GitHub is the world’s biggest internet storage space for collaborative projects. Source projects and companies have been publishing their progress to the world since GitHub, allowing others to extend and work on top of these amazing technological achievements.
Without a doubt, GitHub has had a significant influence on technology innovation as well as an influence on ML innovations and data science overall. The repository (short “Repo) is a folder or storage area where you may save your projects and documents.
Inside a repository, you’ll discover different types of files, such as text, picture or code, and everything else you can think of.
Without a doubt, the top open-source DL (deep learning) and ML (machine learning) libraries, learning tools, and frameworks have continually evolving repositories.
Studying these ML Repos is like walking into the office of the most brilliant data scientists in the world and diving into their work. Here’s a roundup of the greatest data science GitHub repositories available right now:
- Caffe – Caffe is a deep learning framework for machine vision, multimedia, and language that is free source. Many different types of deep learning architectures for image classification and segmentation are supported by Caffe. CNN, RCNN, LSTM, and fully connected neural network architectures are all supported. NVIDIA cuDNN and Intel MKL are two GPU and CPU-based acceleration computational kernel libraries that Caffe supports.
- SKLearn – or differently known as Scikit-learn, is a free Python ML toolkit that includes support for various things like the random forest, gradient boosting or, as well as other classification, regression, and clustering methods. It was created to work with the SciPy and NumPy libraries.
- PyTorch – is a Torch-based open-source machine learning framework for computer vision and natural language processing (NLP). Facebook’s AI Research lab is principally responsible for its development. Tensor computation with powerful GPU acceleration and deep neural networks built on a tape-based autodiff system are two high-level capabilities provided by PyTorch.
- NLTK – stands for “Natural Language Toolkit”. It’s a popular Python framework for working with language data. It includes a set of text processing libraries for tagging, categorization, stemming, tokenization, semantic reasoning. It also includes easy-to-use interfaces to over 50 corpora and lexical resources, such as WordNet. Educators, students, linguists, engineers, and academics will all benefit from NLTK.
- Bokeh – is a contemporary online browser-based visualization library. It allows for the creation of adaptable visuals in a beautiful and simple manner, as well as high-performance across big or streaming information. Anyone who wants to create interactive charts and data apps quickly and efficiently may use Bokeh. Bokeh is capable of producing attractive and dynamic visualizations with interaction across very large datasets.
- TensorFlow – an open-source toolkit and framework, to design, create, and train deep learning models and large-scale machine learning systems. Across a variety of applications, the software library is utilized for data flow and differentiable programming. It’s a symbolic math library that’s also utilized in neural networks and other machine learning applications. At Google, it’s utilized for both research and manufacturing.
- Pandas – is a Python library that includes a variety of data analysis capabilities. The package includes a number of structures that may be used to perform a variety of manipulation tasks. Not only that, but it also provides a number of data analysis methods, which come in helpful when working on ML and data science challenges in Python.