Différences entre les versions de « Deep Learning Tutorial 2019 »
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<center><h1><br />'''DSAI DEEP LEARNING TUTORIAL'''<br /><br /></h1></center> | <center><h1><br />'''DSAI DEEP LEARNING TUTORIAL'''<br /><br /></h1></center> | ||
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− | <td width='20% | + | <td width='20%'> [https://docs.google.com/forms/d/1GLQCjtki4If7fRcnEHgUju8MS-Gz4GLwp6aGutdNuqc/edit?ts=5cf64e9b Registration] </td> |
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− | <td width='20%'> [ | + | <td width='20%'> [http://icube-web.unistra.fr/dsai/index.php?title=Deep_Learning_Tutorial_2019_installations Installations] </td> |
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− | < | + | <center><div align="center" style="border:2px solid black; background:#fef6e7; color:blue; align:center; width:30%" > Registration deadline: July 7, 2019 <b style="color:red">Closed</b></div></center> |
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− | + | {| class="noborder" style="text-align:justify;" cellpadding="15" | |
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− | <p> | + | !colspan="2"| <b style="font-size:150%">Scope</b><hr> |
+ | |- | ||
+ | | valign="top" style="font-size:110%"|<p>The Data science and artificial intelligence (DSAI) research axis of ICube offers a two-day tutorial targeting researchers from all scientific backgrounds interested in the fast developing area of deep learning from a practical standpoint.<br></p><br> | ||
+ | <p>The purpose of this event is to help participants build up the basic skillset necessary for understanding, training and running deep neural networks that may benefit their respective research activities. A wide array of topics will be covered with a heavily hands-on approach - from fundamental concepts and programming skills to state-of-the-art deep learning libraries and models - through a series of practicals, given by experienced PhD students and researchers.<br></p><br> | ||
+ | <p>The tutorial will introduce participants to the libraries using Jupyter notebooks and help researchers desiring to apply DSAI approaches to analyze their data, such as images, videos, text, etc. ICube researchers with interest in deep learning are hereby encouraged to join the tutorial by subscribing via the registration link.<br></p><br> | ||
+ | || [[Image:Deeplearn.jpeg|550 px|right|''[[Artificial Intelligence]]'' ]] | ||
+ | [[Image:Conv.jpg|frameless|550 px|right|Convolutional Neural Networks]] | ||
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+ | !colspan="2"|<b style="font-size:150%">Participation Pre-requisites </b><hr> | ||
+ | |- | ||
+ | |colspan="2" valign="top" style="font-size:110%"| Participation is by registration. Limited space is available and the slots are given on a first come first serve basis. | ||
+ | <p>In addition, a participant needs the following:</p> | ||
+ | |||
+ | * A laptop | ||
+ | * Basic programming skill | ||
+ | * Deep learning libraries installable via the ''Installations'' menu | ||
+ | * Training dataset downloadable from the ''Installations'' menu | ||
+ | <p>If you have hitches in accessing any file or completing the necessary installations before the program days, keep on watching this space, a date will be announced later to resolve all preparatory installation issues. | ||
+ | <br /><br></p> | ||
+ | Help session date: ''Thursady 26th September, 2019''. Time: ''8:30AM - 8:59AM'' | ||
+ | |- | ||
+ | !colspan="2"|<br><b style="font-size:150%">Date & Venue </b><hr> | ||
+ | |- | ||
+ | |valign="top" align="center" style="font-size:120%; text-align:center; background-color:#F5F5F5"| | ||
+ | <p><b>Date :</b><br> Thursday 26th - Friday 27th September, 2019 </p><br> | ||
+ | <p><b>Time :</b><br> 9:00am - 5:30pm daily </p><br> | ||
+ | <p><b>Venue :</b><br> ICube Laboratory, 300 Bd Sébastien Brant, 67400 Illkirch-Graffenstaden<br>(<i>Room C218</i>).</p><p>[https://goo.gl/maps/XVoh1KzCHzGMekSm6 Google map direction]</p> | ||
+ | |style="background-color:#F5F5F5"| <gallery mode="packed-hover" heights="300">Image:icube.jpg|''[[ICube Laboratory]]'' </gallery> | ||
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− | + | <br><br> | |
+ | {|width="100%" style="text-align:justify; font-size:110%;" | ||
+ | |- | ||
+ | ! <b style="font-size:150%">Program</b><hr> | ||
+ | |- | ||
+ | |<p> The courses will run for 2 days. Participants will be able to attend both sessions. The detailed syllabus will be accessible via link on the menu bar. However, an abridge form is presented as follows:</p> | ||
+ | |} | ||
+ | {|class="wikitable" style="text-align:justify; font-size:110%; background:#fef6e7; margin-left:10px;" | ||
+ | |- | ||
+ | | colspan="3" cellspacing=0 align=center cellpadding=5px width=45% style="background:#fff; color:darkblue; border: 1.5px solid gray; font-size:120%"|Day 1 | ||
+ | | rowspan="9" style="background: white;"| | ||
+ | |colspan="3" cellspacing=0 align=center cellpadding=5px width=45% style="background:#fff; color:darkblue; border: 1.5px solid gray; font-size:120%"|Day 2 | ||
+ | |- | ||
+ | ! style="background:lightblue;color:black;text-align:center;font-width:bold;border-bottom:1.5px solid black" |Time | ||
+ | ! style="background:lightblue;color:black;font-width:bold;text-align:center;border-bottom:1.5px solid black" |Topic | ||
+ | ! style="background:lightblue;color:black;font-width:bold;text-align:center;border-bottom:1.5px solid black" |Speaker | ||
+ | ! style="background:lightblue;color:black;font-width:bold;text-align:center;border-bottom:1.5px solid black" |Time | ||
+ | ! style="background:lightblue;color:black;font-width:bold;text-align:center;border-bottom:1.5px solid black" |Topic | ||
+ | ! style="background:lightblue;color:black;font-width:bold;text-align:center;border-bottom:1.5px solid black" |Speaker | ||
+ | |- | ||
+ | | width=11% | 09:00 - 10:00 | ||
+ | | width=28% | Introduction to deep learning concepts | ||
+ | | width=10% | Nicolas Padoy | ||
+ | | width=11% | 09:00 - 11:00 | ||
+ | | width=28% | Introduction to TensorFlow | ||
+ | | width=10% | Armine Vardazaryan | ||
+ | |- | ||
+ | || 10:00 - 10:30 | ||
+ | || Coffee Break | ||
+ | || | ||
+ | || 11:00 - 11:30 | ||
+ | || Coffee Break | ||
+ | || | ||
+ | |- | ||
+ | ||10:30 - 12:30 | ||
+ | ||A primer on Python for deep learning | ||
+ | ||Tong Yu | ||
+ | ||11:30 - 12:30 | ||
+ | ||Inference with state-of-the-art models in TensorFlow (part 1) | ||
+ | ||Chinedu Nwoye | ||
+ | |- | ||
+ | ||12:30 - 14:00 | ||
+ | ||Break/Lunch | ||
+ | || | ||
+ | ||12:30 - 14:00 | ||
+ | ||Break/Lunch | ||
+ | || | ||
+ | |- | ||
+ | ||14:00 - 16:00 | ||
+ | ||Introduction to PyTorch | ||
+ | ||Vinkle Srivastav | ||
+ | ||14:00 - 15:00 | ||
+ | ||Inference with state-of-the-art models in TensorFlow (part 2) | ||
+ | ||Chinedu Nwoye | ||
+ | |- | ||
+ | ||16:00 - 16:30 | ||
+ | ||Coffee Break | ||
+ | || | ||
+ | ||15:00 - 15:30 | ||
+ | ||Coffee Break | ||
+ | || | ||
+ | |- | ||
+ | ||16:30 - 17:30 | ||
+ | ||Presentation of Unistra HPC | ||
+ | ||Vincent Lucas | ||
+ | ||15:30 - 17:30 | ||
+ | ||Generating images with GANs | ||
+ | ||Thomas Lampert | ||
+ | |- | ||
+ | |} | ||
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+ | <br><br><br><br> | ||
+ | <b style="font-size:150%">Speakers</b><hr> | ||
+ | <gallery style="text-align:center"> | ||
+ | Image:NP.jpg|<b>Nicolas Padoy</b> <br /> <i>Professor <br /> University of Strasbourg.</i><br>[http://camma.u-strasbg.fr/npadoy website] | ||
+ | File:Chinedu.jpg|<b>Chinedu Nwoye </b> <br /> <i>PhD Student<br/> University of Strasbourg.</i> <br>[https://cidsoft.com/nwoyecid website] | ||
+ | File:Tong.jpg|<b>Tong Yu </b> <br /> <i>PhD Student<br/> University of Strasbourg.</i> <br>[http://camma.u-strasbg.fr/people website] | ||
+ | File:Armine.jpg|<b>Armine Vardazaryan </b> <br /> <i>Research Engineer<br/> University of Strasbourg.</i> <br>[http://camma.u-strasbg.fr/people website] | ||
+ | File:Vinkle.png|<b>Vinkle Kumar Srivasta </b> <br /> <i>PhD Student<br/> University of Strasbourg.</i> <br>[http://camma.u-strasbg.fr/people website] | ||
+ | File:Thomas.jpg|<b>Thomas Lampert </b> <br /> <i>Post-doc Researcher<br/> University of Strasbourg.</i> <br>[https://sites.google.com/site/tomalampert website] | ||
+ | File:hpc.jpg|<b>Vincent Lucas </b> <br /> <i> HPC Representative<br/> University of Strasbourg.</i> <br>[https://services-numeriques.unistra.fr/les-services-aux-usagers/hpc.html website] | ||
+ | </gallery> | ||
+ | <br><br><br> | ||
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+ | <b style="font-size:150%">General Information</b><hr> | ||
+ | {|width="100%" align="center" valign="top" cellpadding="10" style="border-style:none; border-width: 40px border-color: #fefefe" | ||
+ | | valign="top"| <b><br><big>News</big></b><br> | ||
+ | * [ <i style='color:blue'>28/09/2019</i> ]: Tutorial ended successfully. Thanks for coming!. | ||
+ | * [ <i style='color:blue'>25/09/2019</i> ]: Links to download the codes, datasets, models and slides updated. | ||
+ | * [ <i style='color:blue'>05/09/2019</i> ]: Tutorial installation instructions uploaded. | ||
+ | * [ <i style='color:blue'>18/07/2019</i> ]: Tutorial registration is closed. | ||
+ | * [ <i style='color:blue'>12/06/2019</i> ]: Registration for the DSAI deep learning tutorial is open. | ||
+ | * [ <i style='color:blue'>06/06/2019</i> ]: Deep learning tutorial website is up. | ||
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− | < | + | <hr><hr><hr> |
Version actuelle datée du 30 septembre 2019 à 10:31
DSAI DEEP LEARNING TUTORIAL
Registration | Installations |
Scope | |
---|---|
The Data science and artificial intelligence (DSAI) research axis of ICube offers a two-day tutorial targeting researchers from all scientific backgrounds interested in the fast developing area of deep learning from a practical standpoint. The purpose of this event is to help participants build up the basic skillset necessary for understanding, training and running deep neural networks that may benefit their respective research activities. A wide array of topics will be covered with a heavily hands-on approach - from fundamental concepts and programming skills to state-of-the-art deep learning libraries and models - through a series of practicals, given by experienced PhD students and researchers. The tutorial will introduce participants to the libraries using Jupyter notebooks and help researchers desiring to apply DSAI approaches to analyze their data, such as images, videos, text, etc. ICube researchers with interest in deep learning are hereby encouraged to join the tutorial by subscribing via the registration link. |
|
Participation Pre-requisites | |
Participation is by registration. Limited space is available and the slots are given on a first come first serve basis.
In addition, a participant needs the following:
If you have hitches in accessing any file or completing the necessary installations before the program days, keep on watching this space, a date will be announced later to resolve all preparatory installation issues.
Help session date: Thursady 26th September, 2019. Time: 8:30AM - 8:59AM | |
Date & Venue | |
Date : Time : Venue : |
Program |
---|
The courses will run for 2 days. Participants will be able to attend both sessions. The detailed syllabus will be accessible via link on the menu bar. However, an abridge form is presented as follows: |
Day 1 | Day 2 | |||||
Time | Topic | Speaker | Time | Topic | Speaker | |
---|---|---|---|---|---|---|
09:00 - 10:00 | Introduction to deep learning concepts | Nicolas Padoy | 09:00 - 11:00 | Introduction to TensorFlow | Armine Vardazaryan | |
10:00 - 10:30 | Coffee Break | 11:00 - 11:30 | Coffee Break | |||
10:30 - 12:30 | A primer on Python for deep learning | Tong Yu | 11:30 - 12:30 | Inference with state-of-the-art models in TensorFlow (part 1) | Chinedu Nwoye | |
12:30 - 14:00 | Break/Lunch | 12:30 - 14:00 | Break/Lunch | |||
14:00 - 16:00 | Introduction to PyTorch | Vinkle Srivastav | 14:00 - 15:00 | Inference with state-of-the-art models in TensorFlow (part 2) | Chinedu Nwoye | |
16:00 - 16:30 | Coffee Break | 15:00 - 15:30 | Coffee Break | |||
16:30 - 17:30 | Presentation of Unistra HPC | Vincent Lucas | 15:30 - 17:30 | Generating images with GANs | Thomas Lampert |
Speakers
Nicolas Padoy
Professor
University of Strasbourg.
websiteChinedu Nwoye
PhD Student
University of Strasbourg.
websiteTong Yu
PhD Student
University of Strasbourg.
websiteArmine Vardazaryan
Research Engineer
University of Strasbourg.
websiteVinkle Kumar Srivasta
PhD Student
University of Strasbourg.
websiteThomas Lampert
Post-doc Researcher
University of Strasbourg.
websiteVincent Lucas
HPC Representative
University of Strasbourg.
website
General Information
News
|