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What is Vectorization in Machine Learning?
Make your code execute fast using vectorization
What you’ll learn :
- What is Vectorization?
- How Vectorization is important in Machine learning?
- Example: Unvectorized Vs Vectorized Implementation
- Advantages of Vectorized Implementation
- Demonstration on jupyter notebook
The first time when I learned about the concept of Vectorization it was when I was learning the most well-known course Machine learning by Prof Andrew Ng on the Coursera platform.
According to Prof Andrew Ng :
“ The ability to perform Vectorization has become a key skill “
So let’s learn this skill and include it into our tools box :)
What is Vectorization?
Vectorization is a technique by which you can make your code execute fast. It is a very interesting and important way to optimize algorithms when you are implementing it from scratch.
Now, with the help of highly optimized numerical linear algebra libraries in C/C++, Octave/Matlab, Python, …etc. We can make our code run efficiently.