{"id":53299,"date":"2019-11-28T00:00:00","date_gmt":"2019-11-27T21:00:00","guid":{"rendered":"https:\/\/prohoster.info\/blog\/blog_prohoster\/56-proektov-na-python-s-otkrytym-ishodnym-kodom"},"modified":"2020-02-18T14:01:11","modified_gmt":"2020-02-18T11:01:11","slug":"56-proektov-na-python-s-otkrytym-ishodnym-kodom","status":"publish","type":"post","link":"https:\/\/prohoster.info\/en\/blog\/news\/56-proektov-na-python-s-otkrytym-ishodnym-kodom","title":{"rendered":"56 open-source projects in Python","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"

\"56
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1. Flask<\/h3>\n

\nThis is a micro-framework written in Python. It does not have validations for forms or a level of database abstraction, but allows you to use third-party libraries for common functions. That\u2019s precisely why it is called a micro-framework. Flask is designed for easy and quick application development, as well as being scalable and lightweight. It is based on the Werkzeug and Jinja2 projects. You can learn more about it in the latest DataFlair article on Python Flask<\/a><\/noindex>.<\/p>\n

2. Keras<\/h3>\n

\nKeras is an open-source neural network library written in Python. It is user-friendly, modular, and extensible, and can work on top of TensorFlow, Theano, PlaidML, or Microsoft Cognitive Toolkit (CNTK). Keras includes everything: templates, target and transfer functions, optimizers, and much more. It also supports convolutional and recurrent neural networks.<\/p>\n

Working on the latest open-source project based on Keras \u2014 Breast Cancer Classification<\/a><\/noindex>.
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\nThis article is translated with support from EDISON Software, which is developing a document storage diagnostic system named Vivaldi.<\/a><\/noindex>, as well as invests in startups<\/a><\/noindex>.<\/p><\/blockquote>\n

3. SpaCy<\/h3>\n

\nThis is an open-source software library that specializes in natural language processing (NLP)<\/a><\/noindex> and is written in Python and Cython. While NLTK is more suitable for educational and research purposes, SpaCy's focus is on providing software for production. Additionally, Thinc is SpaCy's machine learning library, which features CNN models for part-of-speech tagging, dependency parsing, and named entity recognition.<\/p>\n

4. Sentry<\/h3>\n

\nSentry offers open-source error monitoring hosting so you can detect and sort errors in real-time. Simply install the SDK for your language(s) or framework(s) and get started. It allows you to capture unhandled exceptions, study stack traces, analyze the impact of each issue, track errors across various projects, assign issues, and much more. Using Sentry means fewer errors and more code being shipped.<\/p>\n

5. OpenCV<\/h3>\n

\nOpenCV is an open-source computer vision and machine learning library. It contains over 2500 optimized algorithms for computer vision tasks such as object detection and recognition, classification of various types of human activities, motion tracking using cameras, creating 3D models of objects, stitching images for high-resolution outputs, and many other tasks. The library is available in multiple languages, including Python, C++, Java, etc.<\/p>\n

Stars on GitHub: 39,585<\/p>\n

Have you worked on any OpenCV projects? Here\u2019s one \u2014 Gender and Age Detection Project<\/a><\/noindex><\/p>\n

6. Nilearn<\/h3>\n

\nThis module is designed for the fast and simple implementation of statistical learning on NeuroImaging data. It enables the use of scikit-learn for multivariate statistics for predictive modeling, classification, decoding, and connectivity analysis. Nilearn is part of the NiPy ecosystem, which is a community dedicated to using Python for neuroimaging data analysis.<\/p>\n

Stars on Github<\/a><\/noindex>: 549<\/p>\n

7. scikit-Learn<\/h3>\n

\nScikit-learn is another open-source Python project. It is a widely recognized machine learning library for Python. Often used with NumPy and SciPy, it offers classification, regression, and clustering \u2014 it supports SVM (Support Vector Machines)<\/a><\/noindex>, random forests, gradient boosting, k-means, and DBSCAN. This library is written in Python and Cython.<\/p>\n

Stars on GitHub: 37,144<\/p>\n

8. PyTorch<\/h3>\n

\nPyTorch is yet another open-source machine learning library written in and for Python. It is based on the Torch library and is excellent for areas such as computer vision and natural language processing (NLP). It also has a C++ frontend. <\/p>\n

Among many other features, PyTorch offers two high-level functionalities:<\/p>\n