Which Optimizer is better than SGD?

Essentially Adam is an algorithm for gradient-based optimization of stochastic objective functions. It combines the advantages of two SGD extensions — Root Mean Square Propagation (RMSProp) and Adaptive Gradient Algorithm (AdaGrad) — and computes individual adaptive learning rates for different parameters.

Why does SGD generalize better than Adam?

By analysis, we find that compared with ADAM, SGD is more locally unstable and is more likely to converge to the minima at the flat or asymmetric basins/valleys which often have better generalization performance over other type minima. So our results can explain the better generalization performance of SGD over ADAM.

Which optimizer is based on both momentum and adaptive learning rate?

Adam. Adaptive Moment Estimation (Adam) combines ideas from both RMSProp and Momentum. It computes adaptive learning rates for each parameter and works as follows. First, it computes the exponentially weighted average of past gradients (vdW).

What is momentum optimizer?

Momentum is an extension to the gradient descent optimization algorithm that allows the search to build inertia in a direction in the search space and overcome the oscillations of noisy gradients and coast across flat spots of the search space.

What is momentum in Adam optimizer?

Momentum: This algorithm is used to accelerate the gradient descent algorithm by taking into consideration the ‘exponentially weighted average’ of the gradients. Using averages makes the algorithm converge towards the minima in a faster pace.

Which of the following SGD variants is based on both momentum and adaptive learning?

Answer: Option-C Explanation: Adam, being a popular deep learning optimizer is based on both momentum and adaptive learning.

Why is momentum used in deep learning?

A very popular technique that is used along with SGD is called Momentum. Instead of using only the gradient of the current step to guide the search, momentum also accumulates the gradient of the past steps to determine the direction to go. The equations of gradient descent are revised as follows.