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Update space README.md
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README.md
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<div align="center">
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<img src="https://user-images.githubusercontent.com/16392542/77208906-224aa500-6aba-11ea-96bd-e81806074030.png" width="350">
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[Install Instructions](https://auto.gluon.ai/stable/install.html) | [Documentation](https://auto.gluon.ai/stable/index.html) | [Release Notes](https://auto.gluon.ai/stable/whats_new/index.html)
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AutoGluon automates machine learning tasks enabling you to easily achieve strong predictive performance in your applications. With just a few lines of code, you can train and deploy high-accuracy machine learning and deep learning models on image, text, time series, and tabular data.
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</div>
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## 💾 Installation
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AutoGluon is supported on Python 3.8 - 3.11 and is available on Linux, MacOS, and Windows.
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Visit our [Installation Guide](https://auto.gluon.ai/stable/install.html) for detailed instructions, including GPU support, Conda installs, and optional dependencies.
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##
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Build accurate end-to-end ML models in just 3 lines of code!
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predictions = predictor.predict("test.csv")
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```
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### Hands-on Tutorials / Talks
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| Title | Format | Location | Date |
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|--------------------------------------------------------------------------------------------------------------------------|----------|----------------------------------------------------------------------------------|------------|
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### Scientific Publications
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- [AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data](https://arxiv.org/pdf/2003.06505.pdf) (*Arxiv*, 2020) ([BibTeX](CITING.md#general-usage--autogluontabular))
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- [AutoGluon Official Docker Container](https://hub.docker.com/r/autogluon/autogluon)
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- [AutoGluon-Tabular on AWS Marketplace](https://aws.amazon.com/marketplace/pp/prodview-n4zf5pmjt7ism) (Not maintained by us)
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##
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If you use AutoGluon in a scientific publication, please refer to our [citation guide](CITING.md).
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##
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We are actively accepting code contributions to the AutoGluon project. If you are interested in contributing to AutoGluon, please read the [Contributing Guide](https://github.com/autogluon/autogluon/blob/master/CONTRIBUTING.md) to get started.
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##
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This library is licensed under the Apache 2.0 License.
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<div align="center">
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<img src="https://user-images.githubusercontent.com/16392542/77208906-224aa500-6aba-11ea-96bd-e81806074030.png" width="350">
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<h2>AutoML for Image, Text, Time Series, and Tabular Data</h2>
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<div style="width: max-content; margin: 0 auto; max-width: 90%;">
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<p style="display: flex; flex-wrap: wrap; gap: 10px; padding: 0; line-height: 1.1; justify-content: center;">
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<a rel="nofollow" style="display: inline-block;" href="https://github.com/autogluon/autogluon/releases">
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<img style="margin: 10px 0" alt="Latest Release" src="https://img.shields.io/github/v/release/autogluon/autogluon">
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</a>
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<a rel="nofollow" style="display: inline-block;" href="https://anaconda.org/conda-forge/autogluon">
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<img style="margin: 10px 0" alt="Conda Forge" src="https://img.shields.io/conda/vn/conda-forge/autogluon.svg">
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</a>
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<a rel="nofollow" style="display: inline-block;" href="https://pypi.org/project/autogluon/">
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<img style="margin: 10px 0" alt="Python Versions" src="https://img.shields.io/badge/python-3.8%20%7C%203.9%20%7C%203.10%20%7C%203.11-blue">
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</a>
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<a rel="nofollow" style="display: inline-block;" href="https://pepy.tech/project/autogluon">
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<img style="margin: 10px 0" alt="Downloads" src="https://pepy.tech/badge/autogluon/month">
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</a>
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<a rel="nofollow" style="display: inline-block;" href="https://github.com/autogluon/autogluon/blob/master/LICENSE">
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<img style="margin: 10px 0" alt="GitHub license" src="https://img.shields.io/badge/License-Apache_2.0-blue.svg">
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</a>
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<a rel="nofollow" style="display: inline-block;" href="https://discord.gg/wjUmjqAc2N">
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<img style="margin: 10px 0" alt="Discord" src="https://img.shields.io/discord/1043248669505368144?logo=discord&style=flat">
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</a>
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<a rel="nofollow" style="display: inline-block;" href="https://x.com/autogluon">
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<img style="margin: 10px 0" alt="Twitter" src="https://img.shields.io/twitter/follow/autogluon?style=social">
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</p>
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</div>
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[Install Instructions](https://auto.gluon.ai/stable/install.html) | [Documentation](https://auto.gluon.ai/stable/index.html) | [Release Notes](https://auto.gluon.ai/stable/whats_new/index.html)
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</div>
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AutoGluon automates machine learning tasks enabling you to easily achieve strong predictive performance in your applications. With just a few lines of code, you can train and deploy high-accuracy machine learning and deep learning models on image, text, time series, and tabular data.
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## 💾 Installation
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AutoGluon is supported on Python 3.8 - 3.11 and is available on Linux, MacOS, and Windows.
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Visit our [Installation Guide](https://auto.gluon.ai/stable/install.html) for detailed instructions, including GPU support, Conda installs, and optional dependencies.
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## ⚡ Quickstart
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Build accurate end-to-end ML models in just 3 lines of code!
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predictions = predictor.predict("test.csv")
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```
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<table>
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<thead>
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<tr>
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<th style="text-align: left">AutoGluon Task</th>
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<th style="text-align: left">Quickstart</th>
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<th style="text-align: left">API</th>
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</tr>
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</thead>
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<tbody>
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<tr>
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<td>TabularPredictor</td>
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<td>
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<a href="https://auto.gluon.ai/stable/tutorials/tabular/tabular-quick-start.html">
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<img style="margin: 0" alt="Quick Start" src="https://img.shields.io/static/v1?label=&message=tutorial&color=grey">
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</a>
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</td>
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<td>
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<a href="https://auto.gluon.ai/stable/api/autogluon.tabular.TabularPredictor.html">
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<img style="margin: 0" alt="API" src="https://img.shields.io/badge/api-reference-blue.svg">
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</a>
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</td>
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</tr>
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<tr>
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<td>TimeSeriesPredictor</td>
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<td>
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<a href="https://auto.gluon.ai/stable/tutorials/timeseries/forecasting-quick-start.html">
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<img style="margin: 0" alt="Quick Start" src="https://img.shields.io/static/v1?label=&message=tutorial&color=grey">
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</a>
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</td>
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<td>
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<a href="https://auto.gluon.ai/stable/api/autogluon.timeseries.TimeSeriesPredictor.html">
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<img style="margin: 0" alt="API" src="https://img.shields.io/badge/api-reference-blue.svg">
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</a>
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</td>
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</tr>
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<tr>
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<td>MultiModalPredictor</td>
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<td>
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<a href="https://auto.gluon.ai/stable/tutorials/multimodal/multimodal_prediction/multimodal-quick-start.html">
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<img style="margin: 0" alt="Quick Start" src="https://img.shields.io/static/v1?label=&message=tutorial&color=grey">
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</a>
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</td>
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<td>
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<a href="https://auto.gluon.ai/stable/api/autogluon.multimodal.MultiModalPredictor.html">
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<img style="margin: 0" alt="API" src="https://img.shields.io/badge/api-reference-blue.svg">
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</a>
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</td>
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</tr>
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</tbody>
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</table>
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## 🔍 Resources
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### Hands-on Tutorials / Talks
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| Title | Format | Location | Date |
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|--------------------------------------------------------------------------------------------------------------------------|----------|----------------------------------------------------------------------------------|------------|
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| 📺 [AutoGluon 1.0: Shattering the AutoML Ceiling with Zero Lines of Code](https://www.youtube.com/watch?v=5tvp_Ihgnuk) | Tutorial | [AutoML Conf 2023](https://2023.automl.cc/) | 2023/09/12 |
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| 🔉 [AutoGluon: The Story](https://automlpodcast.com/episode/autogluon-the-story) | Podcast | [The AutoML Podcast](https://automlpodcast.com/) | 2023/09/05 |
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| 📺 [AutoGluon: AutoML for Tabular, Multimodal, and Time Series Data](https://youtu.be/Lwu15m5mmbs?si=jSaFJDqkTU27C0fa) | Tutorial | PyData Berlin | 2023/06/20 |
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| 📺 [Solving Complex ML Problems in a few Lines of Code with AutoGluon](https://www.youtube.com/watch?v=J1UQUCPB88I) | Tutorial | PyData Seattle | 2023/06/20 |
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| 📺 [The AutoML Revolution](https://www.youtube.com/watch?v=VAAITEds-28) | Tutorial | [Fall AutoML School 2022](https://sites.google.com/view/automl-fall-school-2022) | 2022/10/18 |
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### Scientific Publications
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- [AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data](https://arxiv.org/pdf/2003.06505.pdf) (*Arxiv*, 2020) ([BibTeX](CITING.md#general-usage--autogluontabular))
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- [AutoGluon Official Docker Container](https://hub.docker.com/r/autogluon/autogluon)
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- [AutoGluon-Tabular on AWS Marketplace](https://aws.amazon.com/marketplace/pp/prodview-n4zf5pmjt7ism) (Not maintained by us)
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## 📝 Citing AutoGluon
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If you use AutoGluon in a scientific publication, please refer to our [citation guide](CITING.md).
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## 👋 How to get involved
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We are actively accepting code contributions to the AutoGluon project. If you are interested in contributing to AutoGluon, please read the [Contributing Guide](https://github.com/autogluon/autogluon/blob/master/CONTRIBUTING.md) to get started.
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## 🏛️ License
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This library is licensed under the Apache 2.0 License.
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