Numpy is so far the most used library for performing mathematical operations on arrays. It has formed the base for many Machine learning and data science libraries. It has a large number of high-level mathematical functions to operate on arrays. As we all know, Numpy gained popularity because of its speed of operations. Numpy array objects work almost 50x faster than the python lists. Also, NumPy arrays support vectorization which removes the loops in python.
Can we run numpy operations even faster? The answer is Yes!
Tensorflow has implemented a subset of Numpy API and released it as part of 2.4 version, as tf.experimental.numpy. This allows running NumPy code much faster and can further improve the performance by running on GPU/TPU.
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Numpy is by far the most used library for performing math operations on arrays. So you know run your numpy operations on GPU using Tensorflow Numpy API?