The learning rate is a hyperparameter in machine learning that controls how much the model's parameters are updated in each iteration. A higher learning rate means that the model's parameters will be updated more aggressively, while a lower learning rate means that the model's parameters will be updated more slowly.
The learning rate is a trade-off between convergence speed and stability. A high learning rate can lead to faster convergence, but it can also make the model more unstable and prone to overfitting. A low learning rate can lead to slower convergence, but it can also make the model more stable and less prone to overfitting.
The optimal learning rate depends on the specific problem and dataset being used. There is no one-size-fits-all answer. However, a good starting point is to use a learning rate that is inversely proportional to the number of parameters in the model.
Here is a simple analogy to help you understand the learning rate: Imagine that you are training a model to walk. You start by giving the model a small push. The model will then take a step forward. You then give the model another small push, and so on. The amount of force you use to push the model is analogous to the learning rate.
- If you use a very high learning rate, you may end up pushing the model too far forward, and it may fall over. This is analogous to the model overfitting the data.
- If you use a very low learning rate, you may end up pushing the model too slowly, and it may never learn to walk. This is analogous to the model not converging.
The optimal learning rate is the one that allows the model to learn the data without overfitting or not converging.