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colab's Issues

Deep Learning insights: Lessons learned from BUI imputation

  • Using large batch size (~50% of training dataset) smoothed out learning process, otherwise losses fluctuated dramatically with each step
  • Scale target variable with large range (0-millions) to enable consistent learning. In this case divide by 1e5. Note that this discounts smaller values.
  • Validation not working properly
  • Consider exploring adaptive learning rate and smaller batch sizes on other tasks

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