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Albert David Bangura
Graduate Teaching and Research Assistant @ Bahcesehir Cyprus University
Vectorization in Machine Learning
๐ฉ๐ฒ๐ฐ๐๐ผ๐ฟ๐ถ๐๐ฎ๐๐ถ๐ผ๐ป ๐ถ๐ป ๐ ๐ฎ๐ฐ๐ต๐ถ๐ป๐ฒ ๐๐ฒ๐ฎ๐ฟ๐ป๐ถ๐ป๐ด
In machine learning, vectorization refers to the process of converting operations into vector or matrix from.
Vectorization is a key concept for achieving high performance in machine learning algorithms, especially when dealing with large datasets.
Vectorized code can perform calculations in less time than code without vectorization.
This matters more when you are running learning algorithms on large datasets or trying to train large models which is always the case in machine learning.
Benefits of vectorized code.
1. Results in smaller code
2. Results in faster code
In sum, being able to write vectorized implementations of learning algorithms has been a key step to getting learning algorithms to run efficiently and therefore scale well to the large datasets that many machine learning algorithms now must operate on.
Below is an implementation of vectorization in Python. The vectorized version can use parallel hardware in the computer and therefore took less time when I ran the program.
Duration of Non-Vectorized Code: 598.6779 ms
Duration of Vectorized Code: 2.5864 ms
#machinelearning #bigdata #algorithms #performance #efficiency
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