MACHINE LEARNING

Detailed explanation-1: -Adam optimizer is the extended version of stochastic gradient descent which could be implemented in various deep learning applications such as computer vision and natural language processing in the future years.

Detailed explanation-2: -Adam optimization is a stochastic gradient descent method that is based on adaptive estimation of first-order and second-order moments.

Detailed explanation-3: -An optimal learning rate value (default value 0.001) means that the optimizer would update the parameters just right to reach the local minima. Varying learning rate between 0.0001 and 0.01 is considered optimal in most of the cases.

Detailed explanation-4: -In deep learning, optimizers are used to adjust the parameters for a model. The purpose of an optimizer is to adjust model weights to maximize a loss function. The loss function is used as a way to measure how well the model is performing. An optimizer must be used when training a neural network model.