Différences entre les versions de « Deep Learning Tutorial 2019 »
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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> | <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> | ||
<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> | <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> | ||
| + | <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> | ||
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<tr style="border: 2% solid black; background: #F5F5F5;" align="center"> | <tr style="border: 2% solid black; background: #F5F5F5;" align="center"> | ||
<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> | <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> | ||
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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> <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> | ||
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Version du 6 juin 2019 à 17:32
DSAI DEEP LEARNING TUTORIAL
| Registration | syllabus | Packages | Dataset | Contacts |
Scope | |
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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 | |
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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:
Contact us if you have hitches in accessing any file or completing the necessary installations before the program days. |
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Date & Venue | |
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Date & Time: Venue: |
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Syllabus 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
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Day 2
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Speakers | |||||
 Professor University of Strasbourg. |
 PhD Student University of Strasbourg. |
 PhD Student University of Strasbourg. |
 Research Engineer ICube Laboratory. |
 PhD Student University of Strasbourg. |
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 Post-doc researcher University of Strasbourg. |
 Engineer University of Strasbourg. |
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General Information | |||||
| News | Contact us
Email: dltutorial.dsai@icube.unistra.fr Send your questions to this email and we will get back to you. |
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correction:
- Intro writing
- jupyter notebooks
- experiment fundamental
- recommended to use laptop otherwise collab will be use
- date and time to fix installations
- merge package & dataset to installations (include inside link to the git)
- Room to be announced later
- Time : 9-12:30, 2-5:30
- Program: Syllabus
- Program: Title, timing and speaker.
- Link to the materials
- Deadline: July 7