Tutorial 14- Stochastic Gradient Descent with Momentum

In this post I’ll talk about simple addition to classic SGD algorithm, called momentum which almost always works better and faster than Stochastic Gradient Descent. Momentum or SGD with momentum is method which helps accelerate gradients vectors in the right directions, thus leading to faster converging. It is one of the most popular optimization algorithms and many state-of-the-art models are trained using it. Before jumping over to the update equations of the algorithm, let’s look at some math that underlies the work of momentum.

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