《Deep Learning Review》.pdf

REVIEW doi:10.1038/nature14539 Deep learning 1,2 3 4,5 Yann LeCun , Yoshua Bengio Geoffrey Hinton 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 rec- ognition, 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. achine-learning technology powers many aspects of modern intricate structures in high-dimensional data and is therefore applica- society: from web searches to content filtering on social net- ble to many domains of science, business and government. In addition Mworks to recommendations on e-commerce websites, and to beating records in image recognition1–4 and speech recognition5–7, it it is increasingly present in consumer products such as cameras and has beaten other machine-learning techniques at predicting the activ- 8 9,10 smartphones. Machine