Why is the learning rate important in machine learning?

Why is the learning rate important in machine learning?

The learning rate determines how much to change the model in response to the estimated error each time the model weights are updated. A proper learning rate helps in converging to the optimal solution.

What happens if the learning rate is too high?

If the learning rate is too high, the model may converge too quickly to a suboptimal solution or even diverge, leading to erratic behavior.

And what if it's too low?

A learning rate that is too low can result in a very slow convergence, making the training process inefficient and potentially getting stuck in local minima.

How do I choose the right learning rate?

You can start with common values like 0.01 or 0.001 and use techniques like learning rate schedules or adaptive learning rates to find the best fit.

Are there any tools to help with this?

Yes, libraries like TensorFlow and PyTorch offer built-in functions to adjust learning rates dynamically during training.