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Working as a data scientist or data engineer, Python is a must-learn programming language. There is possibly no better way of learning Python than working on open-source projects. It will help you become skilled in the language better. Here are the top 10 Python AI open-source projects for you to try in 2022.
Theano lets you optimize, evaluate, and define mathematical expressions that involve multi-dimensional arrays. It is 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 evaluate expressions quickly. Theano allows you to use NumPy.ndarray in its functions as well, so you get to use the capabilities of NumPy effectively.
Scikit-learn is a Python-based library of tools you can use for data analysis and data mining. You can reuse it in numerous contexts. It has excellent accessibility, so using it is quite easy as well. Its developers have built it on top of matplotlib, NumPy, and SciPy. Some tasks for which you can use Scikit-learn include Clustering, Regression, Classification, Model Selection, Preprocessing, and Dimensionality Reduction. To become a proper 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 nets, convnets, recursive nets, and feed-forward nets. Apart from that, it allows CUDA computation so you can use a GPU with very few lines of code. You can run Chainer on many GPUs too if required. A significant advantage of Chainer is it makes debugging the code very easy, so you won't have to put much effort in that regard. On Github, Chainer has more than 12,000 commits, so you can understand 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 are using it for industrial applications, academic research, multimedia, and many other domains.
Gensim is an open-source Python library that can analyze plain-text files for understanding their semantic structure, retrieve files that are semantically similar to that one, and perform many other tasks. It is scalable and platform-independent, like many of the Python libraries and frameworks we have discussed in this article. If you plan on using your knowledge of artificial intelligence to work on NLP (Natural Language Processing) projects, then you should study this library for sure.
PyTorch helps in facilitating research prototyping so you can deploy products faster. It allows you to transition between graph modes through TorchScript and provides distributed training you can scale. PyTorch is available on multiple cloud platforms as well and has numerous libraries and tools in its ecosystem that support NLP, computer vision, and many other solutions. To perform advanced AI implementations, you'll have to become familiar with PyTorch.
Shogun is a machine learning library (open-source) and provides many unified as well as efficient ML methods. It is not based on Python exclusively so you can use it with several other languages too such as Lua, C#, Java, R, and Ruby. It allows the combining of multiple algorithm classes, data representations, and tools so you can prototype data pipelines quickly. It has a fantastic infrastructure for testing that you can use on various OS setups. It has several exclusive algorithms as well, including Krylov methods and Multiple Kernel Learning, so learning about Shogun will surely help you in mastering AI and machine learning.
Based on Theano, Pylearn2 is among the most prevalent 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 more than 7k commits, and they are still growing, which shows its popularity among ML developers. Pylearn2 focuses on flexibility and provides 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've discussed earlier) to perform various statistical actions such as decoding, modeling, connectivity analysis, and classification. Neuro-imaging is a prominent area in the medical sector and can help in solving multiple issues such as better diagnosis with higher accuracy. If you're interested in using AI in the medical field, then 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's a lot of work going on in this project. HTM is a machine intelligence framework that's based on neuroscience.
