What Are Optimizers in Deep Learning?

Optimizers in deep learning are algorithms or methods used to change the attributes of the neural network such as weights and learning rate in order to reduce the losses. Optimizers help to get results faster.

Here is a step-by-step explanation:

1. Initialization: We start with a model with random weights. We input a training example, it goes through the forward propagation step and we get a prediction.

2. Loss Computation: We then compare the prediction with the actual output. The difference is calculated using a loss function. The goal of the optimization is to minimize this loss function.

3. Backpropagation: In this step, we calculate the gradient of the loss function with respect to the weights of the network. The gradient points in the direction of the greatest rate of increase of the function, and its magnitude is the rate of increase in that direction.

4. Updating the Weights: The weights of the network are then updated in the opposite direction to the gradient. This is done by subtracting the gradient of the loss with respect to the weights from the current weights. Usually, a learning rate is multiplied with the gradient to control the rate at which the weights are updated.

5. Iteration: Steps 2-4 are repeated for a number of iterations or until the loss function is minimized to a desired level.

There are several types of optimization algorithms that are used in deep learning. These include Stochastic Gradient Descent (SGD), Momentum, RMSprop, Adam, etc. Each of these optimizers have different ways of updating the weights and hence may result in different performance.