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I don't think this is very true. You can trace the deep learning revolution back to VGG and a fundamental driver in the success of the first multi-level networks was the ability to train in semi-reasonable amounts of time using GPUs.

Even today distributed training is relatively uncommon while pretty much everyone uses NVIDIA GPUs.

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VGG was a long time after the first deep learning revolution.

You could say it started with ImageNet 2012 where AlexNet showed that deep networks were a newly promising area of study - however the actual performance of AlexNet was very very far from human performance. I tend to say the revolution started at ImageNet 2014 with VGG/GoogLeNet, the first human-caliber performances. Or you could say it was ImageNet 2015, when the first ResNet had better-than-human performance.

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