Deep Learning (without Generative Artificial Intelligence) using Python

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# Deep Learning (without Generative Artificial Intelligence) using Python

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			<li>
				<a href="/training-material/hall-of-fame/ralfg/" class="contributor-badge contributor-ralfg"><img src="https://avatars.githubusercontent.com/ralfg?s=36" alt="Ralf Gabriels avatar" width="36" class="avatar" />
    Ralf Gabriels</a></li>
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## Requirements

Before diving into this slide deck, we recommend you to have a look at:

- [Introduction to Galaxy Analyses](/training-material/topics/introduction)

- [Foundations of Data Science](/training-material/topics/data-science)
    - Introduction to Python: [<i class="fas fa-laptop" aria-hidden="true"></i><span class="visually-hidden">tutorial</span> hands-on](/training-material/topics/data-science/tutorials/python-basics/tutorial.html)
    - Python - Warm-up for statistics and machine learning: [<i class="fas fa-laptop" aria-hidden="true"></i><span class="visually-hidden">tutorial</span> hands-on](/training-material/topics/data-science/tutorials/python-warmup-stat-ml/tutorial.html)

- [Statistics and machine learning](/training-material/topics/statistics)
    - Foundational Aspects of Machine Learning using Python: [<i class="fab fa-slideshare" aria-hidden="true"></i><span class="visually-hidden">slides</span> slides](/training-material/topics/statistics/tutorials/intro-to-ml-with-python/slides.html) - [<i class="fas fa-laptop" aria-hidden="true"></i><span class="visually-hidden">tutorial</span> hands-on](/training-material/topics/statistics/tutorials/intro-to-ml-with-python/tutorial.html)
    - Neural networks using Python: [<i class="fab fa-slideshare" aria-hidden="true"></i><span class="visually-hidden">slides</span> slides](/training-material/topics/statistics/tutorials/neural-networks-with-python/slides.html) - [<i class="fas fa-laptop" aria-hidden="true"></i><span class="visually-hidden">tutorial</span> hands-on](/training-material/topics/statistics/tutorials/neural-networks-with-python/tutorial.html)

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## Thank You!

This material is the result of a collaborative work. Thanks to the [Galaxy Training Network](https://training.galaxyproject.org) and all the contributors!

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		<td><abbr title="These people wrote the bulk of the tutorial, they may have done the analysis, built the workflow, and wrote the text themselves.">Author(s)</abbr></td>
		<td>
			<a href="/training-material/hall-of-fame/ralfg/" class="contributor-badge contributor-ralfg"><img src="https://avatars.githubusercontent.com/ralfg?s=36" alt="Ralf Gabriels avatar" width="36" class="avatar" />
    Ralf Gabriels</a>
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	</tr>

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Tutorial Content is licensed under <a rel="license" href="http://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International License</a>.<br/>