摘要： 1. advantage: when number of features is too large, so previous algorithm is not a good way to learn complex nonlinear hypotheses.2. representation"activation" of unit i in layer jmatrix of weights controlling function mapping from layer j to layer j+13. samplewe have the neural expression 阅读全文

摘要： 1. Underfit = High bias Overfit = High varience2. Addressing overfitting: (1) reduce number of features. Manually select which features to keep. Model selection algorithm disadvantage: throw out some useful information (2) Regularization Keep all the features, but reduce magnitude/valu... 阅读全文