Top 7 AI Open Source Projects to Contribute to in 2023



Hey there, Nomadev here! If you’re reading this, you’re probably as excited about AI and open source as I am. As an AI Tool Guru and enthusiast, I’m always on the lookout for cool projects to contribute to and learn from. And guess what? I’ve found some gems that I think you’ll love.
So, whether you’re a seasoned developer or a newbie looking to dip your toes in the world of AI, this list is for you. Let’s dive in!

1. Llama 2 by Meta and Microsoft

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Llama 2 is the next generation of Meta’s open source large language model. It’s free for research and commercial use, making it a great project to contribute to.

→ Large community support from both Meta and Microsoft.
→ Good for those interested in large language models and natural language processing.
→ Requires knowledge of Python and machine learning concepts.
→ Opportunity to contribute to a project that’s at the forefront of AI research.


2. DeepChem

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DeepChem aims to provide a high-quality open-source toolchain that democratizes the use of deep learning in drug discovery, materials science, quantum chemistry, and biology.

→ Ideal for those interested in the intersection of AI and chemistry/biology.
→ Active community and well-maintained project.
→ Good understanding of deep learning, chemistry/biology, and Python is required.


3. Detectron2

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Detectron2 is Facebook AI Research’s next generation software system that implements state-of-the-art object detection algorithms. It is a ground-up rewrite of the previous version, Detectron, and it originates from maskrcnn-benchmark.

→ Backed by Facebook AI Research, ensuring a large and active community.
→ Ideal for those interested in object detection algorithms and computer vision.
→ Requires knowledge of Python, PyTorch, and understanding of deep learning concepts.
→ Opportunity to contribute to a project that’s pushing the boundaries of computer vision.


4. Theano

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Theano is a Python library that lets you define, optimize, and evaluate mathematical expressions, especially ones with multi-dimensional arrays (numpy.ndarray).

→ Supported by a large community of machine learning enthusiasts.
→ Good for those interested in mathematical computations and multi-dimensional arrays.
→ Requires knowledge of Python and NumPy.
→ A chance to contribute to a project that’s widely used in the machine learning community.


5. MXNet

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Apache MXNet is an open-source deep learning software framework, used to train, and deploy deep neural networks. It is known for its capabilities in handling multiple data formats.

→ Backed by Apache, ensuring a large community support.
→ Ideal for those interested in deep learning and handling multiple data formats.
→ Requires knowledge of Python and deep learning concepts.
→ Opportunity to contribute to a project that’s used by many in the AI industry.


6. OpenCV

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OpenCV (Open Source Computer Vision Library) is an open-source computer vision and machine learning software library. OpenCV was built to provide a common infrastructure for computer vision applications and to accelerate the use of machine perception in commercial products.

→ Large community of developers interested in computer vision and machine learning.
→ Good for those interested in real-time computer vision applications.
→ Requires knowledge of C++, Python, and understanding of computer vision concepts.
→ A chance to contribute to a project that’s widely used in the computer vision industry.


7. HuggingFace Transformers

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Transformers provides thousands of pretrained models to perform tasks on texts such as classification, information extraction, answer extraction, summarization, translation, text generation, etc in 100+ languages. Its aim is to make cutting-edge NLP easier to use for everyone.

→ Supported by HuggingFace, a leading company in natural language processing.
→ Ideal for those interested in NLP tasks such as classification, information extraction, summarization, etc.
→ Requires knowledge of Python, PyTorch or TensorFlow, and understanding of NLP concepts.
→ Opportunity to contribute to a project that’s making NLP more accessible to everyone.


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