Différences entre les versions de « Deep Learning Tutorial 2019 »
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</table><hr /><br /> | </table><hr /><br /> | ||
− | <center><div align="center" style="border:2px solid black; background:#fef6e7; color:blue; align:center; width:30%" > Registration deadline: July | + | <center><div align="center" style="border:2px solid black; background:#fef6e7; color:blue; align:center; width:30%" > Registration deadline: July 7, 2019</div></center> |
<table style="width:95%; border-spacing:15px; font-size:130%: margin:auto" align='center'> | <table style="width:95%; border-spacing:15px; font-size:130%: margin:auto" align='center'> | ||
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</ul> | </ul> | ||
</p> | </p> | ||
− | <p> | + | <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> |
</td> | </td> | ||
</tr> | </tr> | ||
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<td align='center' valign="middle"> | <td align='center' valign="middle"> | ||
<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> | ||
− | <p><b>Venue:</b><br> ICube Laboratory | + | <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> |
</td> | </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> | ||
</tr> | </tr> | ||
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− | + | <tr><td colspan="2"><br /><br /><big> <big>'''Program'''</big> </big> | |
− | <tr><td colspan="2"><br /><br /><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> | ||
− | + | <tr> | |
− | + | <td> | |
− | + | {| cellspacing=0 align=center cellpadding=5px width=98% style="background: lightyellow; border: 1px solid gray; font-size:80%" | |
− | + | |+ <big><b>Day 1</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 | |
− | + | | width=50% style="border-bottom:1px solid black" |Introduction to deep learning concepts | |
− | + | | width=25% style="border-bottom:1px solid gray" |Nicolas Padoy | |
− | + | |- | |
− | + | | 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" |A primer on Python (basics) | ||
+ | | style="border-bottom:1px solid gray" |Tong Yu | ||
+ | |- | ||
+ | | 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 PyTorch | ||
+ | | style="border-bottom:1px solid gray" |Vinkle Srivastav | ||
+ | |- | ||
+ | | 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"|High performance computing | ||
+ | | style="border-bottom:1.5px solid gray" |Tong Yu | ||
+ | |} | ||
+ | </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 | ||
+ | | width=50% style="border-bottom:1px solid black" |Introduction to TensorFlow | ||
+ | | width=25% style="border-bottom:1px solid gray" |Armine Vardazaryan | ||
+ | |- | ||
+ | | 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 | ||
+ | | style="border-bottom:1px solid gray" |Chinedu Nwoye | ||
+ | |- | ||
+ | | 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 | ||
+ | | style="border-bottom:1.5px solid gray" |Thomas Lampert | ||
+ | |} | ||
+ | </td> | ||
+ | </tr> | ||
+ | |||
</table> | </table> | ||
+ | <hr> | ||
− | |||
− | <div><table style="width:90%" align='center'> | + | |
+ | |||
+ | <div> | ||
+ | <table style="width:90%" align='center'> | ||
<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> | ||
<tr style="border: 2% solid; background: #F5F5F5;" align="center"> | <tr style="border: 2% solid; background: #F5F5F5;" align="center"> | ||
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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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Version du 7 juin 2019 à 01:10
DSAI DEEP LEARNING TUTORIAL
Registration | syllabus | Packages | Installations | Contacts |
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. |
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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:
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. |
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Date & Venue | |||||||||||||||||||||||||||||||||||||||||||||||||
Date & Time: Venue: |
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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: | |||||||||||||||||||||||||||||||||||||||||||||||||
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Speakers | |||||
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