Specifically, you learned: Adam is a replacement optimization algorithm for stochastic gradient descent

for training deep learning models. Adam combines the best properties of the AdaGrad and RMSProp algorithms to provide an optimization algorithm that can handle sparse gradients on noisy problems.

What is the use of Optimizer?

Optimizers are algorithms or methods used to change the attributes of the neural network such as weights and learning rate to reduce the losses. Optimizers are used to solve optimization problems by minimizing the function.

Is Adam the best optimizer?

Adam is the best among the adaptive optimizers in most of the cases. Good with sparse data: the adaptive learning rate is perfect for this type of datasets.