Mixed-Precision combines different numerical precisions in a computational method. Using precision lower than FP32 reduces memory usage, allowing deployment of larger neural networks. Data transfers take less time, and compute performance increases, especially on NVIDIA GPUs with Tensor Core support for that precision. Mixed-precision training of DNNs achieves two main objectives: This video��
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