Having defined the architecture of a neural network using Keras layers, the next step is to make it learn from data. This chapter focuses on the mechanics of the training process.

You will learn how to prepare a model for training by compiling it, which involves selecting a loss function to measure error, an optimization algorithm to update the model's weights, and metrics to monitor performance. We will cover key concepts like gradient descent and its variants (e.g., Adam, SGD), the conceptual basis of backpropagation (how the network learns adjustments), and the practical implementation using Keras's fit() method.

We'll also explain essential training parameters like $epochs$ and $batch$ $size$, the importance of using validation data to monitor progress, and finally, how to evaluate your model's performance on unseen data using the evaluate() method. By the end of this chapter, you'll understand the complete workflow for taking a defined Keras model and training it effectively.