Pushing Python toward C speeds with SIMD.
In fields like data science and image processing, running time-intensive computations in a for loop iterating over an array is essential, and in a language like Python, receiving a runtime error due to some array shape mismatch hours into a computation is inexcusable, though all too commonplace. Speeding up array computations in Python code can always provide huge benefits in both research and industry, so I was thrilled to learn about Numba, a JIT compiler for Python code with a focus on optimizing NumPy.

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