It would be fascinating to instrument deep learning developer workflows, so as to run the numbers on the productivity increase that comes from using tools that reduce cognitive load vs. tools that lay traps for you to fall into
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The first thing that users that recently switched to Keras mention to me is the productivity boost. In the time it would have taken them to debug their way through the implementation of one idea (`zero_grad()` anyone?), they can try out 2, 3 ideas.
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It matters because trying more ideas (with fewer mistakes) means you will converge faster towards better ideas (thus winning competitions more often or increasing your paper acceptance rate). I'm thinking Kaggle kernels or Colab would be a way to gather hard data on this...
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This is one of the reasons why Keras has always been the first choice. I hope 2.0 would be cherry on the cake
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We're doing our best to make it the best it can be, with your help (and the entire community). Thanks!
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