Deep Learning (without Generative Artificial Intelligence) using Python

Under Development!

This tutorial is not in its final state. The content may change a lot in the next months. Because of this status, it is also not listed in the topic pages.

Author(s)

Ralf Gabriels

Overview
Questions:

to do

Objectives:

Input data representation

Concept of filters

Concept of pooling layers

Initialising a model with conv layers (code)

Concept of RNNs

Concept of attention

Implementation of RNN (code)

Implementation of attention mechanism (code)

Implementation of fine-tuning (code)

Requirements:

Introduction to Galaxy Analyses

tutorial Hands-on: Introduction to Python

tutorial Hands-on: Python - Warm-up for statistics and machine learning

slides Slides: Foundational Aspects of Machine Learning using Python

tutorial Hands-on: Foundational Aspects of Machine Learning using Python

slides Slides: Neural networks using Python

tutorial Hands-on: Neural networks using Python

Time estimation: 3 hours

Level: Intermediate Intermediate

Supporting Materials:

Slides

Jupyter Notebook

instances Available on these Galaxies

Possibly Working

UseGalaxy.eu

UseGalaxy.org

UseGalaxy.org.au

UseGalaxy.fr

Published: Mar 11, 2025

Last modification: Mar 11, 2025

License: Tutorial Content is licensed under Creative Commons Attribution 4.0 International License. The GTN Framework is licensed under MIT

version Revision: 1

Best viewed in a Jupyter Notebook

This tutorial is best viewed in a Jupyter notebook! You can load this notebook one of the following ways

Run on the GTN with JupyterLite (in-browser computations)

Click to Launch JupyterLite

Launching the notebook in Jupyter in Galaxy

Instructions to Launch JupyterLab

Open a Terminal in JupyterLab with File -> New -> Terminal

Run wget https://training.galaxyproject.org/training-material/topics/statistics/tutorials/deep-learning-without-gai-with-python/statistics-deep-learning-without-gai-with-python.ipynb

Select the notebook that appears in the list of files on the left.

Downloading the notebook

Right click one of these links: Jupyter Notebook (With Solutions), Jupyter Notebook (Without Solutions)

Save Link As..

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Key points

To be added

Frequently Asked Questions

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Citing this Tutorial

Ralf Gabriels, Deep Learning (without Generative Artificial Intelligence) using Python (Galaxy Training Materials). https://training.galaxyproject.org/training-material/topics/statistics/tutorials/deep-learning-without-gai-with-python/tutorial.html Online; accessed TODAY
Hiltemann, Saskia, Rasche, Helena et al., 2023 Galaxy Training: A Powerful Framework for Teaching! PLOS Computational Biology 10.1371/journal.pcbi.1010752
Batut et al., 2018 Community-Driven Data Analysis Training for Biology Cell Systems 10.1016/j.cels.2018.05.012

@misc{statistics-deep-learning-without-gai-with-python,
author = "Ralf Gabriels",
	title = "Deep Learning (without Generative Artificial Intelligence) using Python (Galaxy Training Materials)",
	year = "",
	month = "",
	day = "",
	url = "\url{https://training.galaxyproject.org/training-material/topics/statistics/tutorials/deep-learning-without-gai-with-python/tutorial.html}",
	note = "[Online; accessed TODAY]"
}
@article{Hiltemann_2023,
	doi = {10.1371/journal.pcbi.1010752},
	url = {https://doi.org/10.1371%2Fjournal.pcbi.1010752},
	year = 2023,
	month = {jan},
	publisher = {Public Library of Science ({PLoS})},
	volume = {19},
	number = {1},
	pages = {e1010752},
	author = {Saskia Hiltemann and Helena Rasche and Simon Gladman and Hans-Rudolf Hotz and Delphine Larivi{\`{e}}re and Daniel Blankenberg and Pratik D. Jagtap and Thomas Wollmann and Anthony Bretaudeau and Nadia Gou{\'{e}} and Timothy J. Griffin and Coline Royaux and Yvan Le Bras and Subina Mehta and Anna Syme and Frederik Coppens and Bert Droesbeke and Nicola Soranzo and Wendi Bacon and Fotis Psomopoulos and Crist{\'{o}}bal Gallardo-Alba and John Davis and Melanie Christine Föll and Matthias Fahrner and Maria A. Doyle and Beatriz Serrano-Solano and Anne Claire Fouilloux and Peter van Heusden and Wolfgang Maier and Dave Clements and Florian Heyl and Björn Grüning and B{\'{e}}r{\'{e}}nice Batut and},
	editor = {Francis Ouellette},
	title = {Galaxy Training: A powerful framework for teaching!},
	journal = {PLoS Comput Biol}
}

                   

Congratulations on successfully completing this tutorial!

You can use Ephemeris's shed-tools install command to install the tools used in this tutorial.
shed-tools install [-g GALAXY] [-a API_KEY] -t <(curl https://training.galaxyproject.org/training-material/api/topics/statistics/tutorials/deep-learning-without-gai-with-python/tutorial.json | jq .admin_install_yaml -r)
Alternatively you can copy and paste the following YAML
---
install_tool_dependencies: true
install_repository_dependencies: true
install_resolver_dependencies: true
tools: []

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