Deep Learning and AI Superhero

1.3 Overfitting, Underfitting, and Regularization Techniques

When training a neural network, achieving the right balance between model complexity and generalization is crucial. This balance lies between two extremes: underfitting and overfitting. Underfitting occurs when a model lacks the necessary complexity to capture the underlying patterns in the data, resulting in poor performance across both training and testing datasets.

Conversely, overfitting happens when a model becomes excessively complex, memorizing the noise and peculiarities of the training data rather than learning generalizable patterns. This leads to excellent performance on the training set but poor results when applied to new, unseen data.

To address these challenges and improve ...