Optimizers in deep learning are algorithms that adjust the parameters of a neural network to minimize the loss function during training. They determine how the model learns by updating the weights and biases based on the gradients of the loss function. Popular optimizers include Stochastic Gradient Descent (SGD), which updates parameters with a fixed learning rate, and more advanced methods like Adam, RMSprop, and Adagrad, which adaptively adjust the learning rate based on past gradients. Optimizers play a crucial role in finding the optimal set of parameters and affect the training speed, convergence, and generalization performance of the model.