Learning Rate Decay

Learning rate decay is a strategy where the learning rate is gradually reduced as the training of a neural network progresses. This allows the model to take large steps early on to find the general region of the optimal solution and smaller steps later to fine-tune the weights.

In the context of quantitative finance, this is essential for achieving high precision in pricing models. If the learning rate remains too high, the model may oscillate around the minimum and fail to converge to the most accurate values.

By systematically reducing the rate, the model can settle into the precise minimum, leading to better predictive accuracy. This is a common practice when training deep learning models on large datasets of historical market data.

It ensures that the model is both efficient and accurate, balancing the need for speed with the requirement for precision. Proper decay schedules are vital for the successful training of any high-performance financial algorithm.