Data Science and Artificial Intelligence

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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%' align='left'> [https://docs.google.com/forms/d/1GLQCjtki4If7fRcnEHgUju8MS-Gz4GLwp6aGutdNuqc/edit?ts=5cf64e9b Registration] </td>
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       <td width='20%'> [https://docs.google.com/forms/d/1GLQCjtki4If7fRcnEHgUju8MS-Gz4GLwp6aGutdNuqc/edit?ts=5cf64e9b Registration] </td>
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       <td width='20%'> syllabus </td>
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      <td width='20%'> [https://icube-forge.unistra.fr/CAMMA/misc/dsai_dl_tutorial Packages] </td>
 
 
       <td width='20%'> [http://icube-web.unistra.fr/dsai/index.php?title=Deep_Learning_Tutorial_2019_installations Installations] </td>
 
       <td width='20%'> [http://icube-web.unistra.fr/dsai/index.php?title=Deep_Learning_Tutorial_2019_installations Installations] </td>
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       <td align='right' width='20%'> [http://camma.u-strasbg.fr/contact Contacts] </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 27, 2019</div></center>
 
  
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<table style="width:95%; border-spacing:15px; font-size:130%: margin:auto" align='center'>
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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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  <tr><td colspan="2"><br /><br /><big> <big>'''Scope'''</big> </big></td></tr>
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{| class="noborder" style="text-align:justify;" cellpadding="15"
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          <p>DSAI DL 2019 is a research tutorial event with a global scope aiming at introducing the participants to the basic concepts in the critical and fast developing area of deep learning. As part of the programs of the Data science and artificial intelligence (DSAI) research axis in ICube, the tutorial will help to prepare the participants with more efficient methods to deal with large-scale data in healthcare, computer vision, robotics, etc.</p><br />
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|-
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           <p>Renowned academics and PhD students will lecture and provide hands on practicals with the audience.</p>
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!colspan="2"| <b style="font-size:150%">Scope</b><hr>
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|-
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| 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]]'' ]]
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[[Image:Conv.jpg|frameless|550 px|right|Convolutional Neural Networks]]
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|-
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!colspan="2"|<b style="font-size:150%">Participation Pre-requisites </b><hr>
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|-
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|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''
 +
|-
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!colspan="2"|<br><b style="font-size:150%">Date & Venue </b><hr>
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|-
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|valign="top" align="center" style="font-size:120%; text-align:center; background-color:#F5F5F5"|
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        <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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          <p>DSAI DL 2019 is a research tutorial event with a global scope aiming at introducing the participants to the basic concepts in the critical and fast developing area of deep learning. As part of the programs of the Data science and artificial intelligence (DSAI) research axis in ICube, the tutorial will help to prepare the participants with more efficient methods to deal with large-scale data in healthcare, computer vision, robotics, etc.</p><br />
 
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      </td>
 
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      <td width="40%">
 
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          <div>[[Image:Deeplearn.jpeg|450 px|right|''[[Artificial Intelligence]]'' ]]
 
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[[Image:Conv.jpg|frameless|450 px|right|Convolutional Neural Networks]]</div>
 
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      </td>
 
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  </tr>
 
  
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  <tr><td colspan="2"><hr /><br /><big> <big>'''Participation Pre-requisite'''</big> </big></td></tr>
 
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  <tr align='justify'>
 
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      <td colspan="2">
 
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          <div><i>Participation is by registration. Limited space is available and the slot is given on a first come first serve basis.</i></div>
 
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          <p>In addition, a participant needs the following:         
 
−
            <ul>
 
−
                <li> A laptop</li>
 
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                <li> Basic programming skill</li>
 
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                <li> Install all the libraries via the ''Packages'' menu</li>
 
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                <li> Download the training dataset from the ''Dataset'' menu</li>
 
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            </ul>
 
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          </p>
 
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          <p> Contact us if you have hitches in accessing any file or completing the necessary installations before the program days.</p>
 
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      </td>
 
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  </tr>
 
  
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  <tr><td colspan="2"><br /><br /><big> <big>'''Date & Venue'''</big> </big></td></tr>
 
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      <td align='center' valign="middle">
 
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        <p><b>Date & Time:</b><br> September 26 - 27, 2019, 9am-5pm daily</p><br><br>
 
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        <p><b>Venue:</b><br> ICube Laboratory (C218 - <i>tentative</i>), 300 Bd Sébastien Brant, 67400 Illkirch-Graffenstaden</p><p>[https://goo.gl/maps/XVoh1KzCHzGMekSm6 Google map direction]</p>
 
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      </td>
 
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      <td><div><gallery mode="packed-hover" heights="250">Image:icube.jpg|''[[ICube Laboratory]]'' </gallery> </div> </td>
 
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  </tr>
 
  
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  <tr><td colspan="2"><br /><br /><big> <big>'''Syllabus'''</big> </big>
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<br><br>
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      <br><p> The courses will run for 2 days. Participants will be able to attend both sessions. The detailed syllabus can be accessed via link on the menu bar. However, an abridge form is presented as follows:</p>  
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{|width="100%" style="text-align:justify; font-size:110%;"
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  </td></tr>
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|-
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  <tr style="border: 2px solid black; background: #ffffff;">
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! <b style="font-size:150%">Program</b><hr>
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      <td> <h5>Day 1 </h5>
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|-
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* Introduction to deep learning
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  |<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>  
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* Basic python
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|}
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* PyTorch
 
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* HPC
 
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      </td>
 
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      <td> <h5>Day 2 </h5>
 
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* TensorFlow
 
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* TensorFlow Applications
 
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* GAN
 
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* Keras
 
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      </td>
 
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  </tr>
 
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</table>
 
  
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<hr>
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{|class="wikitable" style="text-align:justify; font-size:110%; background:#fef6e7; margin-left:10px;"
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|-
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| 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
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| rowspan="9" style="background: white;"|
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|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
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|-
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  ! style="background:lightblue;color:black;text-align:center;font-width:bold;border-bottom:1.5px solid black" |Time
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  ! style="background:lightblue;color:black;font-width:bold;text-align:center;border-bottom:1.5px solid black" |Topic
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  ! style="background:lightblue;color:black;font-width:bold;text-align:center;border-bottom:1.5px solid black" |Speaker
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  ! style="background:lightblue;color:black;font-width:bold;text-align:center;border-bottom:1.5px solid black" |Time
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  ! style="background:lightblue;color:black;font-width:bold;text-align:center;border-bottom:1.5px solid black" |Topic
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  ! style="background:lightblue;color:black;font-width:bold;text-align:center;border-bottom:1.5px solid black" |Speaker
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|-
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  | width=11%  | 09:00 - 10:00 
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  | width=28% | Introduction to deep learning concepts
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  | width=10% | Nicolas Padoy
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  | width=11%  | 09:00 - 11:00
 +
  | width=28% | Introduction to TensorFlow
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  | width=10% | Armine Vardazaryan
 +
|-
 +
  || 10:00 - 10:30
 +
  || Coffee Break
 +
  ||
 +
  || 11:00 - 11:30
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  || 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
 +
|-
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  ||12:30 - 14:00
 +
  ||Break/Lunch
 +
  ||
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  ||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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<div><table style="width:90%" align='center'>
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<br><br><br><br>
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  <tr><td colspan="5"><br /><br /><big> <big>'''Speakers'''</big> </big></td></tr>
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<b style="font-size:150%">Speakers</b><hr>
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  <tr style="border: 2% solid; background: #F5F5F5;" align="center">
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<gallery style="text-align:center">
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      <td> <div>[[Image:NP.jpg|150px|framed|center|link=http://camma.u-strasbg.fr/npadoy]] <b>Nicolas Padoy</b> <br /> <i>Professor<br /> University of Strasbourg. </i> </div></td>
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Image:NP.jpg|<b>Nicolas Padoy</b> <br /> <i>Professor <br /> University of Strasbourg.</i><br>[http://camma.u-strasbg.fr/npadoy website]
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      <td> <div>[[Image:Chinedu.jpg|150px|framed|center|link=http://cidsoft.com]] <b>Chinedu Nwoye </b> <br /> <i>PhD Student<br /> University of Strasbourg. </i> </div></td>
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File:Chinedu.jpg|<b>Chinedu Nwoye </b> <br /> <i>PhD Student<br/> University of Strasbourg.</i> <br>[https://cidsoft.com/nwoyecid website]
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      <td> <div>[[Image:Tong.jpg|150px|framed|center|link=http://camma.u-strasbg.fr/people]] <b>Tong Yu </b> <br /> <i>PhD Student<br /> University of Strasbourg. </i> </div></td>
+
File:Tong.jpg|<b>Tong Yu </b> <br /> <i>PhD Student<br/> University of Strasbourg.</i> <br>[http://camma.u-strasbg.fr/people website]  
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      <td> <div>[[Image:Armine.jpg|150px|framed|center|link=http://camma.u-strasbg.fr/people]] <b>Armine Vardazaryan </b> <br /> <i>Research Engineer<br /> ICube Laboratory. </i></div> </td>
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File:Armine.jpg|<b>Armine Vardazaryan </b> <br /> <i>Research Engineer<br/> University of Strasbourg.</i> <br>[http://camma.u-strasbg.fr/people website]
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      <td> <div>[[Image:Vinkle.png|150px|framed|center|link=http://camma.u-strasbg.fr/people]] <b>Vinkle Kumar Srivasta </b> <br /> <i>PhD Student<br /> University of Strasbourg. </i></div> </td>
+
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]
−
  </tr>
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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]  
−
  <tr><td colspan="5"><br /></td></tr>
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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]  
−
  <tr style="border: 2% solid black; background: #F5F5F5;" align="center">
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</gallery>
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  <td> <div>[[Image:Thomas.jpg|150px|framed|center|link=https://sites.google.com/site/tomalampert]] <b>Thomas Lampert </b> <br /> <i>Post-doc researcher<br /> University of Strasbourg.</i></div> </td>
+
<br><br><br>
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      <td> <div>[[Image:hpc.jpg|150px|framed|center|center|link=https://services-numeriques.unistra.fr/les-services-aux-usagers/hpc.html]] <b> HPC representative </b> <br /> <i>Engineer<br /> University of Strasbourg. </i></div> </td>
 
−
      <td colspan="3"><div></div><td>
 
−
  </tr>
 
  
  
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  <tr><td colspan="5"><br /><br /><big> <big>'''General Information'''</big> </big></td></tr>
 
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  <tr style="border: 2% solid black; background: #F5F5F5;">
 
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      <td colspan='3'> <big><big><b>News </b></big></big></td>
 
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      <td colspan='2'> <big><big><b>Contact us </b></big></big>
 
−
        Email: [mailto:dltutorial.dsai@icube.unistra.fr dltutorial.dsai@icube.unistra.fr]
 
−
        <p>Send your questions to this email and we will get back to you.</p>
 
−
      </td>
 
−
  </tr>
 
  
 +
<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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</table></div>
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|}
 +
 
  
  
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correction:<br>
+
<hr><hr><hr>
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* Intro writing
 
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* jupyter notebooks
 
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* experiment fundamental
 
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* recommended to use laptop otherwise collab will be use
 
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* date and time to fix installations
 
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* merge package & dataset to installations (include inside link to the git)
 
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* Room to be announced later
 
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* Time : 9-12:30, 2-5:30
 
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* Program: Syllabus
 
−
* Program: Title, timing and speaker.
 
−
* Link to the materials
 
−
* Deadline: July 7
 

Version actuelle datée du 30 septembre 2019 à 10:31


DSAI DEEP LEARNING TUTORIAL

Registration Installations



Registration deadline: July 7, 2019 Closed


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.
Artificial Intelligence
Convolutional Neural Networks


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:

  • A laptop
  • Basic programming skill
  • Deep learning libraries installable via the Installations menu
  • Training dataset downloadable from the Installations menu

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 :
Thursday 26th - Friday 27th September, 2019


Time :
9:00am - 5:30pm daily


Venue :
ICube Laboratory, 300 Bd Sébastien Brant, 67400 Illkirch-Graffenstaden
(Room C218).

Google map direction






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.
    website

  • Chinedu Nwoye
    PhD Student
    University of Strasbourg.
    website

  • Tong Yu
    PhD Student
    University of Strasbourg.
    website

  • Armine Vardazaryan
    Research Engineer
    University of Strasbourg.
    website

  • Vinkle Kumar Srivasta
    PhD Student
    University of Strasbourg.
    website

  • Thomas Lampert
    Post-doc Researcher
    University of Strasbourg.
    website

  • Vincent Lucas
    HPC Representative
    University of Strasbourg.
    website





General Information


News
  • [ 28/09/2019 ]: Tutorial ended successfully. Thanks for coming!.
  • [ 25/09/2019 ]: Links to download the codes, datasets, models and slides updated.
  • [ 05/09/2019 ]: Tutorial installation instructions uploaded.
  • [ 18/07/2019 ]: Tutorial registration is closed.
  • [ 12/06/2019 ]: Registration for the DSAI deep learning tutorial is open.
  • [ 06/06/2019 ]: Deep learning tutorial website is up.


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