Description: Learning rate annealing is a fundamental technique in training artificial intelligence models, especially neural networks. Its main purpose is to adjust the learning rate over time, starting with a relatively high value and gradually decreasing it as the training process progresses. This approach allows the model to make large jumps in the parameter space at the beginning, facilitating the exploration of potentially optimal solutions. As the model approaches a solution, a lower learning rate helps refine adjustments, avoiding oscillations and promoting more stable convergence. This technique is particularly relevant in contexts where data is complex and the search space is vast, as it helps prevent issues such as overfitting and divergence. There are various strategies to implement learning rate annealing, such as exponential decay, step decay, and cyclical learning rates, each with its own characteristics and benefits. In summary, learning rate annealing is an essential tool for optimizing the performance of AI models, improving their ability to learn effectively and efficiently.
