You've successfully trained a basic neural network, getting it to learn patterns from your training data. However, a model that only performs well on data it has already seen isn't very useful. The real goal is generalization: how well the network performs on new, unseen data. Often, networks learn the training data too well, a phenomenon called overfitting, which leads to poor generalization.

This chapter focuses on diagnosing and addressing this challenge. We will cover practical techniques to improve your network's ability to generalize and perform reliably on data it hasn't encountered during training.

Specifically, you will learn about:

• Identifying overfitting and its counterpart, underfitting.
• Using validation sets effectively to monitor generalization during training.
• Applying regularization methods like $L1$, $L2$, and dropout to constrain model complexity.
• Implementing early stopping to prevent the network from training for too long.
• Strategies for tuning hyperparameters to optimize model performance.