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%'> [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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</table><hr /><br />
 
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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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<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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  <tr valign="top" align='justify'>
 
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      <td width="60%">
 
  
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<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.
 
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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>
 
  
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<p>The tutorial will introduce participants to experiment fundamental using Jupyter notebook and help researchers desiring to apply DSAI approaches to analyze their data, such as images, videos, text, etc. As a contribution to the main objective of research axis DSAI, the tutorial will help to prepare the participants with more efficient methods to deal with large-scale data in healthcare, computer vision, robotics, environment, remote sensing, and factories of the future.<br></p><br>
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{| class="noborder" style="text-align:justify;" cellpadding="15"
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|-
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<p> ICube researchers with interesting in deep learning are hereby encourage to join the tutorial by subscribing via the registration link. <br></p>
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!colspan="2"| <b style="font-size:150%">Scope</b><hr>
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<br />
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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>
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      </td>
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<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>
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      <td width="40%">
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<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>
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          <div>[[Image:Deeplearn.jpeg|450 px|right|''[[Artificial Intelligence]]'' ]]
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|| [[Image:Deeplearn.jpeg|550 px|right|''[[Artificial Intelligence]]'' ]]
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[[Image:Conv.jpg|frameless|450 px|right|Convolutional Neural Networks]]</div>
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[[Image:Conv.jpg|frameless|550 px|right|Convolutional Neural Networks]]
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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>Participation is by registration. Limited space is available and the slot is given on a first come first serve basis.
 
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          <p>In addition, a participant needs the following:</p> </div> 
 
−
          <p>     
 
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            <ul>
 
−
                <li> A laptop</li>
 
−
                <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> 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.</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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        <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, 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>
 
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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>'''Program'''</big> </big>
 
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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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  </td></tr>
 
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<tr>
 
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<td>
 
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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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  |+ <big><b>Day 1</b></big>
 
 
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|-
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  ! style="background:lightblue;color:black;font-width:bold;border-bottom:1.5px solid black" |Time
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!colspan="2"|<b style="font-size:150%">Participation Pre-requisites </b><hr>
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  ! style="background:lightblue;color:black;font-width:bold;border-bottom:1.5px solid black" |Topic
 
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  ! style="background:lightblue;color:black;font-width:bold;border-bottom:1.5px solid black" |Speaker
 
 
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|-
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  | width=24% style="border-bottom:1px solid black" |09:00-10:15
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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.
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  | width=50% style="border-bottom:1px solid black" |Introduction to deep learning concepts
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          <p>In addition, a participant needs the following:</p>
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  | width=25% style="border-bottom:1px solid gray" |Nicolas Padoy
+
 
 +
* 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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  | style="border-bottom:1px solid black" |10:15-10:30
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!colspan="2"|<br><b style="font-size:150%">Date & Venue </b><hr>
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  | style="border-bottom:1px solid black" |Coffee Break
 
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  | style="border-bottom:1px solid gray" |
 
 
|-
 
|-
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  | style="border-bottom:1px solid black" |10:30-12:30
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|valign="top" align="center" style="font-size:120%; text-align:center; background-color:#F5F5F5"|  
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  | style="border-bottom:1px solid black" |A primer on Python (basics)
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        <p><b>Date :</b><br> Thursday 26th - Friday 27th September, 2019  </p><br>
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  | style="border-bottom:1px solid gray" |Tong Yu
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        <p><b>Time :</b><br> 9:00am - 5:30pm daily </p><br>
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|-
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        <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>
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  | style="border-bottom:1px solid black" |12:30-14:00
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|style="background-color:#F5F5F5"| <gallery mode="packed-hover" heights="300">Image:icube.jpg|''[[ICube Laboratory]]'' </gallery>
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  | style="border-bottom:1px solid black" |Break/Lunch
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|}
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  | style="border-bottom:1px solid gray" |
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<br><br>
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{|width="100%" style="text-align:justify; font-size:110%;"
 
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|-
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  | style="border-bottom:1px solid black" |14:00-16:00
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! <b style="font-size:150%">Program</b><hr>
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  | style="border-bottom:1px solid black"|Introduction to PyTorch
 
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  | style="border-bottom:1px solid gray" |Vinkle Srivastav
 
 
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|-
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   | style="border-bottom:1px solid black" |16:00-16:15
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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>
−
  | style="border-bottom:1px solid black" |Coffee Break
 
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  | style="border-bottom:1px solid gray" |
 
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|-
 
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  | style="border-bottom:1.5px solid black" |16:15-17:30
 
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  | style="border-bottom:1.5px solid black"|High performance computing
 
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  | style="border-bottom:1.5px solid gray" |Tong Yu
 
 
|}
 
|}
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{|class="wikitable" style="text-align:justify; font-size:110%; background:#fef6e7; margin-left:10px;"
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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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  |+ <big><b>Day 2</b></big>
 
 
|-
 
|-
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  ! style="background:lightblue;color:black;font-width:bold;border-bottom:1.5px solid black" |Time
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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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  ! style="background:lightblue;color:black;font-width:bold;border-bottom:1.5px solid black" |Topic
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| rowspan="9" style="background: white;"|
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  ! style="background:lightblue;color:black;font-width:bold;border-bottom:1.5px solid black" |Speaker
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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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   | width=24% style="border-bottom:1px solid black" |09:00-10:15
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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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   | width=50% style="border-bottom:1px solid black" |Introduction to TensorFlow
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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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   | width=25% style="border-bottom:1px solid gray" |Armine Vardazaryan
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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
 +
  ! style="background:lightblue;color:black;font-width:bold;text-align:center;border-bottom:1.5px solid black" |Topic
 +
   ! 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 
 +
  | 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
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  | width=28% | Introduction to TensorFlow
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  | width=10% | 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
 
|-
 
|-
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   | style="border-bottom:1px solid black" |10:15-10:30
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   ||12:30 - 14:00
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   | style="border-bottom:1px solid black" |Coffee Break
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  ||Break/Lunch
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   | style="border-bottom:1px solid gray" |
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  ||
 +
  ||12:30 - 14:00
 +
   ||Break/Lunch
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   ||
 
|-
 
|-
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   | style="border-bottom:1px solid black" |10:30-12:30
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   ||14:00 - 16:00
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   | style="border-bottom:1px solid black" |Deep learning applications ~ TensorFlow
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  ||Introduction to PyTorch
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   | style="border-bottom:1px solid gray" |Chinedu Nwoye
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  ||Vinkle Srivastav
 +
  ||14:00 - 15:00
 +
   ||Inference with state-of-the-art models in TensorFlow (part 2)
 +
   ||Chinedu Nwoye
 
|-
 
|-
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   | style="border-bottom:1px solid black" |12:30-14:00
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   ||16:00 - 16:30
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   | style="border-bottom:1px solid black" |Break/Lunch
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  ||Coffee Break
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   | style="border-bottom:1px solid gray" |
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  ||
 +
  ||15:00 - 15:30
 +
   ||Coffee Break
 +
   ||
 
|-
 
|-
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   | style="border-bottom:1px solid black" |14:00-16:00
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   ||16:30 - 17:30
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   | style="border-bottom:1px solid black"|Introduction to Keras
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  ||Presentation of Unistra HPC
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   | style="border-bottom:1px solid gray" |Thomas Lampert
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  ||Vincent Lucas
 +
  ||15:30 - 17:30
 +
   ||Generating images with GANs
 +
   ||Thomas Lampert
 
|-
 
|-
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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
 
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  | style="border-bottom:1px solid gray" |
 
−
|-
 
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  | style="border-bottom:1.5px solid black" |16:15-17:30
 
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  | 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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</td>
 
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</tr>
 
  
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</table>
 
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<hr> 
 
  
  
 +
<br><br><br><br>
 +
<b style="font-size:150%">Speakers</b><hr>
 +
<gallery style="text-align:center">
 +
Image:NP.jpg|<b>Nicolas Padoy</b> <br /> <i>Professor <br /> University of Strasbourg.</i><br>[http://camma.u-strasbg.fr/npadoy website]
 +
File:Chinedu.jpg|<b>Chinedu Nwoye </b> <br /> <i>PhD Student<br/> University of Strasbourg.</i> <br>[https://cidsoft.com/nwoyecid website]
 +
File:Tong.jpg|<b>Tong Yu </b> <br /> <i>PhD Student<br/> University of Strasbourg.</i> <br>[http://camma.u-strasbg.fr/people website]
 +
File:Armine.jpg|<b>Armine Vardazaryan </b> <br /> <i>Research Engineer<br/> University of Strasbourg.</i> <br>[http://camma.u-strasbg.fr/people website] 
 +
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]
 +
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]
 +
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]
 +
</gallery>
 +
<br><br><br>
  
  
  
 +
<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.
  
−
<div>
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|}
−
<table style="width:90%" align='center'>
+
    
−
  <tr><td colspan="5"><br /><br /><big> <big>'''Speakers'''</big> </big></td></tr>
 
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  <tr style="border: 2% solid; background: #F5F5F5;" align="center">
 
−
      <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>
 
−
      <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>
 
−
      <td> <div>
 
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[[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>
 
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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>
 
−
  </tr>
 
−
  <tr><td colspan="5"><br /></td></tr>
 
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   <tr style="border: 2% solid black; background: #F5F5F5;" align="center">
 
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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>
 
−
      <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>
 
−
 
 
−
 
 
−
  <tr><td colspan="5"><br /><br /><big> <big>'''General Information'''</big> </big></td></tr>
 
−
  <tr style="border: 2% solid black; background: #F5F5F5; cellpadding:10px" valign="top">
 
−
      <td colspan='3'> <br><big><big><b>News </b></big></big>
 
−
* [ <i style='color:blue'>06/06/2019</i> ]:  Deep learning tutorial website is up.
 
−
* [ <i style='color:blue'>10/06/2019</i> ]:  Registration for the DSAI deep learning tutorial is open.
 
−
* <i>What's new?</i>
 
−
      </td>
 
−
      <td colspan='2'> <br><big><big><b>Contact us </b></big></big>
 
−
        <p>Send your questions to this email and we will get back to you.</p>
 
−
          Board Email: [mailto:dltutorial.dsai@icube.unistra.fr dltutorialboard.dsai@icube.unistra.fr]
 
−
       
 
−
        <p><br> All participants are encourage to subscribe to the group email for updates and feedback.</p>
 
−
          Group email: [mailto:dltutorial.dsai@icube.unistra.fr dltutorialparticipants2019.dsai@icube.unistra.fr]
 
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      </td>
 
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  </tr>
 
  
  
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</table></div>
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<hr><hr><hr>

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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