Explain optimizers in Keras. Name a few commonly used optimizers.

In Keras, optimizers are algorithms used to update the parameters (weights) of a neural network model during training in order to minimize the loss function. The goal of optimization is to find the set of model parameters that minimize the difference between the predicted outputs of the model and the true labels or targets.

Optimizers work by iteratively adjusting the parameters in the direction that reduces the loss, typically using some form of gradient descent. Different optimizers employ various strategies for updating the parameters, such as momentum, adaptive learning rates, and second-order methods.

Here are a few commonly used optimizers in Keras :

* Stochastic Gradient Descent (SGD)
* Adam
* RMSprop
* Adagrad
* Adamax
* Nadam