Regularization in image processing

Regularization is a technique that helps prevent overfitting by penalizing a model for having large weights. Essentially, a model has large weights when it isn't fitting appropriately on the input data.

1. What is the purpose of regularization?
2. What is regularization process?
3. What is regularization and its types?
4. What is L1 and L2 regularization works?

What is the purpose of regularization?

Regularization refers to techniques that are used to calibrate machine learning models in order to minimize the adjusted loss function and prevent overfitting or underfitting. Using Regularization, we can fit our machine learning model appropriately on a given test set and hence reduce the errors in it.

What is regularization process?

In mathematics, statistics, finance, computer science, particularly in machine learning and inverse problems, regularization is a process that changes the result answer to be "simpler". It is often used to obtain results for ill-posed problems or to prevent overfitting.

What is regularization and its types?

Regularization consists of different techniques and methods used to address the issue of over-fitting by reducing the generalization error without affecting the training error much. Choosing overly complex models for the training data points can often lead to overfitting.

What is L1 and L2 regularization works?

L1 Regularization, also called a lasso regression, adds the “absolute value of magnitude” of the coefficient as a penalty term to the loss function. L2 Regularization, also called a ridge regression, adds the “squared magnitude” of the coefficient as the penalty term to the loss function.