Little Bits of Artificial Intelligence

1. Simple Explanation (Layman’s Terms)

Imagine we’re training a neural network and it’s learning too much from the training data — it starts to remember every little detail, even noise! This leads to overfitting.

Now think of L1 Regularization like a discipline rule:

“Keep only the most important features and ignore the rest.”

It works by penalizing large weights, and actually encourages many weights to become zero.
So the model becomes simpler and sparser — just like cleaning your wardrobe and keeping only the clothes you really wear.