Regularization is very important for helping neural networks avoid a problem called overfitting. This happens a lot in supervised learning.

So, what is overfitting?

It’s when a model learns the training data too well. Instead of understanding the real patterns in the data, it ends up capturing a lot of extra noise. This makes the model not perform well when faced with new, unseen data.

What is Regularization?

Regularization techniques add a penalty to larger weights in the model. This means it discourages the model from being too complicated. Instead, it encourages simpler models that can do better on different data.

Here are some common types of regularization:

1. L1 Regularization (Lasso): This method adds a penalty based on the absolute value of the weight values. Because of this, some weights can actually become zero. This can make the model easier to understand.

   $L = L_0 + \lambda \sum |w_i|$

2. L2 Regularization (Ridge): This technique adds a penalty based on the square of the weights. It helps keep the model balanced and reduces the chances of overfitting.

   $L = L_0 + \lambda \sum w_i^2$

3. Dropout: This method is often used during training. It randomly ignores certain neurons, which helps the network learn stronger features.

Illustration

Think about trying to fit a complex curve to a set of data points. If you don’t use regularization, the model might make a really wavy curve just to match every single point. This can lead to overfitting.

Regularization encourages smoother curves that are better for new data.

In conclusion, regularization is a helpful tool. It keeps our models focused on the right patterns, without getting distracted by too much noise.