Alex Krizhevsky is a Ukrainian-born Canadian computer scientist most noted for his work on artificial neural networks and deep learning. In 2012, Krizhevsky, Ilya Sutskever and their PhD advisor Geoffrey Hinton, at the University of Toronto,[1] developed a powerful visual-recognition network AlexNet using only two GeForce NVIDIA GPU cards.[2] This revolutionized research in neural networks. Previously neural networks were trained on CPUs. The transition to GPUs opened the way to the development of advanced AI models.[2] AlexNet won the ImageNet challenge in 2012. Krizhevsky and Sutskever sold their startup, DNN Research Inc., to Google, shortly after winning the contest. Krizhevsky left Google in September 2017 after losing interest in the work, to work at the company Dessa in support of new deep-learning techniques.[1] Many of his numerous papers on machine learning and computer vision are frequently cited by other researchers.[3] He is also the main author of the CIFAR-10 and CIFAR-100 datasets.[4][5]
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AlexNet and ImageNet Explained
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[Classic] ImageNet Classification with Deep Convolutional Neural Networks (Paper Explained)
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Image retrieval using short binary codes
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References
- ^ a b Gershgorn, Dave (18 June 2018). "The inside story of how AI got good enough to dominate Silicon Valley". Quartz. Retrieved 23 February 2021.
- ^ a b Witt, Stephen (27 November 2023). "How Jensen Huang's Nvidia Is Powering the A.I. Revolution". The New Yorker. Retrieved 24 December 2023.
- ^ "Alex Krizhevsky". Google Scholar Citations.
- ^ "CIFAR-10 and CIFAR-100 datasets". Retrieved 7 March 2021.
- ^ Krizhevsky, Alex (2009), Learning multiple layers of features from tiny images (PDF), CiteSeerX 10.1.1.222.9220, S2CID 18268744
External links
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This page was last edited on 9 June 2024, at 04:45