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      Silicon Photonic Modulator Neuron

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          Deep learning.

          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.
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            Gradient-based learning applied to document recognition

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              Support-vector networks

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                Author and article information

                Journal
                PRAHB2
                Physical Review Applied
                Phys. Rev. Applied
                American Physical Society (APS)
                2331-7019
                June 2019
                June 18 2019
                : 11
                : 6
                Article
                10.1103/PhysRevApplied.11.064043
                1a2b0700-f917-4728-a988-adc8348b3132
                © 2019

                https://link.aps.org/licenses/aps-default-license

                History

                Quantitative & Systems biology,Biophysics
                Quantitative & Systems biology, Biophysics

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