Mastering Regularization: Preventing Overfitting in Neural Networks
In the quest for powerful neural networks, a common pitfall is overfitting. This occurs when a model learns the training data too well, including its noise and specific quirks, leading to poor performance on unseen data. Regularization techniques are our arsenal against this challenge, helping our models generalize better.
The Problem: Overfitting Explained
Imagine a student memorizing answers for a test instead of understanding the concepts. They might ace the practice questions but falter on the actual exam. Similarly, an overfitted neural network performs exceptionally on training data but fails to predict accurately on new, real-world examples. This is characterized by a large gap between training accuracy and validation/test accuracy.
Overfitting, which leads to poor generalization on unseen data.