Vectorization in Python. Today we will be looking into an amazing concept of what exactly is vectorization in python. Is it really important?

Hello readers, today we will be looking into an amazing concept of what exactly is Vectorization in python. If you ask me, I would love to say, vectorization is an art. Yes, it’s the art of avoiding explicit folders from your code. Of course, you can use this in any of your coding works. But, particularly in deep learning, where you will work with tons of data, your code must execute faster than ever. So, you will be using loops right? if so, you got good news. You need not use loops explicitly to get into your data. Instead, you can vectorize the data points for faster execution. Let’s see how this works.

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Vectorization in Python We are going to understand Vectorizations in the context of logistic regression. It is used to speed up the code without explicitly using it for loops. This not only makes execution faster but also reduces errors and produces a neat code that will be easier to read.

Numpy which is a python library widely used for the numerical computations. This library will help us in vectorization. There will be two approaches –

Non-vectorized approach Vectorized approach Let’s understand about the math behind both as well their implementation.

Learn numpy features to see why you should use numpy - high performance, multidimensional container, broadcasting functions, working with varied databases

Learn the uses of numpy - Alternate for lists in python, multi dimensional array, mathematical operations. See numpy applications with python libraries.

Learn NumPy Copy and View - Deep Copy, shallow copy and No copy in NumPy, NumPy view creation and types with examples, NumPy View vs Copy

Python is an open-source object-oriented language. It has many features of which one is the wide range of external packages. There are a lot of packages for installation and use for expanding functionalities. These packages are a repository of functions in python script. NumPy is one such package to ease array computations.

In this post, we'll learn linear Algebra for Data Scientists with NumPy