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.

CUDA C++ practice project for RTX 4070 SUPER — explore GPU concurrency, pinned memory, and Nsight profiling. Includes SAXPY and 2D blur kernels to train optimization, stream overlap, and timing analysis for NVIDIA Developer Technology Engineering skillset.

A CUDA C++ demo showing how to overlap data transfer and kernel execution using multiple streams and pinned (page-locked) host memory. This project illustrates asynchronous memcpy, event timing, and performance benefits of concurrent GPU execution — essential for building high-throughput pipelines.