An overview of gradient descent optimization algorithms


Note: If you are looking for a review paper, this blog post is also available as an article on arXiv. Added derivations of AdaMax and Nadam. Gradient descent is one of the most popular algorithms to perform optimization and by far the most common way to optimize neural networks. At the same time, every state-of-the-art Deep Learning library contains implementations of various algorithms to optimize gradient descent (e.g. These algorithms, however, are often used as black-box optimizers, as practical explanations of their strengths and weaknesses are hard to come by.