Building and training a neural network is often just the starting point. Ensuring your model performs well on new data and refining your development workflow are essential next steps. This chapter focuses on techniques to diagnose and address common training issues, improve model generalization, and manage the training process more effectively.

You will learn to identify overfitting and underfitting, two frequent obstacles in model development. We'll cover methods to mitigate these issues, including regularization techniques like $L_1$, $L_2$, and Dropout, as well as data augmentation strategies. We will also explore practical workflow enhancements using Keras callbacks, such as ModelCheckpoint for saving the best model during training and EarlyStopping to prevent unnecessary computation. Additionally, you'll learn how to properly save and load your trained models for later use and get introduced to TensorBoard for visualizing training progress and model structure. Finally, we'll touch upon the concepts behind hyperparameter tuning.