In neural networks, we use gradient descent optimization algorithm to minimize the error function to reach a global minima. In an ideal world the error function would look like this

So you are guaranteed to find the global optimum because there are no local minimum where your optimization can get stuck. However in real the error surface is more complex, may comprise of several local minima and may look like this

In this case, you can easily get stuck in a local minima and the algorithm may think you reach the global minima leading to sub-optimal results. To avoid this situation, we use a momentum term in the objective function, which is a value between 0 and 1 that increases the size of the steps taken towards the minimum by trying to jump from a local minima. If the momentum term is large then the learning rate should be kept smaller. A large value of momentum also means that the convergence will happen fast. But if both the momentum and learning rate are kept at large values, then you might skip the minimum with a huge step. A small value of momentum cannot reliably avoid local minima, and can also slow down the training of the system. Momentum also helps in smoothing out the variations, if the gradient keeps changing direction. A right value of momentum can be either learned by hit and trial or through cross-validation.

Pictures Source – Momentum and Learning Rate Adaptation