Deep learning
Yann LeCun, Yoshua Bengio & Geoffrey Hinton
http://www.nature.com/nature/journal/v521/n7553/full/nature14539.html
#Deep_learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. These methods have dramatically improved the state-of-the-art in speech recognition, visual object #recognition, object detection and many other domains such as drug discovery and genomics. Deep learning discovers intricate structure in large data sets by using the backpropagation algorithm to indicate how a machine should change its internal parameters that are used to compute the representation in each layer from the representation in the previous layer. Deep #convolutional nets have brought about breakthroughs in processing #images, #video, #speech and #audio, whereas #recurrent nets have shone light on sequential data such as #text and speech.
Yann LeCun, Yoshua Bengio & Geoffrey Hinton
http://www.nature.com/nature/journal/v521/n7553/full/nature14539.html
#Deep_learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. These methods have dramatically improved the state-of-the-art in speech recognition, visual object #recognition, object detection and many other domains such as drug discovery and genomics. Deep learning discovers intricate structure in large data sets by using the backpropagation algorithm to indicate how a machine should change its internal parameters that are used to compute the representation in each layer from the representation in the previous layer. Deep #convolutional nets have brought about breakthroughs in processing #images, #video, #speech and #audio, whereas #recurrent nets have shone light on sequential data such as #text and speech.