Libraries-list of python libraries for deep learning

By | August 29, 2018

Quick Install: List of Useful Python Libraries for Deep Learning

Here is a list of Python libraries with installation commands that are very useful if you are working in deep learning, AI, Computer Vision. Run these commands in bash or shell.

Note: If you are using Python virtualenv, then do not forget to activate virtualenv before running them.

tensorflow

Tensorflow is one of the most widely used libraries in deep learning. For GPU install and other details, please check official website here.

keras

Keras is one of my favourite deep learning libraries. It is super simple to use and runs on top of tensorflow or theano. For more details, visit keras website here.

deep learning libraries

h5py

This library helps to deal with saving and loading operations of keras model.

jupyter

Jupyter is one of the best tools for data science and deep learning. One of my favourite. You can write math operations and see instant output inline. You can write python code line by line and even visualize your arrays. There are plenty of things you can do with jupyter like draw graphs, print arrays, draw images etc. It is great for programming and visualisation. I highly recommend this.

matplotlib

It is used to plot graphs. It is a must needed.

numpy

Numpy is very popular python library for mathematical operations. In deep learning, we mostly use it for matrix operation.

SciPy

It is used for mathematical and scientific operations with python.

scikit-learn

PIL

Pillow is used for image processing.

bcolz

You can read about bcolz here.

opencv

OpenCV is one of the leading computer vision and image processing library. You must need this if you are working in deep learning for computer vision based projects.

Apart from this, there are other useful libraries like;

graphviz

sklearn-pandas

isoweek

tqdm

pandas

cython

lxml