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<a class="nav-link page-scroll" href="#header">HOME <span class="sr-only">(current)</span></a>
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<li class="nav-item">
<a class="nav-link page-scroll" href="#overview">OVERVIEW</a>
</li>
<li class="nav-item">
<a class="nav-link page-scroll" href="#schedule">SCHEDULE</a>
</li>
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<a class="nav-link page-scroll" href="#team">TEAM</a>
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<h1><highlight>MIT 6.S191</highlight></h1> <!-- MIT 6.S191 -->
<h1>Introduction to<br>Deep Learning</h1>
<h5>MIT's introductory program on deep learning methods with applications in <span id="js-rotating">vision, robotics, medicine, language, game play, art</span>, and more!</h5>
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<div id="overview" class="anchor"></div>
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<div class="row">
<div class="col-lg-7">
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<h2>Description</h2>
<h5 style="text-align:center">An <highlight>efficient and high-intensity bootcamp</highlight> designed to teach you the fundamentals of deep learning <highlight>as quickly as possible</highlight>!</h5>
<p style="text-align:center">
MIT's introductory program on deep learning methods with applications to natural language processing, computer vision, biology, and more! Students will gain foundational knowledge of deep learning algorithms, practical experience in building neural networks, and understanding of cutting-edge topics including large language models and generative AI. Program concludes with a project proposal competition with feedback from staff and panel of industry sponsors. Prerequisites assume calculus (i.e. taking derivatives) and linear algebra (i.e. matrix multiplication), we'll try to explain everything else along the way! Experience in Python is helpful but not necessary. Listeners are welcome!
<!-- <b><highlight>A complete syllabus, grading info, and student deliverables is available <a href="https://docs.google.com/document/d/1p1zAcNlogBCQFDXnSntwHFQ06pwmzewXpiQns-KfOd8/edit?usp=sharing"><highlight>HERE</highlight></a>.</highlight></b> -->
</p>
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</div> <!-- end col-lg-7 -->
<div class="col-lg-1"></div>
<div class="col-lg-4">
<div class="text-container">
<h2>Time and Location</h2>
<!-- <h5 class="text-center">
<highlight>MIT 6.S191</highlight><br>
<highlight>Mon Jan 8 - Fri Jan 12, 2024</highlight><br>
3pm - 6pm EST Everyday
</h5>
<p class="text-center">
3:00pm-3:55pm: Lecture Part 1<br>
4:00pm-4:55pm: Lecture Part 2<br>
5:00pm-6:00pm: Software Labs
</p>
<h5 class="text-center">
<highlight>MIT Room <a href="https://whereis.mit.edu/?go=32">32-123</a></highlight><br>
New competitions & prizes!
<h5> -->
<!-- <p class="text-center">
Social hosted by <a href="https://en.wikipedia.org/wiki/John_Werner"><highlight>John Werner</highlight></a> at <a href="https://www.linkventures.com/"><highlight>Link Ventures</highlight></a> on Jan 8 at 6:30pm.</highlight><br>
<p> -->
<h5 class="text-center">
<!-- <highlight>Mon Jan 5 - Fri Jan 9, 2026</highlight><br> -->
<highlight>Mon Mar 30 - Mon May 25, 2026</highlight><br>
Every Monday at 10am ET<br>
Every week!
</h5>
<p class="text-center">
The 2026 in-person edition has completed and was held in MIT Room <a href="https://whereis.mit.edu/?go=32">32-123</a>. The online edition of the course is live on Monday at 10am ET, every week!
</p>
<p class="text-center">
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<!-- <p>[<a data-toggle="modal" data-target="#themis_modal">Info</a>] [<a href='slides/6S191_MIT_DeepLearning_L5.pdf'>Slides</a>] [<a href="https://www.youtube.com/watch?v=kIiO4VSrivU&list=PLtBw6njQRU-rwp5__7C0oIVt26ZgjG9NI&index=5">Video</a>]</p> -->
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Registration opens on Dec 1 at 9am. If you are a current MIT student please <a href="https://student.mit.edu/cgi-bin/sfprwtrm.sh">register here</a> after registration opens. You can specify if you want to take the course for credit or as a listener there.
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<br><br>In addition, everyone interested in taking the course (MIT or not; and in-person or not), should also register on the <a href="http://eepurl.com/gQJo2D">internal registration</a> to receive updates.
<br><br>After the MIT program, the content will be open-sourced to the world. Again, please sign up for the <a href="http://eepurl.com/gQJo2D">internal registration</a> to receive updates when this occurs.
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What pre-requisites are required?
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We are expecting very elementary knowledge of linear algebra and calculus. How to multiply matrices, take derivatives and apply the chain rule. Familiarity in Python is a big plus as well.
The program will be beginner friendly since we have many registered students from outside of computer science.
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Is there a mailing list I can join?
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If you would like to receive related updates and lecture materials please subscribe to our <a href="https://www.youtube.com/channel/UCtslD4DGH6PKyG_1gFAX7sg">YouTube channel</a> and sign up for our <a href="http://eepurl.com/gQJo2D">mailing list</a>.
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