Regularization, Machine Learning, Convex Optimization, Lagrangian, Ml So Good.
to understand this could be, that if network is sparse, number of learnable parameters will be low, thus complexity would reduce, preventing model from overfitting. 2. constraining size of parameter values Putting penalty over the parameter values, This is generally done by adding a penalty term in