AlexNet - Wikipedia

AlexNet is a convolutional neural network architecture developed for image classification tasks, notably achieving prominence through its performance in the ImageNet Large Scale Visual Recognition Challenge (ILSVRC). It classifies images into 1,000 distinct object categories and is regarded as the first widely recognized application of deep convolutional networks in large-scale visual recognition. Developed in 2012 by Alex Krizhevsky in collaboration with Ilya Sutskever and his Ph.D. advisor Geoffrey Hinton at the University of Toronto, the model contains 60 million parameters and 650,000 neurons.[1] The original paper's primary result was that the depth of the model was essential for its high performance, which was computationally expensive, but made feasible due to the utilization of graphics processing units (GPUs) during training.[1] The three formed team SuperVision and submitted AlexNet in the ImageNet Large Scale Visual Recognition Challenge on September 30, 2012.[2] The network

AlexNet - Wikipedia Jump to content From Wikipedia, the free encyclopedia Influential 2012 deep convolutional neural network AlexNet Developers Alex Krizhevsky , Ilya Sutskever , and Geoffrey Hinton Release September 30, 2012 ; 13 years ago ( 2012-09-30 ) Written in CUDA , C++ Type Convolutional neural network License New BSD License Repository code .google .com /archive /p /cuda-convnet / AlexNet architecture and a possible modification. At the top is half of the original AlexNet, which is divided into two halves, one for each GPU. At the bottom is the same architecture, but the final "projec

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