Today we take a look at some of the top Python libraries for deep learning, natural language processing, and computer vision.
We did our best to categorize each library by expected usage, hope this helps.
Obviously, not all natural language processing and computer vision work is done using deep learning techniques these days, but the trend is moving in the direction of this technique.
All included libraries have corresponding Github code repositories, and we also list the Github collections (Stars), commits (Commits), and contributors (Contributors) data of each library, which reflects the library to a certain extent popularity and usage.
Let’s take a look at the 30 top Python libraries for deep learning, natural language processing, and computer vision put together by the KDnuggets staff.
deep learning
1. TensorFlow
Collection: 149000, Commits: 97741, Contributors: 2754
TensorFlow is an end-to-end open source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries, and community resources that allow researchers to push the state-of-the-art in machine learning and developers to easily build and deploy machine learning-driven applications.
2. Hard
Collections: 50000, Commits: 5349, Contributors: 864
Keras is a deep learning API written in Python that runs on top of the machine learning platform TensorFlow.
3. PyTorch
Collection: 43200, Commits: 30696, Contributors: 1619
Tensors and dynamic neural networks in Python with powerful GPU acceleration
4. employment
Collection: 19800, Commits: 1450, Contributors: 607
fastai simplifies fast, accurate neural network training using modern best practices.
5. PyTorch Lightning
Collections: 9600, Commits: 3594, Contributors: 317
A lightweight PyTorch wrapper for high-performance artificial intelligence research.
6. JAX
Collections: 10000, Commits: 5708, Contributors: 221
Composable transformation of Python+NumPy programs: diff, vectorize, JIT to GPU/TPU, etc.
7. MXNet
Collection: 19100, Commits: 11387, Contributors: 839
Lightweight, portable, flexible distributed, mobile deep learning, data flow scheduler with dynamic and mutation awareness; suitable for Python, R, Julia, Scala, Go, Javascript, etc.
8. Ignite
Collections: 3100, Commits: 747, Contributors: 112
High-level library to help neural networks in PyTorch be trained and evaluated flexibly and transparently.
Natural Language Processing (NLP)
9. FastText
Collections: 21700, Commits: 379, Contributors: 47
FastText is a library for efficiently learning word representations and sentence classification.
10. spaCy
Collections: 17400, Commits: 11628, Contributors: 482
Powerful Natural Language Processing using Python and Cython.
11. gensim
Collections: 11200, Commits: 4024, Contributors: 361
Python library for topic modeling, document indexing, and similarity retrieval over large corpora. The target audience is the natural language processing and information retrieval communities.
12. NLTK
Collections: 9300, Commits: 13990, Contributors: 319
Open source Python modules, datasets, and tutorials to support research and development in natural language processing.
13. Datasets (Huggingface开发)
Collections: 4300, Commits: 568, Contributors: 64
Provides fast, efficient, open datasets and evaluation metrics for natural language processing and more in PyTorch, TensorFlow, NumPy, and Pandas.
14. Tokenizers (Huggingface development)
Collections: 3800, Commits: 1252, Contributors: 30
State-of-the-art fast marker optimized for research and production
15. Transformers (Huggingface development)
Collections: 3500, Commits: 5480, Contributors: 585
State-of-the-art Natural Language Processing for Pytorch and TensorFlow 2.0.
16. Stanza
Collections: 4800, Commits: 1514, Contributors: 19
The official Stanford Natural Language Python library for many human languages
17. TextBlob
Collections: 7300, Commits: 542, Contributors: 24
Simple, Pythonic, with text processing—sentiment analysis, part-of-speech tagging, noun phrase extraction, translation, and more.
18. PyTorch NLP
Collections: 1800, Commits: 442, Contributors: 15
Basic tools for PyTorch natural language processing
19. Textacy
Collections: 1500, Commits: 1324, Contributors: 23
A Python library for performing various natural language processing tasks, built on the high-performance spaCy library.
20. Finetune
Collections: 626, Commits: 1405, Contributors: 13
Allows users to leverage state-of-the-art pre-trained natural language processing models for a variety of downstream tasks.
21. TextHero
Collection: 1900, Commits: 266, Contributors: 17
From scratch, quantities are used for text preprocessing, representation and visualization.
22. Spark NLP
Collections: 1700, Commits: 4363, Contributors: 50
Spark NLP is a natural language processing library built on top of Apache Spark ML.
23. GluonNLP
Collections: 2200, Commits: 712, Contributors: 72
GluonNLP is a toolkit that enables easy text preprocessing, dataset loading, and neural model building to help you accelerate your natural language processing (NLP) research.
computer vision
24. Pillow
Collections: 7800, Commits: 10799, Contributors: 303
Pillow is a nice fork of the Python imaging library.
25. OpenCV
Collections: 49600, Commits: 29453, Contributors: 1234
Open source computer vision library
26. scikit-image
Collections: 4000, Commits: 12352, Contributors: 403
Image Processing in Python
27. Mahotas
Favorites: 644, Commits: 1273, Contributors: 25
A library of fast computer vision algorithms (all implemented in C++ for speed) that operates on numpy arrays.
28. Simple-CV
Collections: 2400, Commits: 2625, Contributors: 69
Open source machine vision framework, using OpenCV and Python programming languages.
29. GluonCV
Collections: 4300, Commits: 774, Contributors: 101
Provides implementations of state-of-the-art (SOTA) deep learning models in computer vision.
30. Torchvision
Collections: 7500, Commits: 1286, Contributors: 334
Packages include popular datasets, model architectures, and common image transformations for computer vision.
Conclusion:
These are 30 top Python libraries for deep learning, natural language processing, and computer vision that you should know about. Hope they will help you.
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