AlexNet

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Apprentissage profond

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AlexNet

AlexNet est le nom de l'architecture d'un réseau profond de neurones à convolutions qui a remporté l'épreuve ImageNet en 2012. Conçu par une équipe de l'Université de Toronto dirigée par Geoffrey Hinton, dont faisaient partie Alex Krizhevsky et Ilya Sutskever, AlexNet a marqué un point tournant dans l'emploi des réseaux profonds de neurones.

Anglais

Alexnet

Alexnet is the name of the Convolutional Neural Network architecture that won the ILSVRC 2012 competition by a large margin and was responsible for a resurgence of interest in CNNs for Image Recognition. It consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax. Alexnet was introduced in ImageNet Classification with Deep Convolutional Neural Networks. Autoencoder

An Autoencoder is a Neural Network model whose goal is to predict the input itself, typically through a “bottleneck” somewhere in the network. By introducing a bottleneck, we force the network to learn a lower-dimensional representation of the input, effectively compressing the input into a good representation. Autoencoders are related to PCA and other dimensionality reduction techniques, but can learn more complex mappings due to their nonlinear nature. A wide range of autoencoder architectures exist, including Denoising Autoencoders, Variational Autoencoders, or Sequence Autoencoders.