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How binarized networks work — and why they’ll be big for AI in 2020


The concept of neural networks first emerged more than 40 years ago when scientists experimented with mathematically modelling the functions of the brain. They worked out they could make a mechanical implementation of the neural network that could be trained to recognize patterns and classify data — for example recognizing whether a video contains a cat or a dog. Over the past decade, the complexity and capacity of neural networks has increased sharply. Coinciding with the extraordinary growth of cheap and easily accessible heavy-duty supercomputers and graphics processing units (GPUs), they have come to the fore as the de facto…

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