Learning Rate Decay is a technique used in training machine learning models, particularly in deep learning, to adjust the learning rate over time. The learning rate determines how much the model's weights are updated during training, and a high learning rate can lead to overshooting the optimal solution, while a low learning rate can slow down convergence. By gradually decreasing the learning rate, models can achieve better performance and stability as they approach the optimal solution. Common strategies for learning rate decay include step decay, exponential decay, and cosine annealing. This technique is widely used in various applications, from image recognition to natural language processing, to enhance model training efficiency and accuracy.
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AI Fundamentals