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Umer-Farooq-CS / MNIST-Classification

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The MNIST classification problem is a fundamental machine learning task that involves recognizing handwritten digits (0- 9) from a dataset of 70,000 grayscale images (28x28 pixels each). It serves as a benchmark for evaluating machine learning models, particularly neural networks.

benchmarking deep-learning parallel-computing cuda mnist neural-networks high-performance-computing gpu-acceleration profiling shared-memory openacc performance-optimization c-cpp nsight tensor-cores cuda-streams pinned-memory
  • Updated Sep 12, 2025
  • Cuda

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