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[DOC] improved readme links (#1658)
* reorganizes the top of the readme for a more orderly presentation of badges. * migrates all badges to `shields.io` for consistency * adds community links to `sktime` developer channels
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README.md

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![PyTorch Forecasting](./docs/source/_static/logo.svg)
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[pypi-image]: https://badge.fury.io/py/pytorch-forecasting.svg
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[pypi-url]: https://pypi.python.org/pypi/pytorch-forecasting
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[conda-image]: https://img.shields.io/conda/v/conda-forge/pytorch-forecasting
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[conda-url]: https://anaconda.org/conda-forge/pytorch-forecasting
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[build-image]: https://github.com/jdb78/pytorch-forecasting/actions/workflows/test.yml/badge.svg?branch=master
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[build-url]: https://github.com/jdb78/pytorch-forecasting/actions/workflows/test.yml?query=branch%3Amaster
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[linter-image]: https://github.com/jdb78/pytorch-forecasting/actions/workflows/lint.yml/badge.svg?event=push
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[linter-url]: https://github.com/jdb78/pytorch-forecasting/actions/workflows/code_quality.yml?query=branch%3Amaster
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[docs-image]: https://readthedocs.org/projects/pytorch-forecasting/badge/?version=latest
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[docs-url]: https://pytorch-forecasting.readthedocs.io
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[coverage-image]: https://codecov.io/gh/jdb78/pytorch-forecasting/branch/master/graph/badge.svg
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[coverage-url]: https://codecov.io/github/jdb78/pytorch-forecasting?branch=master
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_PyTorch Forecasting_ is a PyTorch-based package for forecasting with state-of-the-art deep learning architectures. It provides a high-level API and uses [PyTorch Lightning](https://pytorch-lightning.readthedocs.io/) to scale training on GPU or CPU, with automatic logging.
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[![PyPI Version][pypi-image]][pypi-url] [![Conda Version][conda-image]][conda-url] [![Docs Status][docs-image]][docs-url] [![Linter Status][linter-image]][linter-url] [![Build Status][build-image]][build-url] [![Code Coverage][coverage-image]][coverage-url]
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**[Documentation](https://pytorch-forecasting.readthedocs.io)** | **[Tutorials](https://pytorch-forecasting.readthedocs.io/en/latest/tutorials.html)** | **[Release Notes](https://pytorch-forecasting.readthedocs.io/en/latest/CHANGELOG.html)**
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| | **[Documentation](https://pytorch-forecasting.readthedocs.io)** · **[Tutorials](https://pytorch-forecasting.readthedocs.io/en/latest/tutorials.html)** · **[Release Notes](https://pytorch-forecasting.readthedocs.io/en/latest/CHANGELOG.html)** |
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| **Open Source** | [![MIT](https://img.shields.io/github/license/jdb78/pytorch-forecasting)](https://github.com/jdb78/pytorch-forecasting/blob/master/LICENSE) |
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| **Community** | [![!discord](https://img.shields.io/static/v1?logo=discord&label=discord&message=chat&color=lightgreen)](https://discord.com/invite/54ACzaFsn7) [![!slack](https://img.shields.io/static/v1?logo=linkedin&label=LinkedIn&message=news&color=lightblue)](https://www.linkedin.com/company/scikit-time/) |
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| **CI/CD** | [![github-actions](https://img.shields.io/github/actions/workflow/status/jdb78/pytorch-forecasting/pypi_release.yml?logo=github)](https://github.com/jdb78/pytorch-forecasting/actions/workflows/pypi_release.yml) [![readthedocs](https://img.shields.io/readthedocs/pytorch-forecasting?logo=readthedocs)](https://pytorch-forecasting.readthedocs.io) [![platform](https://img.shields.io/conda/pn/conda-forge/pytorch-forecasting)](https://github.com/jdb78/pytorch-forecasting) [![Code Coverage][coverage-image]][coverage-url] |
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| **Code** | [![!pypi](https://img.shields.io/pypi/v/pytorch-forecasting?color=orange)](https://pypi.org/project/pytorch-forecasting/) [![!conda](https://img.shields.io/conda/vn/conda-forge/pytorch-forecasting)](https://anaconda.org/conda-forge/pytorch-forecasting) [![!python-versions](https://img.shields.io/pypi/pyversions/pytorch-forecasting)](https://www.python.org/) [![!black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black) |
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_PyTorch Forecasting_ is a PyTorch-based package for forecasting time series with state-of-the-art network architectures. It provides a high-level API for training networks on pandas data frames and leverages
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[PyTorch Lightning](https://pytorch-lightning.readthedocs.io/) for scalable training on (multiple) GPUs, CPUs and for automatic logging.
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[coverage-image]: https://codecov.io/gh/jdb78/pytorch-forecasting/branch/master/graph/badge.svg
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[coverage-url]: https://codecov.io/github/jdb78/pytorch-forecasting?branch=master
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