In this chapter, we’ll go over some other optimization techniques for improving the ability of a neural network to learn complex patterns in a dataset.
Momentum
Momentum is a technique for improving the convergence speed of stochastic gradient descent (SGD) optimization. Remember that stochastic gradient works by learning the direction of steepest descent by evaluating a training example at each time step to optimize the weights of the network. Momentum improves on this by calculating the average of previous gradients in a process ...