Little Bits of Artificial Intelligence

1. Simple Explanation (Layman’s Terms)

Imagine we’re training a neural network to learn from data. During this process, the model adjusts its weights to reduce error.

• If these weights grow too large, the model may memorize the training data instead of learning patterns.
• This leads to overfitting – great accuracy on training data, but poor results on new data.

L2 Regularization helps by gently penalizing large weights.

It:

• Adds a small penalty to the loss for big weights.
• Encourages the model to keep weights small and smooth.
• Makes the model simpler and better at generalizing to new data.

Think of it like adding a friction that keeps the model from overreacting or becoming overly confident.