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Improving Deep Learning with Hyperparameter Tuning

This notebook presents regularization and supplemental approaches like initialization that are proven to improve the predictive accuracy of models in Deep Learning.The AI Soccer Coach's accuracy improves to 95%!

decisionBoundaryRegularization

What to use this notebook for:

  • Access regularization techiques such as L1, L2 and dropout to improve predictive accuracy.
  • Develop intuition for how regularization works in order to apply it successfully.
  • Supplement regularization with the appropriate choice of method for weight initiatialization.

Deep Learning models derive power from a large number of tunable parameters that impart capacity for learning. However, that can turn out to be too much of a good thing when making suitable choices for hyper-paramters that influence training. The approaches presented here help to guide wise choices.

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Deep Learning as applied to coaching The Beautiful Game

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