{"id":7008,"date":"2017-02-02T07:00:14","date_gmt":"2017-02-02T15:00:14","guid":{"rendered":"http:\/\/formtek.com\/blog\/?p=7008"},"modified":"2016-12-22T14:58:52","modified_gmt":"2016-12-22T22:58:52","slug":"deep-learning-finding-patterns-in-data-with-artificial-intelligence","status":"publish","type":"post","link":"https:\/\/formtek.com\/blog\/deep-learning-finding-patterns-in-data-with-artificial-intelligence\/","title":{"rendered":"Deep Learning: Finding Patterns in Data with Artificial Intelligence"},"content":{"rendered":"

The Deep Learning<\/a> market is growing at an annual rate of 65 percent annually and is expected to reach $1.8 billion by 2022 according to a report by Research and Markets<\/a>.<\/p>\n

Deep Learning is a kind of machine learning, but it is more super charged than just machine learning.\u00a0 Deep learning software algorithms derive insight by processing massive amounts of data, like images, video, audio or text.\u00a0 With standard machine learning, an analyst or programmer would first create a model that explicitly tells the computer what to look for in the data, but with deep learning, instead the software sifts through the data and tries to identify patterns on its own.<\/p>\n

Businesses are beginning to apply deep learning techniques to areas like customer churn prediction, financial fraud detection and product recommendation.<\/p>\n

George E. Dahl<\/a>, member of the Google Brain team, said that<\/a> “a lot of successful machine learning applications depend on hand-engineering features where the researcher manually encodes relevant information about the task at hand and then there is learning on top of that. The difference between that and deep learning is that the deep learning researcher will try and get the system to engineer its own features as much as is feasible.”<\/p>\n

Yoshua Bengio<\/a>, Computer Science professor at the University of Montreal, said that<\/a> “what the computer is learning using deep learning algorithms are more abstract representations of concepts. Deep learning comes from the notion that as humans we have multiple types of representation\u2014with simpler features at the lower levels and high-level abstractions built on top of that. By representing information in this more abstract way, the machine can generalize more easily.”<\/p>\n

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The Deep Learning market is growing at an annual rate of 65 percent annually and is expected to reach $1.8 billion by 2022 according to a report by Research and Markets. Deep Learning is a kind of machine learning, but…<\/span><\/p>\n

Read more ›<\/a><\/div>\n

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