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The IT Law Wiki


Deep learning (also known as deep network learning or DL) is

a form of machine learning based on layered representations of variables referred to as neural networks. . . ."[1]
a general family of methods that use multi-layered neural networks; these methods have supported rapid progress on tasks once believed to be incapable of automation.[2]


"Deep learning, a class of learning procedures, has facilitated object recognition in images, video labeling, and activity recognition, and is making significant inroads into other areas of perception, such as audio, speech, and natural language processing."[3]

"DL has proved to be an effective technique for image classification, object detection, speech recognition, and natural language processing, among other application areas. Learning from data, DL can solve problems never before solvable, or with a level of performance never before achievable."[4]

"Deep learning uses structures loosely inspired by the human brain, consisting of a set of units (or 'neurons'). Each unit combines a set of input values to produce an output value, which in turn is passed on to other neurons downstream. For example, in an image recognition application, a first layer of units might combine the raw data of the image to recognize simple patterns in the image; a second layer of units might combine the results of the first layer to recognize patterns-of-patterns; a third layer might combine the results of the second layer; and so on. Deep learning networks typically use many layers — sometimes more than 100 — and often use a large number of units at each layer, to enable the recognition of extremely complex, precise patterns in data.

"In recent years, new theories of how to construct and train deep networks have emerged, as have larger, faster computer systems, enabling the use of much larger deep learning networks. The dramatic success of these very large networks at many machine learning tasks has come as a surprise to some experts, and is the main cause of the current wave of enthusiasm for machine learning among AI researchers and practitioners."[5]


See also[]

External resources[]

  • Frank Chen, "AI, Deep Learning, and Machine Learning: A Primer," Andreessen Horowitz (June 10, 2016) (full-text).