What is weight regularization in neural networks

This recipe explains what is weight regularization in neural networks

Recipe Objective - What is Weight Regularization in neural network?

Regularization is the technique in which slight modifications are made to learning algorithm such that the model generalizes better. This in turn results in the improvement of the model’s performance on the test data or unseen data. In weight regularization, It penalizes the weight matrices of nodes. Weight regularization results in simpler linear network and slight underfitting of training data. Optimization of the value of regularization coefficient is done in order to obtain a well-fitted model. Weight regularization helps in reducing underfitting in model and thus making model a robust and improving accuracy.

This recipe explains what is Weight regularization, its types and how it is beneficial for neural network models.

A Deep Dive into the Types of Neural Networks

Explanation of Weight Regularization.

In L1 weight regularization, the sum of squared values of the weights are used to calculate size of the weights. L1 regularization encourages weights to 0.0 thereby resulting in more sparse weights that is weights with more 0.0 values. L1 regularization sometimes calculate the size of the weight as penalty.

In L2 weight regularization, the sum of absolute values of the weights is used to calculate size of the weights. L2 regularization offers more nuance that is both penalizing larger weights more severely and also resulting in less sparse weights. L2 regularization use in linear regression and logistic regression is often referred as Ridge Regression or Tikhonov regularization. L2 regularization can sometimes calculate the size of the weight as penalty. The L2 regularization approach is the most used and traditionally referred to as “weight decay” in the neural networks field. It is also called as “shrinkage” in statistics that given a name that encourages to think of the impact of the penalty on model weights during learning process. L2 regularization is preferred over L1 regularization as in L1 regularization, the weights may be reduced to zero.

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