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All The NumPy You Need To Supercharge Your Deep Learning Code

7 min readNov 10, 2020

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NumPy or Numerical Python is an open-source Python library that makes it easy to complex numerical operations. Working with machine learning and deep learning applications involve complex numerical operations with large datasets. NumPy makes implementing these operations relatively simple and effective when compared to their pure Python implementation.

At its core, NumPy implements its (n-dimensional array) data structure, which is similar to a regular Python list. Most programming languages only have the concept of arrays. Python implements lists, which work as arrays, but there are differences.

One key difference between regular python lists and NumPy is that python lists can contain various data types. In contrast, a NumPy array can only contain objects of the same data type. In other words, the data structures are homogenous. While this may seem like a negative, it allows NumPy operations to be faster as they can avoid conversions and constraints while doing computations.

Why should you care about NumPy, and why specifically for deep learning?

I will cover several specific use cases in the post, but one of the most crucial features of NumPy as compared to other python data structures is speed. NumPy is orders of…

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TDS Archive
TDS Archive

Published in TDS Archive

An archive of data science, data analytics, data engineering, machine learning, and artificial intelligence writing from the former Towards Data Science Medium publication.

Sam Palani
Sam Palani

Written by Sam Palani

Machine Learning & AI Specialist @ AWS. ❤ = Travel, Books & Jazz. {samx18 @ most places online} Views are my own.

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