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# Neural networks using Python

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				<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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  <i class="far fa-calendar" aria-hidden="true"></i><span class="visually-hidden">last_modification</span> Updated:   
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## Requirements

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

- [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="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)

---

---

## 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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			<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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			<a href="/training-material/hall-of-fame/shiltemann/" class="contributor-badge contributor-badge-small contributor-shiltemann"><img src="https://avatars.githubusercontent.com/shiltemann?s=36" alt="Saskia Hiltemann avatar" width="36" class="avatar" /></a><a href="/training-material/hall-of-fame/bebatut/" class="contributor-badge contributor-badge-small contributor-bebatut"><img src="https://avatars.githubusercontent.com/bebatut?s=36" alt="Bérénice Batut avatar" width="36" class="avatar" /></a></td>
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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/>

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Notes for next slide

Neural networks using Python

Ralf Gabriels

Updated: PURL: gxy.io/GTN:S00133

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1 / 4

Presenter notes contain extra information which might be useful if you intend to use these slides for teaching.

Press P again to switch presenter notes off

Press C to create a new window where the same presentation will be displayed. This window is linked to the main window. Changing slides on one will cause the slide to change on the other.

Useful when presenting.

Requirements

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

Foundations of Data Science
- Introduction to Python: tutorial hands-on
- Python - Warm-up for statistics and machine learning: tutorial hands-on

Statistics and machine learning
- Foundational Aspects of Machine Learning using Python: tutorial hands-on

2 / 4

3 / 4

Thank You!

This material is the result of a collaborative work. Thanks to the Galaxy Training Network and all the contributors!

Author(s)	Ralf Gabriels
Reviewers

Tutorial Content is licensed under Creative Commons Attribution 4.0 International License.

4 / 4

Requirements

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

Foundations of Data Science
- Introduction to Python: tutorial hands-on
- Python - Warm-up for statistics and machine learning: tutorial hands-on

Statistics and machine learning
- Foundational Aspects of Machine Learning using Python: tutorial hands-on

2 / 4

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