Bottou and Marek Rei)

  3. 15. lis 2019.

    Day 3⃣7⃣ of I went through the rudiments of building a Convolutional Neural Network (CNN) in PyTorch normalization, loss function,

  4. 1. srp 2019.

    BackProp Festival : Models & theories on how neural circuits in the brain could approximate the error back-propagation algorithm

  5. 20. sij

    Given the smoothness of videos, can we learn models more efficiently than with ? We present Sideways - a step towards a high-throughput, approximate backprop that considers the one-way direction of time and pipelines forward and backward passes.

  6. 13. lip 2016.
  7. 14. lip 2017.
  8. 28. velj 2019.

    if you want to find a new method, it’s hard because there isn’t the hardware for it. This makes it challenging to find alternatives to

  9. 14. ruj 2019.

    Fully differentiable Tic-Tac-Toe game using straight-through estimation, a powerful tool for backpropagation through discrete actions.

  10. 20. pro 2016.
  11. 7. srp 2017.

    Just getting started on ! Getting right seems to be one of the terminal steps in the success of multi layer neural nets.

  12. 3. svi 2019.

    Tutorial 2: Dr. Abdellah Chkifa from . "An introduction to gradient descent algorithms". Theory-centered presentation of the maths behind the revolution and the big role played by the algorithm in reviving deep neural networks.

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  13. 3. velj 2018.

    w/ quotes 4m & : '“We are born knowing there are causal relationships in the world, that wholes can b made of parts, & that the world consists of places & objects that persist in space & time. No machine ever learned *any* of that stuff using

  14. 26. pro 2018.

    Cats ready to pack up the ol’ pseudo-thought-leader show just because their idols aren’t there to direct their thoughts manually for few days! Just cheesin but A single master leaving my purview is welcomed as a new state with new synchronicity to mine.

  15. 4. sij 2019.

    Applying Backprop framework to Convolutional Neural Networks: “Bayesian Convolutional Neural Networks with Bayes by Backprop” via

  16. 11. lis 2019.

    is one of the greatest ideas in deep learning.

  17. 9. pro 2015.
    Odgovor korisniku/ci
  18. 30. ruj 2017.
  19. 29. ožu 2019.

    Friday article is here. Tackling the demon called Gradient Descent. A very simplified presentation of this elegant concept @rishirajsidhu

  20. 17. ožu 2019.

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