Update section descriptions

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Vinta 2019-05-25 18:09:56 +08:00
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## Code Analysis
*Tools of static analysis, linters and code quality checkers. Also see: [awesome-static-analysis](https://github.com/mre/awesome-static-analysis).*
*Tools of static analysis, linters and code quality checkers. Also see [awesome-static-analysis](https://github.com/mre/awesome-static-analysis).*
* Code Analysis
* [coala](https://github.com/coala/coala/) - Language independent and easily extendable code analysis application.
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## Concurrency and Parallelism
*Libraries for concurrent and parallel execution. See [awesome-asyncio](https://github.com/timofurrer/awesome-asyncio).*
*Libraries for concurrent and parallel execution. Also see [awesome-asyncio](https://github.com/timofurrer/awesome-asyncio).*
* [concurrent.futures](https://docs.python.org/3/library/concurrent.futures.html) - (Python standard library) A high-level interface for asynchronously executing callables.
* [multiprocessing](https://docs.python.org/3/library/multiprocessing.html) - (Python standard library) Process-based parallelism.
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## Data Visualization
*Libraries for visualizing data. Also see: [awesome-javascript](https://github.com/sorrycc/awesome-javascript#data-visualization).*
*Libraries for visualizing data. Also see [awesome-javascript](https://github.com/sorrycc/awesome-javascript#data-visualization).*
* [Altair](https://github.com/altair-viz/altair) - Declarative statistical visualization library for Python.
* [Bokeh](https://github.com/bokeh/bokeh) - Interactive Web Plotting for Python.
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## Deep Learning
*Frameworks for Neural Networks and Deep Learning. Also see: [awesome-deep-learning](https://github.com/ChristosChristofidis/awesome-deep-learning).*
*Frameworks for Neural Networks and Deep Learning. Also see [awesome-deep-learning](https://github.com/ChristosChristofidis/awesome-deep-learning).*
* [caffe](https://github.com/BVLC/caffe) - A fast open framework for deep learning..
* [keras](https://github.com/keras-team/keras) - A high-level neural networks library and capable of running on top of either TensorFlow or Theano.
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## Machine Learning
*Libraries for Machine Learning. Also see: [awesome-machine-learning](https://github.com/josephmisiti/awesome-machine-learning#python).*
*Libraries for Machine Learning. Also see [awesome-machine-learning](https://github.com/josephmisiti/awesome-machine-learning#python).*
* [H2O](https://github.com/h2oai/h2o-3) - Open Source Fast Scalable Machine Learning Platform.
* [Metrics](https://github.com/benhamner/Metrics) - Machine learning evaluation metrics.
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## Science
*Libraries for scientific computing. Also see: [Python-for-Scientists](https://github.com/TomNicholas/Python-for-Scientists)*
*Libraries for scientific computing. Also see [Python-for-Scientists](https://github.com/TomNicholas/Python-for-Scientists)*
* [astropy](http://www.astropy.org/) - A community Python library for Astronomy.
* [bcbio-nextgen](https://github.com/chapmanb/bcbio-nextgen) - Providing best-practice pipelines for fully automated high throughput sequencing analysis.
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## Third-party APIs
*Libraries for accessing third party services APIs. Also see: [List of Python API Wrappers and Libraries](https://github.com/realpython/list-of-python-api-wrappers).*
*Libraries for accessing third party services APIs. Also see [List of Python API Wrappers and Libraries](https://github.com/realpython/list-of-python-api-wrappers).*
* [apache-libcloud](https://libcloud.apache.org/) - One Python library for all clouds.
* [boto3](https://github.com/boto/boto3) - Python interface to Amazon Web Services.
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## Continuous Integration
*Also see: [awesome-CIandCD](https://github.com/ciandcd/awesome-ciandcd#online-build-system).*
*Also see [awesome-CIandCD](https://github.com/ciandcd/awesome-ciandcd#online-build-system).*
* [CircleCI](https://circleci.com/) - A CI service that can run very fast parallel testing.
* [Travis CI](https://travis-ci.org) - A popular CI service for your open source and [private](https://travis-ci.com) projects. (GitHub only)