Deep Learning with Keras

Adopting regularization for avoiding overfitting

Intuitively, a good machine learning model should achieve low error on training data. Mathematically, this is equivalent to minimizing the loss function on the training data given the machine learning model built. This is expressed by the following formula.:

However, this might not be enough. A model can become excessively complex in order to capture all the relations inherently expressed by the training data. This increase of complexity might have two negative consequences. First, a complex model might require a significant amount of time to be executed. Second, a complex model can achieve ...