Data Science and Artificial Intelligence

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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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<center><div align="center" style="border:2px solid black; background:#fef6e7; color:blue; align:center; width:30%" > Registration deadline: July 7, 2019</div></center>
  
 
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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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           <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 attend to this.</p>
 
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         <p><b>Date & Time:</b><br> September 26 - 27, 2019, 9am-5pm daily</p><br><br>
 
         <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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         <p><b>Venue:</b><br> ICube Laboratory, 300 Bd Sébastien Brant, 67400 Illkirch-Graffenstaden<br>(<i>Room to be announced later.</i>)</p><p>[https://goo.gl/maps/XVoh1KzCHzGMekSm6 Google map direction]</p>
 
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       </td>
 
       <td><div><gallery mode="packed-hover" heights="250">Image:icube.jpg|''[[ICube Laboratory]]'' </gallery> </div> </td>
 
       <td><div><gallery mode="packed-hover" heights="250">Image:icube.jpg|''[[ICube Laboratory]]'' </gallery> </div> </td>
 
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   <tr><td colspan="2"><br /><br /><big> <big>'''Program'''</big> </big>
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   <tr><td colspan="2"><br /><br /><big> <big>'''Syllabus'''</big> </big>
 
 
       <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>  
 
       <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>  
 
   </td></tr>
 
   </td></tr>
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  <tr style="border: 2px solid black; background: #ffffff;">
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<tr>
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      <td> <h5>Day 1 </h5>
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<td>
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* Introduction to deep learning
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{|  cellspacing=0 align=center cellpadding=5px width=98% style="background: lightyellow; border: 1px solid gray; font-size:80%"
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* Basic python
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  |+ <big><b>Day 1</b></big>
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* PyTorch
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* HPC
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  ! style="background:brown;color:white;border-bottom:1.5px solid black" |Time
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      </td>
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  ! style="background:brown;color:white;border-bottom:1.5px solid black" |Topic
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      <td> <h5>Day 2 </h5>
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  ! style="background:brown;color:white;border-bottom:1.5px solid black" |Speaker
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* TensorFlow
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|-
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* TensorFlow Applications
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  | width=24% style="border-bottom:1px solid black" |09:00-10:15
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* GAN
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  | width=50% style="border-bottom:1px solid black" |Introduction to deep learning concepts
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* Keras
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  | width=25% style="border-bottom:1px solid gray" |Nicolas Padoy
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      </td>
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|-
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  </tr>
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  | style="border-bottom:1px solid black" |10:15-10:30
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  | style="border-bottom:1px solid black" |Coffee Break
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  | style="border-bottom:1px solid gray" |
 +
|-
 +
  | style="border-bottom:1px solid black" |10:30-12:30
 +
  | style="border-bottom:1px solid black" |A primer on Python (basics)
 +
  | style="border-bottom:1px solid gray" |Tong Yu
 +
|-
 +
  | style="border-bottom:1px solid black" |12:30-14:00
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  | style="border-bottom:1px solid black" |Break/Lunch
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  | style="border-bottom:1px solid gray" |
 +
|-
 +
  | style="border-bottom:1px solid black" |14:00-16:00
 +
  | style="border-bottom:1px solid black"|Introduction to PyTorch
 +
  | style="border-bottom:1px solid gray" |Vinkle Srivastav
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|-
 +
  | style="border-bottom:1px solid black" |16:00-16:15
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  | style="border-bottom:1px solid black" |Coffee Break
 +
  | style="border-bottom:1px solid gray" |
 +
|-
 +
  | style="border-bottom:1.5px solid black" |16:15-17:30
 +
  | style="border-bottom:1.5px solid black"|High performance computing
 +
  | style="border-bottom:1.5px solid gray" |Tong Yu
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|}
 +
</td>
 +
<td>
 +
{|  cellspacing=0 align=center cellpadding=5px width=98% style="background: lightyellow; border: 1px solid gray; font-size:80%"
 +
  |+ <big><b>Day 2</b></big>
 +
|-
 +
  ! style="background:brown;color:white;border-bottom:1.5px solid black" |Time
 +
  ! style="background:brown;color:white;border-bottom:1.5px solid black" |Topic
 +
  ! style="background:brown;color:white;border-bottom:1.5px solid black" |Speaker
 +
|-
 +
  | width=24% style="border-bottom:1px solid black" |09:00-10:15
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  | width=50% style="border-bottom:1px solid black" |Introduction to TensorFlow
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  | width=25% style="border-bottom:1px solid gray" |Armine Vardazaryan
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|-
 +
  | style="border-bottom:1px solid black" |10:15-10:30
 +
  | style="border-bottom:1px solid black" |Coffee Break
 +
  | style="border-bottom:1px solid gray" |
 +
|-
 +
  | style="border-bottom:1px solid black" |10:30-12:30
 +
  | style="border-bottom:1px solid black" |Deep learning applications ~ TensorFlow
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  | style="border-bottom:1px solid gray" |Chinedu Nwoye
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|-
 +
  | style="border-bottom:1px solid black" |12:30-14:00
 +
  | style="border-bottom:1px solid black" |Break/Lunch
 +
  | style="border-bottom:1px solid gray" |
 +
|-
 +
  | style="border-bottom:1px solid black" |14:00-16:00
 +
  | style="border-bottom:1px solid black"|Introduction to Keras
 +
  | style="border-bottom:1px solid gray" |Thomas Lampert
 +
|-
 +
  | style="border-bottom:1px solid black" |16:00-16:15
 +
  | style="border-bottom:1px solid black" |Coffee Break
 +
  | style="border-bottom:1px solid gray" |
 +
|-
 +
  | style="border-bottom:1.5px solid black" |16:15-17:30
 +
  | style="border-bottom:1.5px solid black"|Generative Adversarial Network
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  | style="border-bottom:1.5px solid gray" |Thomas Lampert
 +
|}
 +
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   <tr><td colspan="5"><br /><br /><big> <big>'''Speakers'''</big> </big></td></tr>
 
   <tr><td colspan="5"><br /><br /><big> <big>'''Speakers'''</big> </big></td></tr>
 
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correction:<br>
 
correction:<br>
 +
* date and time to fix installations
 
* Intro writing
 
* Intro writing
 
* jupyter notebooks
 
* jupyter notebooks
 
* experiment fundamental
 
* experiment fundamental
 
* recommended to use laptop otherwise collab will be use
 
* 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
 
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* Program: Title, timing and speaker.
 
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* Link to the materials
 
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* Deadline: July 7
 

Version du 7 juin 2019 à 01:10


DSAI DEEP LEARNING TUTORIAL

Registration syllabus Packages Installations Contacts


Registration deadline: July 7, 2019


Scope

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.


Renowned academics and PhD students will lecture and provide hands on practicals with the audience.

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.
Artificial Intelligence
Convolutional Neural Networks

Participation Pre-requisite
Participation is by registration. Limited space is available and the slot is given on a first come first serve basis.

In addition, a participant needs the following:

  • A laptop
  • Basic programming skill
  • Install all the libraries via the Packages menu
  • Download the training dataset from the Dataset 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 attend to this.


Date & Venue

Date & Time:
September 26 - 27, 2019, 9am-5pm daily



Venue:
ICube Laboratory, 300 Bd Sébastien Brant, 67400 Illkirch-Graffenstaden
(Room to be announced later.)

Google map direction


Program

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:

Day 1
Time Topic Speaker
09:00-10:15 Introduction to deep learning concepts Nicolas Padoy
10:15-10:30 Coffee Break
10:30-12:30 A primer on Python (basics) Tong Yu
12:30-14:00 Break/Lunch
14:00-16:00 Introduction to PyTorch Vinkle Srivastav
16:00-16:15 Coffee Break
16:15-17:30 High performance computing Tong Yu
Day 2
Time Topic Speaker
09:00-10:15 Introduction to TensorFlow Armine Vardazaryan
10:15-10:30 Coffee Break
10:30-12:30 Deep learning applications ~ TensorFlow Chinedu Nwoye
12:30-14:00 Break/Lunch
14:00-16:00 Introduction to Keras Thomas Lampert
16:00-16:15 Coffee Break
16:15-17:30 Generative Adversarial Network Thomas Lampert






Speakers
NP.jpg
Nicolas Padoy
Professor
University of Strasbourg.
Chinedu.jpg
Chinedu Nwoye
PhD Student
University of Strasbourg.
Tong.jpg
Tong Yu
PhD Student
University of Strasbourg.
Armine.jpg
Armine Vardazaryan
Research Engineer
ICube Laboratory.
Vinkle.png
Vinkle Kumar Srivasta
PhD Student
University of Strasbourg.

Thomas.jpg
Thomas Lampert
Post-doc researcher
University of Strasbourg.
Hpc.jpg
HPC representative
Engineer
University of Strasbourg.


General Information
News Contact us 
        Email: dltutorial.dsai@icube.unistra.fr

Send your questions to this email and we will get back to you.


correction:

  • date and time to fix installations
  • Intro writing
  • jupyter notebooks
  • experiment fundamental
  • recommended to use laptop otherwise collab will be use
Récupérée de « https://dsai.icube.unistra.fr/index.php?title=Deep_Learning_Tutorial_2019&oldid=107 »