Designing sophisticated CNN architectures, as discussed previously, introduces challenges in training. Very deep networks can be difficult to optimize, prone to issues like vanishing or exploding gradients, and sensitive to hyperparameter settings. This chapter focuses on the techniques required to train these models effectively and efficiently.

We will cover optimization algorithms that adapt learning rates per parameter, such as AdamW and Lookahead. You'll learn about managing the learning rate $\eta$ throughout training using various schedules, including cyclical methods. We will examine regularization techniques like label smoothing and advanced dropout variations to improve generalization. Furthermore, we'll study alternatives to standard batch normalization, suitable weight initialization strategies for deep models, and methods like gradient clipping to maintain training stability. Finally, we cover practical considerations such as mixed precision training for speed and memory savings, alongside strategies for monitoring and debugging the training loop.