Gradient clipping originates from the fields of artificial intelligence and big data. It is a technique that helps in the training of so-called neural networks \u2013 these are learning computer programmes that can, for instance, recognise speech or analyse images.<\/p>\n
During the training of these networks, mathematical calculations are constantly performed, which results in so-called \u201egradients\u201c. However, these sometimes become far too large. This can make training unstable and lead to the network not learning properly. Gradient clipping simply cuts off these excessively large values, thus keeping the learning process within a controlled framework.<\/p>\n
Imagine you're training a sports team and someone suddenly sprints off extremely fast. The whole team gets thrown off as a result. With gradient clipping, you'd say, \u201eNo faster than this maximum speed!\u201c so that everyone can train together more effectively.<\/p>\n
These methods make the training of Artificial Intelligence more reliable and help to achieve usable results faster. Therefore, gradient clipping is particularly important for many modern applications, such as voice assistants or image recognition software.<\/p>","protected":false},"excerpt":{"rendered":"