Having learned to construct various neural network architectures using Flux.jl, the next step is to bring these models to life. This chapter focuses on the practical aspects of training your deep learning models and systematically evaluating their performance.

You will learn to implement and understand the components of a model training loop, from processing data in batches and epochs to calculating loss and updating model parameters using optimizers. We will cover how to use callbacks for effective training oversight and examine common metrics like accuracy, $F_1$-score for classification, and Mean Squared Error ($MSE$) for regression to assess your model's effectiveness.

Furthermore, this chapter will guide you through techniques to improve model generalization, such as regularization methods including dropout and weight decay ($L_2$ regularization). You will also address strategies for hyperparameter tuning to find optimal model configurations. Finally, we'll discuss methods for visualizing training progress and debugging common issues encountered during the training process, culminating in a hands-on exercise to solidify these concepts.