Stochastic gradient descent methods are at home in the fields of artificial intelligence, big data and smart data, as well as digital transformation. This term describes a method by which computers can learn quickly and efficiently from large amounts of data.<\/p>\n
Imagine a computer is supposed to recognise whether a cat or a dog is visible in a photograph. For the computer to learn this, it must analyse many examples and improve its \u201edecision\u201c step by step. Stochastic gradient descent helps with this: it doesn't always take all available data at once but looks at randomly selected examples. This saves the computer a lot of time and processing power.<\/p>\n
A simple example: imagine a large box full of letters that need to be sorted into \u201eimportant\u201c and \u201enot important\u201c. Instead of checking all the letters at once, an employee just looks at a few randomly picked letters, learns from them, and adjusts their sorting method. Over many repetitions, they get better and better without ever having to see every single letter.<\/p>\n
In AI and Big Data, the stochastic gradient descent method makes it possible to process huge datasets quickly and draw conclusions from them.<\/p>","protected":false},"excerpt":{"rendered":"