Réseau de neurones dynamique

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Dynamic Neural Network

Dynamic neural networks address nonlinear multivariate behaviour and include (learning of) time-dependent behaviour, such as transient phenomena and delay effects. Techniques to estimate a system process from observed data fall under the general category of system identification.

Neural network uses hidden layers to break down information (the input-images, audio, videos, handwritten text…) into tiny pieces of easily understandable components, allowing a computer to inform a prediction about the nature of said input. It does this thanks to a wide array of training data and mathematical models. In doing so, it works “similarly” to our brain, hence the technology’s name. This is far from new, but the world of data science has been on the lookout for faster and more efficient ways of using neural networks to serve the upcoming IoT revolution.

Source : Wikipedia