{"id":3359,"date":"2024-09-01T13:08:02","date_gmt":"2024-09-01T13:08:02","guid":{"rendered":"https:\/\/workhouse.sweetdishy.com\/?p=3359"},"modified":"2024-09-01T13:08:02","modified_gmt":"2024-09-01T13:08:02","slug":"neural-networks-and-deep-learning","status":"publish","type":"post","link":"https:\/\/workhouse.sweetdishy.com\/index.php\/2024\/09\/01\/neural-networks-and-deep-learning\/","title":{"rendered":"Neural Networks and Deep Learning"},"content":{"rendered":"\n

As a teen in the 1950s, Geoffrey Hinton wanted to be a professor and to study AI. He came from a family of noted academics (his great-great-grandfather was George Boole). His mom would often say, \u201cBe an academic or be a failure.\u201d10<\/a><\/sup><\/p>\n\n\n\n

Even during the first AI winter, Hinton was passionate about AI and was convinced that Rosenblatt\u2019s neural network approach was the right path. So in 1972, he received his PhD on the topic from the University of Edinburgh.<\/p>\n\n\n\n

But during this period, many people thought that Hinton was wasting his time and talents. AI was essentially considered a fringe area. It wasn\u2019t even thought of as a science.<\/p>\n\n\n\n

But this only encouraged Hinton more. He relished his position as an outsider and knew that his ideas would win out in the end.<\/p>\n\n\n\n

Hinton realized that the biggest hindrance to AI was computer power. But he also saw that time was on his side. Moore\u2019s Law predicted that the number of components on a chip would double about every 18 months.<\/p>\n\n\n\n

In the meantime, Hinton worked tirelessly on developing the core theories of neural networks\u2014something that eventually became known as deep learning. In 1986, he wrote\u2014along with David Rumelhart and Ronald J. Williams\u2014a pathbreaking paper, called \u201cLearning Representations by Back-propagating Errors.\u201d It set forth key processes for using backpropagation in neural networks. The result was that there would be significant improvement in accuracy, such as with predictions and visual recognition.<\/p>\n\n\n\n

Of course, this did not happen in isolation. Hinton\u2019s pioneering work was based on the achievements of other researchers who also were believers in neural networks. And his own research spurred a flurry of other major achievements:<\/p>\n\n\n\n