What are optimizers and how does learning rate affect them?

What are optimizers in machine learning?

Optimizers are algorithms that adjust the weights of a neural network to minimize the loss function during training.

How does the learning rate impact the optimization process?

The learning rate determines the size of the steps taken towards the minimum of the loss function; a small rate may slow convergence, while a large rate can overshoot.

What happens if the learning rate is too high?

If the learning rate is too high, the model may diverge and fail to converge to a minimum, resulting in poor performance.

Can you explain different types of optimizers?

Sure! Common optimizers include SGD, Adam, and RMSprop, each with unique strategies for updating weights.