Having established the process for building and training basic deep neural networks, we now turn our attention to a frequent issue encountered in practice: overfitting. An overfitted model learns the training data too well, including its noise and specific patterns, resulting in poor performance on new, unseen data.

This chapter introduces techniques designed to combat overfitting and enhance the generalization capability of your models. We will examine several key regularization methods:

• L1 and L2 Regularization: Understand how adding penalties to the loss function based on the magnitude of weights ($L_1 = \lambda \sum |w_i|$ or $L_2 = \lambda \sum w_i^2$) can constrain model complexity.
• Dropout: Learn how randomly setting a fraction of neuron activations to zero during training helps prevent complex co-adaptations between neurons.
• Early Stopping: Implement a strategy to monitor validation performance and stop training before the model begins to overfit.
• Batch Normalization: Explore how normalizing activations within mini-batches can stabilize and accelerate training, often providing a regularizing effect.

Finally, we will introduce the basics of Hyperparameter Tuning, covering fundamental strategies like Grid Search and Random Search to help find better configurations for elements like learning rate, network size, or regularization strength. By the end of this chapter, you will be equipped with practical tools to build more reliable and better-performing deep learning models.