Mini Batch Gradient Descent is an algorithm that helps to speed up learning while dealing with a large dataset. Instead of updating the weight parameters after assessing the entire dataset, Mini ...

“The Adam algorithm combines the advantages of gradient descent and momentum methods, with the aim of adaptively adjusting the learning rate,” the scientists explained.