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<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.1//EN" "http://www.w3.org/TR/xhtml11/DTD/xhtml11.dtd">
<html xmlns="http://www.w3.org/1999/xhtml" lang="en-gb" xml:lang="en-gb">
<head>
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<meta name="style" content="cs" />
<title>University of Bristol - Computer Science Department - COMSM0045 - Applied Deep Learning</title>
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<body>
<div id="wrap">
<a href="https://www.bris.ac.uk/unit-programme-catalogue/UnitDetails.jsa?unitCode=COMSM0045" target="_blank">UNIT INFO</a>
<h1>COMSM0045 - Applied Deep Learning</h1>
<img src="comsm0018.jpg" alt="ADL Banner" width="900"></img>
<link rel="stylesheet" href="simple.css" />
<a id="info"></a>
<hr/>
<h2>Unit Information</h2>
<p>Welcome to COMSM0045. The unit introduces the students to deep
architectures for learning linear and non-linear transformations of big
data towards tasks such as classification and regression. The unit paves
the path from understanding the fundamentals of convolutional and
recurrent neural networks through to training and optimisation as well as
evaluation of learnt outcomes. The unit's approach is hands-on, focusing
on the 'how-to' while covering the basic theoretical foundations. For
further general information, see
<a href="https://www.bris.ac.uk/unit-programme-catalogue/UnitDetails.jsa?unitCode=COMSM0045" target="_blank">
the syllabus for the unit</a>.
</p>
<b>UPDATE - 29/08/2024 Welcome new students for the 24/25 year!</b>
<b> PLEASE NOTE: lecture content will be updated, slides below are placeholder and may change until the lecture</b>
If you have any questions, head to the <a href = "https://teams.microsoft.com/l/team/19%3ALWXnse6nfuQqi6JCGIJkA_LL9E4IATzuWivU5VQQqNo1%40thread.tacv2/conversations?groupId=c1bcad56-70dc-4e9b-ae72-1aa0f583695d&tenantId=b2e47f30-cd7d-4a4e-a5da-b18cf1a4151b">unit teams</a>.
<hr/>
<h2> Staff</h2>
<table>
<tr><td class="mike"><a href="http://mwray.github.io" target="_blank">Michael Wray (MW)</a></td><td><b>Unit Director</b></td></tr>
<tr><td class="tilo"><a href="http://www.cs.bris.ac.uk/~burghard" target="_blank">Tilo Burghardt (TB)</a></td><td></td></tr>
</table>
<hr/>
<h2> Teaching Assistants</h2>
<p>Jacob Chalk (JC), Omar Emara (OE), Rhdori Guerrier (RG), Sam Pollard (SP), Saptarshi Sinha (SS), Siddhant Bansal (SB), Zhifan Zhu (ZZ) </p>
<a id="materials"></a>
<hr/>
<h2>Unit Materials</h2>
<table class="blank">
<tr class="blank">
<td><i>Wks</i></td> <td><i>Monday 14:00-16:00</i></td> <td><i>Tuesday 09:00-12:00</i></td> <td><i>Labs</i></td>
</tr>
<tr>
<td class="blank">1</td>
<td class="tilo">
<b>16/09/2024 - 14:00 - Queens BLDG 1.07</b><br/>
Wk1 - LECTURE 1<br/>
<b>INTRODUCTION TO THE UNIT</b><br/>
<a href="https://uob-my.sharepoint.com/:p:/g/personal/mw1760_bristol_ac_uk/Efda8SEXnDlBlpTqEsgUDRkBxmiTdBZGysDzG5lZA9x7Fw?e=zr0wmJ" target="_blank">intro PDF</a> <br/><hr/>
<b>BASICS OF ARTIFICIAL NEURAL NETWORKS</b><br/>(Queens Building 1.07, in-person)<br/>
(Introduction, Neural Networks, Perceptron, Cost Functions, Gradient Descent, Delta Rule, Deep Networks)<br/>
<a href="https://www.ole.bris.ac.uk/bbcswebdav/courses/COMSM0045_2022_TB-1/content/COMSM0045_01.pdf" target="_blank">PDF Slides</a>, <a href="https://mediasite.bris.ac.uk/Mediasite/Play/fbd084a2bca141c5a663fa076c54f4f91d" target="_blank">Recording</a>
</td>
<td class="tilo">
<b>17/09/2024 - 09:00 - MVB 1.15</b><br/>
Wk1 - LECTURE 2<br/>
<b>TOWARDS TRAINING DEEP FORWARD NETWORKS</b><br/>(MVB 1.15, in-person + lecture recap)<br/>
(Network Representation, Computational Graphs, Reverse Auto-Differentiation)<br/>
<a href="https://www.ole.bris.ac.uk/bbcswebdav/courses/COMSM0045_2022_TB-1/content/COMSM0045_02.pdf" target="_blank">PDF Slides</a>,
<a href="https://mediasite.bris.ac.uk/Mediasite/Play/4db6e3234d314a1aa2935d71c02a0e071d" target="_blank">Extra Recap Recording Lecture 2 Refresher (first part of video)</a>
</td>
<td><b>GETTING STARTED:</b><br/>
<hr/><a href="#bc4" target="_blank">Register Individually on BlueCrystal4<br/>(details see below)</a><hr/> <b>RECAP WORKSHEETS:</b><br/>-<a href="https://github.com/COMSM0045-Applied-Deep-Learning/labsheets">Lab0 - Python (Homework)</a>
</td> </tr> <tr>
<td class="blank">2</td>
<td class="tilo">
<b>23/09/2024 - 14:00 - Queens BLDG 1.07</b><br/>
Wk2 - LECTURE 3<br/>
<b>BACKPROPAGATION ALGORITHM</b><br/>(Queens Building 1.07, in-person + recorded lecture)<br/>
(The Backpropagation Algorithm in Full Detail, Activation Functions)<br/>
<a href="https://www.ole.bris.ac.uk/bbcswebdav/courses/COMSM0045_2022_TB-1/content/COMSM0045_03.pdf" target="_blank">PDF Slides</a><br/>
<a href="https://mediasite.bris.ac.uk/Mediasite/Play/4db6e3234d314a1aa2935d71c02a0e071d" target="_blank">Extra Recap Recording (second part of video)</a><hr/>
Wk2 - LECTURE 4<br/>
<b>OPTIMISATION TECHNIQUES</b><br/>(Queens Building 1.07, in-person + recorded lecture)<br/>
(Stochastic Gradient Descent, Nesterov Momentum, RMSProp, Newton's Method, AdaGrad, Adam, Saddle Points)<br/>
<a href="https://www.ole.bris.ac.uk/bbcswebdav/courses/COMSM0045_2022_TB-1/content/COMSM0045_04.pdf" target="_blank">PDF Slides</a><br/>
<a href="https://mediasite.bris.ac.uk/Mediasite/Play/aa25459da02e430a8c99e3cfd5082a371d" target="_blank">Extra Recap Recording</a><hr/>
</td>
<td class="mike">
<b>24/09/2024 - 09:00 - MVB 1.15 - </b><br/>PRACTICAL 1 (<a href="https://uob-my.sharepoint.com/:p:/g/personal/mw1760_bristol_ac_uk/ESjuN3OMOZpCiB5yEexp1QkBcTLdcN3uJdtJ3S5TV0s8lA?e=dCdHG3">Slides</a>, <a href="https://bristol-ac-uk.zoom.us/rec/share/5Hu33OuqmW-vc01-gdetm4ZHnc0kDT5GJC53oct7IkRJZ-FhRELWvQM2NsmOimri.UQu-5LfU6A5ZBS3h">Recording</a>)<br/>
Your first fully connected layer<br/>
gradient descent<br/>
stochastic gradient descent
</td>
<td class="labs">24/09/2024, (MVB 1.15) - 3hrs<br/>-<a href="https://github.com/COMSM0045-Applied-Deep-Learning/labsheets/blob/master/lab-1-dnns/bc4-setup.ipynb">BC4 Setup</a>
<hr/>
<a href="https://github.com/COMSM0045-Applied-Deep-Learning/labsheets"><b>Lab 1</b> - Training your first Deep Neural Network</a>
</td>
</tr>
<tr>
<td class="blank" rowspan="2">3</td>
<td class="tilo">
<b>30/09/2024 - 09:00 - Queens BLDG 1.07</b><br/>
Wk3 - LECTURE 5<br/>
<b>COST FUNCTIONS, REGULARISATION AND DEPTH</b><br/>
(Queens Building 1.07, in-person + recorded lecture)<br/>
(SoftMax, Cross Entropy, L1 and L2 Regularisation, DropOut, DropConnect, Depth Considerations)<br/>
<a href="https://www.ole.bris.ac.uk/bbcswebdav/courses/COMSM0045_2022_TB-1/content/COMSM0045_05.pdf" target="_blank">PDF Slides</a><br/>
<a href="https://mediasite.bris.ac.uk/Mediasite/Play/9fd25a71068443d18c1689759d3e79f31d" target="_blank">Extra Recap Recording</a>
</td>
<td class="mike" rowspan="2">
<b>01/10/2024 - 09:00 - MVB 1.15 - </b><br/>PRACTICAL 2 (<a href="https://uob-my.sharepoint.com/:p:/g/personal/mw1760_bristol_ac_uk/ETwlrUQAeH5Kv-LlFYtfdkEBAt_7_7jfmx7a5YcLGFdv6A?e=81Lrkh">Slides</a>,
<a target="_blank" href="https://uob-my.sharepoint.com/:v:/g/personal/mw1760_bristol_ac_uk/EQ_8_RMg0ixNms9R__UZ3IABtIMC2A3vBLyP385n12t1PQ?e=suG508&nav=eyJyZWZlcnJhbEluZm8iOnsicmVmZXJyYWxBcHAiOiJTdHJlYW1XZWJBcHAiLCJyZWZlcnJhbFZpZXciOiJTaGFyZURpYWxvZy1MaW5rIiwicmVmZXJyYWxBcHBQbGF0Zm9ybSI6IldlYiIsInJlZmVycmFsTW9kZSI6InZpZXcifX0%3D">Recording</a>). <br/>
Your first convolutional connected layer
</td>
<td class="labs" rowspan="2">01/10/2024, (MVB 1.15) - 3hr<br/> <a href="https://github.com/COMSM0045-Applied-Deep-Learning/labsheets"><b>Lab 2</b> - Your First Convolutional Connected Network</a></td>
</tr>
<tr>
<td class="mike">
Wk3 - LECTURE 6<br/><b>CONVOLUTIONAL NEURAL NETWORKS</b><br/>(Queens Building 1.07, in-person + recorded lecture)<br/>
(sharing parameters, conv layers, pooling, CNN architectures)<br/>
(<a target="_blank" href="https://uob-my.sharepoint.com/:p:/g/personal/mw1760_bristol_ac_uk/EWIfh6PHhUhDgsO-XUh9JZABh0u_4HIG0oFIdEnUt2NBdg?e=0bd5ro">Slides</a>, <a target="_blank" href="https://uob-my.sharepoint.com/:v:/g/personal/mw1760_bristol_ac_uk/EeBv6uvIR0RKpvOiTBt5JG4BtrkjPCxKiqz8hVQQLgBUzQ?nav=eyJyZWZlcnJhbEluZm8iOnsicmVmZXJyYWxBcHAiOiJPbmVEcml2ZUZvckJ1c2luZXNzIiwicmVmZXJyYWxBcHBQbGF0Zm9ybSI6IldlYiIsInJlZmVycmFsTW9kZSI6InZpZXciLCJyZWZlcnJhbFZpZXciOiJNeUZpbGVzTGlua0NvcHkifX0&e=DDS4Kc">Recording</a>)
</td>
</tr>
<tr>
<td class="blank">4</td> <td class="blank">-</td>
<td class="mike">
<b>08/10/2024 - 09:00 - MVB 1.15 - </b>
<br/>PRACTICAL 3 (<a href="https://uob-my.sharepoint.com/:p:/g/personal/mw1760_bristol_ac_uk/ET_rzh23oBZOhJldiX4_LsEBs0bj6Vm7qdphP1V3IQ8Maw?e=CQeggT">Slides</a>, <a href="https://uob-my.sharepoint.com/:v:/g/personal/mw1760_bristol_ac_uk/EVF6K4-tk31PlfS1Udi4c_UBECxsBof3dijEXRbZjCErPA?nav=eyJyZWZlcnJhbEluZm8iOnsicmVmZXJyYWxBcHAiOiJTdHJlYW1XZWJBcHAiLCJyZWZlcnJhbFZpZXciOiJTaGFyZURpYWxvZy1MaW5rIiwicmVmZXJyYWxBcHBQbGF0Zm9ybSI6IldlYiIsInJlZmVycmFsTW9kZSI6InZpZXcifX0%3D&e=mg7NL3">Video Recording</a>).<br/>
Error rate monitoring (training/validation/testing)<br/>
Batch-based training<br/>
Learning rate<br/>
Weight Freezing<br/>
Batch normalisation<br/>
Parameter intialisation</td>
<!-- <a href="https://www.ole.bris.ac.uk/bbcswebdav/courses/COMSM0045_2018/content/COMSM0045_Practical2_handout.pdf" target="_blank">slides</a>-->
<td class="labs">08/10/2024, (MVB 1.15) - 3hr<br/><br/><a href="https://github.com/COMSM0045-Applied-Deep-Learning/labsheets"><b>Lab 3</b> - Hyperparameters</a></td> </tr>
<tr> <td class="blank">5</td>
<td class="mike">
<b>14/10/2024 - 09:00 - Queens BLDG 1.07</b><br/>
Wk5 - LECTURE 7 <br/>
<b>RECURRENT and RELATIONAL NEURAL NETWORKS</b><br/>(Queens Building 1.07, in-person + Recorded) <br/>
(RNN, encoder-decoder, Transformers)<br/>
(<a href="https://uob-my.sharepoint.com/:p:/g/personal/mw1760_bristol_ac_uk/EbV0hYanhp1AjBmbLRLd6zsBseZgq2ZQrQl0yGZ2lPu1lQ?e=0xkWO4">Slides</a>, <a href="https://mediasite.bris.ac.uk/Mediasite/Play/a4bc670591f346f6850902c2107b8eef1d">Video Pt. 1</a>)
<hr/>
Wk5 - LECTURE 8 <br/>
(Queens Building 1.07, in-person + Recorded) <br/><b>GENERATIVE MODELS</b><br/>
(Autoregressive models)<br/>
(<a href="https://uob-my.sharepoint.com/:p:/g/personal/mw1760_bristol_ac_uk/EQOaxoUpHPJCrOjtK3dGqfkBrIQbhbp-2B1ZouRv64klLw?e=tM808v">Slides</a>, <a href="https://mediasite.bris.ac.uk/Mediasite/Play/a4bc670591f346f6850902c2107b8eef1d">Video pt. 1</a>, <a href="https://uob-my.sharepoint.com/:v:/g/personal/mw1760_bristol_ac_uk/ESrzWRq8r2xCl4jhMOnnf0MBYAchL-SNK4bLuHIUrqP8cA?e=kTJIUw">Video pt. 2</a>)
</td>
<td class="mike">
<b>15/10/2024 - 9am - MVB 1.15 - </b><br/>PRACTICAL 4 <a href="https://uob-my.sharepoint.com/:p:/g/personal/mw1760_bristol_ac_uk/EXz-UJdhzQFGprWvn_90cRYBXhh9DTtlvLIj61CSpOs2KQ?e=rPTTv5">(Slides)</a>.<br/> Data Augmentation<br/>Debugging strategies<br/>
Dropout
<br/>
(<a href="https://uob-my.sharepoint.com/:v:/g/personal/mw1760_bristol_ac_uk/ESrzWRq8r2xCl4jhMOnnf0MBYAchL-SNK4bLuHIUrqP8cA?e=kTJIUw">Video Recording</a>)<br/>
<br/>
</td>
<td class="labs">15/10/2024, (MVB 1.15) - 3hr<br/><br/><a href="https://github.com/COMSM0045-Applied-Deep-Learning/labsheets"><b>Lab 4</b> - Data Augmentation</a></td></tr>
<tr> <td class="blank">6</td> <td class="blank" colspan="3">READING WEEK - Mid Term for MAJOR unit students 25/10/2024 - MVB - 1.07 - 09:30-10:30 </td></tr>
<tr>
<td class="blank">7</td>
<td class="blank">-</td>
<td class="mike">
<b>29/10/2024 - 09:00 - MVB 1.15 - </b><br/>PRACTICAL 5 (<a href="https://uob-my.sharepoint.com/:p:/g/personal/mw1760_bristol_ac_uk/EUFxZEfdog1LoTuKqK4mYuoBDx4qimnhQSXcXHJVsTGR_g?e=usXOr">Slides</a>).<br/> Transformers<br/>
<!--, <a href="https://mediasite.bris.ac.uk/Mediasite/MyMediasite/presentations/145760ee91944d0ea78f37c8b2f92bc01d">Video Recording</a>--><br/>
</td>
<td class="labs">29/10/2024, (MVB 1.15) - 3hr<br/><a href="https://github.com/COMSM0045-Applied-Deep-Learning/labsheets"><b>Lab 5</b> - Transformers</a></td>
</tr>
<tr>
<td class="blank">8</td>
<td class="blank">-</td>
<td class="mike"><b>05/11/2024 - 09:00 - MVB 1.15 - </b><br/>
Continuation Lab<br/> </td>
<td class="labs">05/11/2024, (MVB 1.15) - 3hr<br/><br/> </td>
</tr>
<tr> <td class="blank">9</td> <td class="blank">-</td> <td class="mike"><b>12/11/2024, 10:00 [2 hours], (MVB 1.15) - </b><br/>CW Support Session</td>
<td class="blank">-</td></tr>
<tr>
<td class="blank">10</td> <td class="blank">-</td> <td class="mike"><b>19/11/2024, 10:00 [2 hours], (MVB 1.15) - </b><br/>CW Support Session</td>
<td class="blank">-</td>
</tr>
<tr>
<td class="blank">11</td> <td class="blank">-</td> <td class="mike"><b>26/11/2024, 10:00 [2 hours], (MVB 1.15) - </b><br/>CW Support Session</td>
<td class="blank">-</td>
</tr>
<tr> <td class="blank">12</td> <td class="blank">-</td> <td class="mike"><b>03/12/2024, MVB 1.15 - </b><br/>Exam Support Session</td><td class="blank">-</td></tr>
<tr> <td class="blank">13</td> <td class="blank" colspan="3">DECEMBER EXAMS - Final for MINOR unit students </td></tr>
</table>
<hr/>
<h2>Assessment Details</h2>
<ul>
<li><b>Coursework (for Major option): </b> You are requested to re-implement a paper to be specified, and provide your code as well as a final report. Coursework completed in groups (up to 3)</li>
<li><b>Mid-term test (for Major option): </b> The theoretical components of the unit up to week 5 are examinable. No code would be written in the exam, but code can be provided to answer questions. Calculators are allowed.</li>
<li><b>Exam (for Minor option): </b> Both the theoretical and practical components of the unit are examinable. No code would be written in the exam, but code can be provided to answer questions. Calculators are allowed.</li>
</ul>
<p> </p>
<hr/>
<h2>Assessment Details - Coursework</h2>
<p>The coursework has been released, information can be found <a href="https://uob-my.sharepoint.com/:w:/g/personal/mw1760_bristol_ac_uk/EXOwIUeYHPVGrjhJhKJ2sAoBMxarcz9ksWrf0pLzweavig?e=xgKW1w">here</a> </p>
<p>Note that the deadline is the 29th November at 13:00 </p>
<p><a href="https://forms.office.com/e/3DYE6Qm6Z4">Form for Sign-Ups</a>. Even if you plan to work solo, please fill in this form. </p>
<p> If you need help, ask on the unit teams and/or come to the weekly CW support sessions. </p>
<hr/>
<h2>Assessment Details - Exam</h2>
More details regarding the exam and in-class test coming soon.
You can find previous papers <a href="https://uob-my.sharepoint.com/:f:/g/personal/mw1760_bristol_ac_uk/EmldHGtc0MBLoUErTEubMj0BFDlCQzibjgjMNW40uAcMug?e=U2vkpb">here</a>, but please note that these were from when the unit only contained one 2 hour exam.
<!--<p> We have now released the exam preparation materials on the unit webpage, you can also find these in the folder <a href="https://uob-my.sharepoint.com/:f:/g/personal/mw1760_bristol_ac_uk/EmldHGtc0MBLoUErTEubMj0BFDlCQzibjgjMNW40uAcMug?e=U2vkpb">here</a>.</p>
<p> This includes a list of exam topics, 3 past papers (1 with answers). </p>-->
<p> Please note that you cannot take notes into the exam, but calculators are permitted.</p>
<hr/>
<h2>Github</h2>
<p>All technical resources will be posted on the
<a href="https://github.com/COMSM0045-Applied-Deep-Learning" target="_blank">COMSM0045 ADL Github organisation</a>. If you find any issues, please kindly raise an issue in the respective repository.
</p>
<hr/>
<h2>Textbook</h2>
<p>Recommended Reading:<br/><a href="http://www.deeplearningbook.org/" target="_blank">Goodfellow et al (2016). Deep Learning. MIT Press</a></p>
<hr/>
<h2><a id="bc4">Blue Crystal 4 Registration [only applicable for Bristol undergraduate students with corresponding email]</a></h2>
<p>All students must apply online to register an account on BC4 for this
unit. This also applies to students who already have accounts on BC4 for other units (e.g. HPC), in this case you must
register again using the instructions below.</p>
<ol>
<li>Click on: <a href="https://www.acrc.bris.ac.uk/login-area/apply.cgi" target="_blank">https://www.acrc.bris.ac.uk/login-area/apply.cgi</a></li>
<li>Enter your personal details</li>
<li>Choose: "Join an existing project"</li>
<li>Enter project code: COMS033444</li>
<li>Keep Preferred log-in shell as bash</li>
<li>In the comments box please enter the following:</li>
"I am on the taught course Applied Deep Learning (COMSM0045), unit director Michael Wray, and will need access to BC4"
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<p>Note that it takes up to 48 hours to enable your account on BC4.</p>
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