Abstract

This speeds up training and can lead to more stable convergence. This helps the optimization process to overcome small local minimal and converge faster. Instead of using a fixed learning rate, learning rate schedules adaptively change the learning rate during training. Techniques like learning rate decay, step decay, or cosine annealing can help improve convergence. These methods often converge faster and are less sensitive to manual tuning of learning rates. Proper weight initialization techniques, such as His initialization or Xavier initialization, can significantly speed up convergence and reduce the likelihood of getting stuck in poor local minima. Batch normalization normalizes activations within a layer, making training more stable and enabling the use of higher learning rates. It can accelerate training and improve the final model's performance. The choice of optimization methods and their configurations should be tailored to the specific task and dataset, emphasizing the need for careful experimentation and hyper parameter tuning.