Différences entre les versions de « Deep Learning Tutorial 2019 »
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|+ <big><b>Day 1</b></big> | |+ <big><b>Day 1</b></big> | ||
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! style="background:lightblue;color:black;font-width:bold;border-bottom:1.5px solid black" |Speaker | ! style="background:lightblue;color:black;font-width:bold;border-bottom:1.5px solid black" |Speaker | ||
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− | | width=24% style="border-bottom:1px solid black" |09:00-10: | + | | width=24% style="border-bottom:1px solid black" |09:00-10:00 | width=50% style="border-bottom:1px solid black" |Introduction to deep learning concepts |
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| width=25% style="border-bottom:1px solid gray" |Nicolas Padoy | | width=25% style="border-bottom:1px solid gray" |Nicolas Padoy | ||
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− | | style="border-bottom:1px solid black" |10: | + | | style="border-bottom:1px solid black" |10:00-10:30 |
| style="border-bottom:1px solid black" |Coffee Break | | style="border-bottom:1px solid black" |Coffee Break | ||
| style="border-bottom:1px solid gray" | | | style="border-bottom:1px solid gray" | | ||
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| style="border-bottom:1px solid gray" |Vinkle Srivastav | | style="border-bottom:1px solid gray" |Vinkle Srivastav | ||
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− | | style="border-bottom:1px solid black" |16:00-16: | + | | style="border-bottom:1px solid black" |16:00-16:30 |
| style="border-bottom:1px solid black" |Coffee Break | | style="border-bottom:1px solid black" |Coffee Break | ||
| style="border-bottom:1px solid gray" | | | style="border-bottom:1px solid gray" | | ||
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− | | style="border-bottom:1.5px solid black" |16: | + | | style="border-bottom:1.5px solid black" |16:30-17:30 |
| style="border-bottom:1.5px solid black"|Unistra HPC (<i>to confirm</i>) | | style="border-bottom:1.5px solid black"|Unistra HPC (<i>to confirm</i>) | ||
| style="border-bottom:1.5px solid gray" |TBA | | style="border-bottom:1.5px solid gray" |TBA | ||
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! style="background:lightblue;color:black;font-width:bold;border-bottom:1.5px solid black" |Speaker | ! style="background:lightblue;color:black;font-width:bold;border-bottom:1.5px solid black" |Speaker | ||
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− | | width=24% style="border-bottom:1px solid black" |09:00- | + | | width=24% style="border-bottom:1px solid black" |09:00-11:00 |
| width=50% style="border-bottom:1px solid black" |Introduction to TensorFlow | | width=50% style="border-bottom:1px solid black" |Introduction to TensorFlow | ||
| width=25% style="border-bottom:1px solid gray" |Armine Vardazaryan | | width=25% style="border-bottom:1px solid gray" |Armine Vardazaryan | ||
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− | | style="border-bottom:1px solid black" | | + | | style="border-bottom:1px solid black" |11:00-11:30 |
| style="border-bottom:1px solid black" |Coffee Break | | style="border-bottom:1px solid black" |Coffee Break | ||
| style="border-bottom:1px solid gray" | | | style="border-bottom:1px solid gray" | | ||
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− | | style="border-bottom:1px solid black" | | + | | style="border-bottom:1px solid black" |11:30-12:30 |
− | | style="border-bottom:1px solid black" |Using state of art models in TensorFlow | + | | style="border-bottom:1px solid black" |Using state of art models in TensorFlow (part 1) |
| style="border-bottom:1px solid gray" |Chinedu Nwoye | | style="border-bottom:1px solid gray" |Chinedu Nwoye | ||
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| style="border-bottom:1px solid gray" | | | style="border-bottom:1px solid gray" | | ||
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− | | style="border-bottom:1px solid black" |14:00- | + | | style="border-bottom:1px solid black" |14:00-15:00 |
− | | style="border-bottom:1px solid black"| | + | | style="border-bottom:1px solid black" |Using state of art models in TensorFlow (part 2) |
− | | style="border-bottom:1px solid gray" | | + | | style="border-bottom:1px solid gray" |Chinedu Nwoye |
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− | | style="border-bottom:1px solid black" | | + | | style="border-bottom:1px solid black" |15:00-15:30 |
| style="border-bottom:1px solid black" |Coffee Break | | style="border-bottom:1px solid black" |Coffee Break | ||
| style="border-bottom:1px solid gray" | | | style="border-bottom:1px solid gray" | | ||
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− | | style="border-bottom:1.5px solid black" | | + | | style="border-bottom:1.5px solid black" |15:30-17:30 |
| style="border-bottom:1.5px solid black"|TBA | | style="border-bottom:1.5px solid black"|TBA | ||
| style="border-bottom:1.5px solid gray" |Thomas Lampert | | style="border-bottom:1.5px solid gray" |Thomas Lampert |
Version du 7 juin 2019 à 16:00
DSAI DEEP LEARNING TUTORIAL
Registration | Syllabus | Installations | Contacts |
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.
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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. |
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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 will be accessible via link on the menu bar. However, an abridge form is presented as follows: | ||||||||||||||||||||||||||||||||||||||||||||||||
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Contact us Send your questions to this email and we will get back to you. Board Email: dltutorialboard.dsai@icube.unistra.fr |