Mini toolsDefinition
A technique for dynamically adjusting the learning rate during training to improve convergence and model performance.
Detailed Explanation
Learning rate scheduling involves systematically adjusting the learning rate during training according to a predefined schedule or based on performance metrics. Common approaches include step decay, exponential decay, and cyclical learning rates. This helps optimize the training process by using larger steps early in training and smaller steps for fine-tuning.
Use Cases
Deep learning optimization, Neural network training, Model fine-tuning
