Gradient descent Method in Machine Learning

Many deep learning models pick up objectives using the gradient-descent method. Gradient-descent optimization needs a big number of training samples for a model to converge. That creates it out of shape for few-shot learning. We train our models to learn to achieve a sure objective in generic deep learning models. However, humans train to learn any objective. There are different optimization methods that emphasize learn-to-learn mechanisms.