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In deep learning, tensors represent everything from input data (images, sound waves, text tokens) to the learned parameters that define a neural network’s knowledge. The computations that update these tensors—multiplying and summing enormous arrays of numbers—are extraordinarily intensive. Standard CPUs, optimized for sequential tasks ...
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