<ul><li>Momentum optimization is a variant of gradient descent that helps overcome issues with slow convergence and local minima.</li><li>Adagrad adapts the learning rate for each parameter based on its frequency of updates, making it useful for dealing with sparse data.</li><li>NAG is an extension of momentum optimization that calculates the gradient ahead of time, improving convergence in certain scenarios.</li><li>RMSprop adjusts the learning rate for each parameter based on the recent average of squared gradients, making it effective for handling non-stationary problems.</li><li>Adam combines the ideas of momentum and RMSprop, maintaining an exponentially decaying average of past gradients and squared gradients, making it widely used for deep learning.</li></ul>

Mastering Model Training: A Deep Dive into the Top 5 Optimizers for Machine Learning

Discover more