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classification

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BlackTentacle
BlackTentacle commented Oct 26, 2018

I tried some RNN regression learning based on the code in the "PyTorch-Tutorial/tutorial-contents/403_RNN_regressor.py" file, which did not work for me at all.

According to an accepted answer on stack-overflow (https://stackoverflow.com/questions/52857213/recurrent-network-rnn-wont-learn-a-very-simple-function-plots-shown-in-the-q?noredirect=1#comment92916825_52857213), it turns out that the li

jchen42703
jchen42703 commented Mar 2, 2020

(I will compile a list and hopefully open a PR if needed)

Describe the bug
This behavior is present in a plethora of catalyst's callbacks and losses. It's consistent, but it's definitely confusing for many new users.

To Reproduce
Steps to reproduce the behavior:
Use these functions/classes:
Callbacks

ma-siddiqui
ma-siddiqui commented Apr 18, 2020

Hi NVIDIA Team,

To make this project successful, I would like to suggest to add few things. I would love to assist on this.

  1. Complete technical installation steps to add more values
  2. Details of all pre-requisites, to build this project successfully.
  3. Overall technical background, design and architecture. Just like we provide a technical guide and documentation for other software engine
lewfish
lewfish commented Apr 4, 2019

In experiments.rst, in the first diagram, I'm inferring that ovals are static resources and parallelograms are processes, and that dashed lines means optional. But if that's true, then I'm not sure what the meaning of purple vs. blue is, the scenes should be ovals and solid lines. It might be simpler and easier to parse if everything was a blue oval, although the dashed line makes sense. In the

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