MLLossFunctions

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Library "MLLossFunctions"
Methods for Loss functions.

mse(expects, predicts) Mean Squared Error (MSE) " MSE = 1/N * sum((y - y')^2) ".
  Parameters:
    expects: float array, expected values.
    predicts: float array, prediction values.
  Returns: float

binary_cross_entropy(expects, predicts) Binary Cross-Entropy Loss (log).
  Parameters:
    expects: float array, expected values.
    predicts: float array, prediction values.
  Returns: float