person Author: Process Fellows
There are various learning algorithms that differ in their method for updating weights. However, they are mostly based on gradient descent and its variants.
Gradient descent explained simply: Imagine a mountain hike:

• You are standing on a hill (high error value)
• You want to go down into the valley (minimum error function).
• The gradient (slope) shows you in which direction you are going downhill.
• You take small steps in the direction of the steepest descent.

Examples of algorithms: Backpropagation, Stochastic Gradient Descent (SGD), Mini-Batch Gradient Descent, Adaptive Moment Estimation (Adam), etc.