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<!DOCTYPE html>
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<h1>Introduction to deep learning: Carpentries-style hands-on lesson material for introducing researchers to deep learning</h1>
<p>Last updated on 2024-12-03 |
<a href="https://github.com/carpentries-incubator/deep-learning-intro/edit/main/paper.md" class="external-link">Edit this page <i aria-hidden="true" data-feather="edit"></i></a></p>
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<div class="section level1">
<h1 id="summary">Summary<a class="anchor" aria-label="anchor" href="#summary"></a></h1>
<p>This article describes a hands-on introduction to the first steps in
deep learning, intended for researchers who are familiar with (non-deep)
machine learning.</p>
<p>The use of deep learning has seen a sharp increase in popularity and
applicability over the last decade. While deep learning can be a useful
tool for researchers from a wide range of domains, taking the first
steps in the world of deep learning can be somewhat intimidating. This
introduction aims to cover the fundamentals of deep learning in a
practical and hands-on manner. By the end of the course, students will
be able to train their first neural network and understand the
subsequent steps needed to improve the model.</p>
<p>The lesson starts by explaining the basic concepts of neural
networks, and then guides learners through the different steps of a deep
learning workflow.<br>
After following this lesson, learners will be able to prepare data for
deep learning, implement a basic deep learning model in Python with
Keras, monitor and troubleshoot the training process, and implement
different layer types, such as convolutional layers.</p>
</div>
<div class="section level1">
<h1 id="statement-of-need">Statement of Need<a class="anchor" aria-label="anchor" href="#statement-of-need"></a></h1>
<p>There are many free online course materials on deep learning, see for
example: <span class="citation">@noauthor_fastai_nodate</span>; <span class="citation">@noauthor_udemy_nodate</span>; <span class="citation">@noauthor_udemy_nodate-1</span>; <span class="citation">@noauthor_udemy_nodate-2</span>; <span class="citation">@noauthor_coursera_nodate</span>; <span class="citation">@noauthor_freecodecamporg_2022</span>.</p>
<p>Nonetheless, these resources are often not available open-source and
can thus not be easily adapted to the students’ needs. Also, these
resources are intended to use for self-study. Our material can be used
for self-study, but it is primarily developed for instructors to use in
a workshop. In addition, although a diverse range of online courses
already exists, few are targeted towards academic researchers.</p>
<p>Many computing centers offer (local) deep learning courses, such as
<span class="citation">@noauthor_csc-_nodate</span>. But the lesson
material, if it is available, is not easily adopted outside the course
organisation.</p>
<p>What works well for learners is to both make them familiar with the
key concepts, and also let them practice with how to implement it.
Eventually resulting in an increase in confidence and the conviction
that ‘I can do this myself’. The key to getting there is live coding:
before the course, learners have to setup a working environment on their
own computer. During the course, learners type in the commands that are
explained by the instructor on their own computer. This design is based
on the Software Carpentry <span class="citation">[@wilson_software_2006]</span> philosophy. Live coding
ensures that learners master the programmatic implementation of deep
learning at the end of the course. We believe that this makes our lesson
a unique and crucial resource.</p>
<p>Researchers can often only free a limited amount of time (maximum 5
consecutive days), since they are so involved in their daily work. To
accomplish this, we created a lesson that can be taught in 2 consecutive
days or 4 half days.</p>
<p>Demand for our workshops and feedback gathered from students
demonstrated the need for a low-threshold lesson that lets researchers
take the first steps in the field of deep learning. This impression was
validated by other instructors who taught the lesson independently to
their own audiences and provided us with feedback on their
experience.</p>
</div>
<div class="section level1">
<h1 id="instructional-design">Instructional design<a class="anchor" aria-label="anchor" href="#instructional-design"></a></h1>
<p>This lesson material was designed using the concepts from The
Carpentries Curriculum Development Handbook <span class="citation">[@becker_carpentries_nodate]</span>. Most importantly,
we used ‘backward design’: we started with identifying learning
objectives, the core skills and concepts that learners should acquire as
a result of the lesson. Next, exercises were designed to assess whether
these objectives are met. Eventually, the content is written to teach
the skills and concepts learners need to successfully complete the
exercises and, it follows, meet the learning objectives.</p>
<p>Live coding is central to this approach: the lesson is built up of
small blocks. In each block first the instructor demonstrates how to do
something, and students follow along on their own computer. Then, the
students work independently on exercises individually or in groups to
test their skills. This approach integrates opportunities for guided
practice throughout the lesson, promoting learning by helping learners
build up a functioning mental model of the domain and transfer new
knowledge from working memory to long-term memory. This is in accordance
with research-based successful teaching strategies <span class="citation">[@lang_small_2021]</span>.</p>
<p>The lesson material is built in the new lesson template: Carpentries
Workbench <span class="citation">[@noauthor_carpentries_nodate]</span>.
This makes the lesson material a complete self-study resource. But it
also serves as lesson material for the instructor teaching the lesson
through live-coding, in that case the lesson material is only shared
with students after the workshop as a reference. The lesson material can
be toggled to the ‘instructor view’. This allows to provide instructor
notes on how to approach teaching the lesson, and these can even be
included at the level of the lesson content. In addition, the
Carpentries Workbench prioritises accessibility of the content, for
example by having clearly visible figure captions and promoting
alt-texts for pictures.</p>
<p>The lesson is split into a general introduction, and 3 episodes that
cover 3 distinct increasingly more complex deep learning problems. Each
of the deep learning problems is approached using the same 10-step deep
learning workflow (<a href="https://carpentries-incubator.github.io/deep-learning-intro/1-introduction.html#deep-learning-workflow" class="external-link uri">https://carpentries-incubator.github.io/deep-learning-intro/1-introduction.html#deep-learning-workflow</a>).
By going through the deep learning cycle three times with different
problems, learners become increasingly confident in applying this deep
learning workflow to their own projects.</p>
</div>
<div class="section level1">
<h1 id="feedback">Feedback<a class="anchor" aria-label="anchor" href="#feedback"></a></h1>
<p>This course was taught 12 times over the course of 3 years, both
online and in-person, by the Netherlands eScience Center (Netherlands,
<a href="https://www.esciencecenter.nl/" class="external-link uri">https://www.esciencecenter.nl/</a>) and Helmholz-Zentrum
Dresden-Rossendorf (Germany, <a href="https://www.hzdr.de/" class="external-link uri">https://www.hzdr.de/</a>). Apart from the core group of
contributors, the workshop was also taught at 3 independent institutes,
namely: University of Wisconson-Madison (US, <a href="https://www.wisc.edu/" class="external-link uri">https://www.wisc.edu/</a>),
University of Auckland (New Zealand, <a href="https://www.auckland.ac.nz/" class="external-link uri">https://www.auckland.ac.nz/</a>), and EMBL Heidelberg
(Germany, <a href="https://www.embl.org/sites/heidelberg/" class="external-link uri">https://www.embl.org/sites/heidelberg/</a>). In general,
adoption of the lesson material by the instructors not involved in the
project went well. The feedback gathered from our own and others’
teachings was used to polish the lesson further.</p>
<div class="section level2">
<h2 id="student-responses">Student responses<a class="anchor" aria-label="anchor" href="#student-responses"></a></h2>
<p>The feedback we gathered from students is in general very positive,
with some responses from students to the question ‘What was your
favourite or most useful part of the workshop. Why?’ further confirming
our statement of need:</p>
<blockquote>
<p><em>I enjoyed the live coding and playing with the models to see how
it would effect the results. It felt hands on and made it easy for me to
understand the concepts.</em></p>
</blockquote>
<blockquote>
<p><em>Well-defined steps to be followed in training a model is very
useful. Examples we worked on are quite nice.</em></p>
</blockquote>
<blockquote>
<p><em>The doing part, that really helps to get the theory into
practice.</em></p>
</blockquote>
<p>Below are two tables summarizing results from our post-workshop
survey. We use the students’ feedback to continuously improve the
lesson.</p>
<table class="table"><colgroup><col width="39%"><col width="13%"><col width="7%"><col width="7%"><col width="5%"><col width="9%"><col width="5%"><col width="10%"></colgroup><thead><tr class="header"><th></th>
<th>STRONGLY DISAGREE</th>
<th>DISAGREE</th>
<th>UNDECIDED</th>
<th>AGREE</th>
<th>STRONGLY AGREE</th>
<th>TOTAL</th>
<th>WEIGHTED AVERAGE</th>
</tr></thead><tbody><tr class="odd"><td>I can immediately apply what I learned at this workshop.</td>
<td>0</td>
<td>5</td>
<td>6</td>
<td>19</td>
<td>8</td>
<td>38</td>
<td>3,8</td>
</tr><tr class="even"><td>The setup and installation instructions for the lesson were complete
and easy to follow.</td>
<td>0</td>
<td>0</td>
<td>4</td>
<td>13</td>
<td>21</td>
<td>38</td>
<td>4,4</td>
</tr><tr class="odd"><td>Examples and tasks in the lesson were relevant and authentic</td>
<td>0</td>
<td>0</td>
<td>5</td>
<td>19</td>
<td>14</td>
<td>38</td>
<td>4,2</td>
</tr></tbody></table><p>Table 1: Agreement on statements by students from 2 workshops taught
at the Netherlands eScience Center. The results from these 2 workshops
are a good representation of the general feedback we get when teaching
this workshop.</p>
<table style="width:100%;" class="table"><colgroup><col width="36%"><col width="8%"><col width="6%"><col width="6%"><col width="8%"><col width="8%"><col width="5%"><col width="6%"><col width="12%"></colgroup><thead><tr class="header"><th></th>
<th>POOR</th>
<th>FAIR</th>
<th>GOOD</th>
<th>VERY GOOD</th>
<th>EXCELLENT</th>
<th>N/A</th>
<th>TOTAL</th>
<th>WEIGHTED AVERAGE</th>
</tr></thead><tbody><tr class="odd"><td>Introduction into Deep Learning</td>
<td>0 (0%)</td>
<td>2 (5%)</td>
<td>10 (27%)</td>
<td>8 (22%)</td>
<td>17 (46%)</td>
<td>0 (0%)</td>
<td>37</td>
<td>4,1</td>
</tr><tr class="even"><td>Classification by a Neural Network using Keras (penguins
dataset)</td>
<td>0 (0%)</td>
<td>1 (3%)</td>
<td>5 (13%)</td>
<td>16 (42%)</td>
<td>16 (42%)</td>
<td>0 (0%)</td>
<td>38</td>
<td>4,2</td>
</tr><tr class="odd"><td>Monitoring and Troubleshooting the learning process (weather
dataset)</td>
<td>0 (0%)</td>
<td>0 (0%)</td>
<td>4 (11%)</td>
<td>18 (47%)</td>
<td>16 (42%)</td>
<td>0 (0%)</td>
<td>38</td>
<td>4,3</td>
</tr><tr class="even"><td>Advanced layer types (CIFAR-10 dataset)</td>
<td>0 (0%)</td>
<td>2 (5%)</td>
<td>5 (13%)</td>
<td>7 (18%)</td>
<td>16 (42%)</td>
<td>8 (21%)</td>
<td>38</td>
<td>4,2</td>
</tr></tbody></table><p>Table 2: Quality of the different episodes of the workshop as rated
by students from 2 workshops taught at the Netherlands eScience Center.
The results from these 2 workshops are a good representation of the
general feedback we get when teaching this workshop.</p>
</div>
</div>
<div class="section level1">
<h1 id="conclusion">Conclusion<a class="anchor" aria-label="anchor" href="#conclusion"></a></h1>
<p>This lesson can be taught as a stand-alone workshop to students
already familiar with machine learning and Python. It can also be taught
in a broader curriculum after an introduction to Python programming (for
example: <span class="citation">@azalee_bostroem_software_2016</span>)
and an introduction to machine learning (for example: <span class="citation">@noauthor_scikit-learn_2023</span>). Concluding, the
described lesson material is a unique and essential resource aimed at
researchers and designed specifically for a live-coding teaching style.
Hopefully, it will help many researchers to set their first steps in a
successful application of deep learning to their own domain.</p>
</div>
<div class="section level1">
<h1 id="acknowledgements">Acknowledgements<a class="anchor" aria-label="anchor" href="#acknowledgements"></a></h1>
<p>We would like to thank all instructors and helpers that taught the
course, and the community of people that left contributions to the
project, no matter how big or small. Also, we thank Chris Endemann
(University of Wisconson-Madison, US, <a href="https://www.wisc.edu/" class="external-link uri">https://www.wisc.edu/</a>), Nidhi Gowdra (University of
Auckland, New Zealand, <a href="https://www.auckland.ac.nz/" class="external-link uri">https://www.auckland.ac.nz/</a>), ,Renato Alves and Lisanna
Paladin (EMBL Heidelberg, Germany, <a href="https://www.embl.org/sites/heidelberg/" class="external-link uri">https://www.embl.org/sites/heidelberg/</a>), that piloted
this workshop at their institutes. We thank the Carpentries for
providing such a great framework for developing this lesson material. We
thank all students enrolled in the workshops that were taught using this
lesson material for providing us with feedback.</p>
</div>
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