Regularization in Deep Learning

Regularization In Deep Learning

AI, But Simple Issue #7

One of the most common problems encountered by deep learning enthusiasts and professionals is the problem of overfitting.

Overfitting can lead to poor performance on new data, especially in the presence of outliers or noise in the training set.

This has become even more significant in the last 5 years, as neural network architectures have become extremely complicated (which make them prone to overfitting).

There are many ways to prevent overfitting in the world of deep learning, and one of the most popular ways to do so is called regularization.

Regularization is a technique used in machine learning and deep learning to prevent overfitting and improve the generalization of a given model.

• When a model fits the noise of the dataset too well, it will have performance issues on new data (only performing well for the train dataset)

• If you want to learn more about overfitting, read this previous issue

Regularization involves adding a “penalty term” to the loss function when the model is training.

• This penalty prevents the model from becoming too complex or having large weights, which helps control the amount of noise a model will fit in a dataset.

Some common regularization methods used frequently in deep learning are L1 and L2 regularization, dropout, early stopping, and more.

• Through regularization, models become more robust and better at making accurate predictions on unseen data.

Let's dive into some of the most common regularization methods used in deep learning, starting with L1 and L2 regularization:

L1 & L2 Regularization

L1 and L2 are the most common types of regularization in deep learning. These methods update the cost function by adding another term to reduce the probability of overfitting.

In deep learning, regularization sort of “penalizes” the weight matrices of the nodes.

• There is a certain coefficient, called a regularization coefficient, which is used in both L1 and L2 (the coefficient is a parameter that needs to be tuned)

• If the value of the coefficient is too high, the model will start underfitting, as the model will become more linear, but when it is too low, the regularizing effect will not be as strong.