Over 137,000 libraries exist in Python’s repository. So, how do you choose the right one for your machine learning project? A cheat sheet on proven uses can help.

Nothing beats Python in finding solutions to complex mathematical and computational problems. It is a versatile language that can be easily used across domains and is easier to debug too.

45% of tech organizations use Python for their machine learning and AI projects. – Builtwith.com

Python libraries are a work in progress and their use cases and toolkits are continuously advancing. Therefore, AI engineers need to keep a constant tab on the latest developments, more so if they intend to use Python for their machine learning projects.

Before we begin with the cheat sheet, please note that Python libraries can be multi-purpose and can be placed in multiple categories. Also, the use of libraries is not constrained to the highlighted tasks.

This cheat sheet aims to help you dig out the best fit.

Top Python Libraries for Deep Learning

We’ve weighed the pros and cons and arrived at these four top picks.

TensorFlow

A plug-and-play library with an extensive resource of commonly-used machine learning models and algorithms, TensorFlow comes with facial recognition capabilities. It is one of the biggest open-source Python libraries and a must-have for beginners.

Level: Good for beginners

Keras

It runs on top of libraries like CNTK, Theano, and TensorFlow, and offers specific support to deep learning applications. Easier prototyping, modularity, and a user-friendly interface make it an excellent choice for beginners.

Level: Excellent choice for beginners

https://www.artiba.org/blog/a-cheatsheet-to-find-the-best-fit-python-library