Deep Learning Optimizer Visualization

Countless machine learning techniques depend on the optimization of a given objective function; for instance, classifiers such as logistic regression, metric learning methods like NCA, manifold learning algorithms like MVU, and the extremely popular field of deep learning. Similarly, the need is also real for a flexible framework that allows new objective functions to be easily implemented and optimized with a variety of possible optimizers. Which makes it easy to combine nearly any type of optimizer with nearly any type of objective function, and has allowed us to minimize the effort necessary to both implement new optimizers and to implement new machine learning algorithms that depend on optimization.

Source: vis.ensmallen.org