Learning to rank

Learning rate cosine annealing is a technique used to adjust the learning rate during training. It starts with an initial learning rate and gradually decreases it following a cosine curve. This approach helps in achieving a balance between fast convergence and fine-tuning of the model parameters.

The formula for learning rate cosine annealing is: learning_rate = learning_rate_initial * 0.5 * (1 + cos(pi * epoch / total_epochs)). In this formula, learning_rate_initial is the starting learning rate, epoch represents the current training iteration, and total_epochs is the total number of training iterations. The cosine function ensures a smooth transition of the learning rate from its initial value to a final value of 0.5 * learning_rate_initial.

Cosine annealing helps in preventing the learning rate from becoming too small too quickly, which can lead to slow convergence or getting stuck in local minima. By gradually decreasing the learning rate, the model can fine-tune its parameters more effectively, leading to better performance and generalization on unseen data.