Training complex deep learning models effectively often requires more than standard optimizers and fixed learning rates. This chapter focuses on techniques to improve convergence speed, model generalization, and resource efficiency during training.

You will examine optimization algorithms beyond standard SGD or Adam, such as AdamW and Lookahead. We will cover how to implement dynamic learning rate schedules, including cosine annealing and warmup phases, to fine-tune the training process. We will also cover regularization methods like label smoothing and advanced weight decay.

Furthermore, this chapter addresses practical training challenges. You'll learn techniques like gradient clipping to manage unstable gradients and gradient accumulation to simulate larger batch sizes ($N_{effective} = N_{accum} \times N_{batch}$). We will introduce automatic mixed-precision (AMP) training using torch.cuda.amp for faster computation and reduced memory usage on compatible hardware. Strategies for handling massive datasets using IterableDataset and integrating automated hyperparameter tuning tools complete the discussion.