Скачать или смотреть Logistic regression - Vectorized computation comparison part 3

In deep learning, we usually deal with very large datasets. A non computationally optimal function can become a huge bottleneck in our algorithm and can result in a model that takes ages to run. When we are writing machine learning functions, we must be sure that our code is computationally efficient so we always use vectorization. So in this tutorial, we will compare vectorized and non vectorized mathematical computations.

You will notice that vectorized implementation is much cleaner and more efficient. For bigger vectors and matrices, the differences in running time become even bigger.

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