Keras 3

6    Regularization Techniques

In this chapter, we’ll explore the critical challenge that every machine learning practitioner faces: creating models that generalize well beyond their training data. You’ll learn how to identify when our model is too complex or too simple. By the end of this chapter, you’ll have acquired essential tools for building neural networks that capture meaningful patterns without being led astray by noise in the training data.

As we’ve explored in previous chapters, machine learning models can capture increasingly complex patterns in data. We’ve moved from simple linear models to powerful neural networks, each time expanding our ability to represent sophisticated relationships. But this growing power comes with a subtle ...