Top 10 Open Source Python AI Projects Aspirants Should Try in 2022


by Satavisa Pati

February 19, 2022

If you want to learn Python, here are the 10 best Python AI open source projects to try in 2022

Working as a data scientist or data engineer, Python is a go-to programming language. There is perhaps no better way to learn Python than working on open source projects. This will help you to master the language better. Here are the 10 best Python AI open source projects to try in 2022.


Theano allows you to optimize, evaluate and define mathematical expressions that involve multidimensional arrays. It’s a Python Library and has many features that make it a must-have for any machine learning professional. It is optimized for stability and speed and can generate dynamic C code to quickly evaluate expressions. Theano also allows you to use NumPy.ndarray in its functions, allowing you to effectively use NumPy’s capabilities.


Scikit-learn is a Python-based tool library that you can use for data analysis and exploration. You can reuse it in many contexts. It has excellent accessibility, so its use is also quite easy. Its developers built it on matplotlib, NumPy and SciPy. Some tasks you can use Scikit-learn for include clustering, regression, classification, model selection, preprocessing, and dimensionality reduction. To become a true AI professional, you must be able to use this library.


Chainer is a Python-based framework for working on neural networks. It supports multiple network architectures, including recurrent networks, convnets, recursive networks, and feedforward networks. Other than that, it allows CUDA computing so you can use a GPU with very few lines of code. You can also run Chainer on many GPUs if needed. A significant advantage of Chainer is that it makes it easier to debug code, so you won’t have to put much effort in this regard. On Github, Chainer has over 12,000 commits, so you can see how popular it is.


Caffe is a product of Berkeley AI Research and is a deep learning framework that focuses on modularity, speed, and expression. It is among the most popular open source AI projects in Python. It has excellent architecture and speed as it can process more than 60 million images in a day. Moreover, it has a thriving community of developers who use it for industrial applications, academic research, multimedia, and many other fields.


Gensim is an open-source Python library that can parse plain text files to understand their semantic structure, retrieve files semantically similar to it, and perform many other tasks. It is scalable and platform-independent, like most of the Python libraries and frameworks we’ve discussed in this article. If you are planning to use your knowledge of artificial intelligence to work on NLP (Natural Language Processing) projects, then you should study this library for sure.


PyTorch helps make research prototyping easier so you can deploy products faster. It lets you switch between graphics modes via TorchScript and provides distributed training that you can scale. PyTorch is also available on multiple cloud platforms and has many libraries and tools in its ecosystem that support NLP, computer vision, and many other solutions. To perform advanced AI implementations, you will need to be familiar with PyTorch.


Shogun is an (open-source) machine learning library and provides many unified and efficient ML methods. It is not exclusively based on Python, so you can use it with several other languages ​​such as Lua, C#, Java, R and Ruby. It lets you combine multiple classes of algorithms, data representations, and tools so you can quickly prototype data pipelines. It has a fantastic testing infrastructure that you can use on various operating system configurations. It also has several proprietary algorithms, including Krylov methods and multi-core learning. Therefore, learning Shogun will surely help you master AI and machine learning.


Based on Theano, Pylearn2 is one of the most popular machine learning libraries among Python developers. You can use mathematical expressions to write its plugins while Theano takes care of their stabilization and optimization. On Github, Pylearn2 has over 7,000 commits, and they keep growing, showing its popularity among ML developers. Pylearn2 emphasizes flexibility and offers a wide variety of features, including an interface for media (images, vectors, etc.) and cross-platform implementations.


Nilearn helps in neuroimaging data and is a popular Python module. It uses scikit-learn (which we discussed earlier) to perform various statistical actions such as decoding, modeling, connectivity analysis, and classification. Neuroimaging is a leading field in the medical sector and can help solve multiple problems such as better diagnosis with higher accuracy. If you want to use AI in the medical field, this is the place to start.


Numenta is based on a neocortex theory called HTM (Hierarchical Temporal Memory). Many people have developed solutions based on HTM and the software. However, there is a lot of work going on in this project. HTM is a neuroscience-based artificial intelligence framework.

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