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<!DOCTYPE html>
<html>
<title>Deep Learning Clinic (DLC) Syllabus</title>
<meta charset="UTF-8">
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background-image: url("background.jpeg");
min-height: 75%;
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<body>
<!-- Links (sit on top) -->
<div class="w3-top">
<div class="w3-row w3-padding w3-black">
<div class="w3-col s3">
<a href="#" class="w3-button w3-block w3-black">HOME</a>
</div>
<div class="w3-col s3">
<a href="#description" class="w3-button w3-block w3-black">Description</a>
</div>
<div class="w3-col s3">
<a href="#info" class="w3-button w3-block w3-black">Info</a>
</div>
<div class="w3-col s3">
<a href="#lectures" class="w3-button w3-block w3-black">Lectures</a>
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<!-- Header with image -->
<header class="bgimg w3-display-container w3-grayscale-min" id="home">
<div class="w3-display-middle w3-center w3-padding-large w3-hide-small">
<span class="w3-tag" style="font-size:30px">Deep Learning Clinic</span>
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<!-- About Container -->
<div class="w3-container" id="description">
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<h5 class="w3-center w3-padding-64"><span class="w3-tag w3-wide">Description</span></h5>
<center>Welcome to the Deep Learning Clinic (DLC) 2018!</center>
<h3>Who DLC Is For</h3>
This <strong>No-Credit</strong> lab is designed for students who are eager to solve complex real world problems with powerful machine learning algorithms, yet need advice on and help with various stages of this process -- what tools to use, how to use them, and practical advice.
<h3>What DLC Is About</h3>
DLC has meetings with two formats: an instructional session (Friday) focusing on the essential knowledge to get started with real-world examples, and a lab session (Wednesday) that provides hands-on exercises and feedback.
<p>The lectures provide a concise introduction to techniques and tools that are essential in solving practical problems with deep learning algorithms.</p>
<p>In the lab session, students are encouraged to work on their own projects that using deep learning. The instructor will have one-on-one analysis with the students to help them tackle challenges raised, including: feasibility evaluation, modeling and task formulation, network architectures searching and designing, and practical guidance on training and tuning neural network models.</p>
<h3>What DLC Is Not</h3>
This is <em>not</em> a machine learning or deep learning introductory course.
Although fundamental materials of those subjects are to be reviewed, they will be covered in a concise manner. Students who are unfamiliar with machine learning or deep learning are encouraged to read the <a href=#ref>Reference</a> section below for a more comprehensive understanding of the relevant topics.
<br></br>
<h4>This lab is zero-credit, has no assignments or evaluations.</h4>
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<div class="w3-container" id="info">
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<h5 class="w3-center w3-padding-64"><span class="w3-tag w3-wide">Information</span></h5>
<p><strong>Instructor:</strong> <p>Jin Sun <a href=http://www.cs.cornell.edu/~jinsun/>Website</a></p></p>
<p><strong>Contact:</strong><p> js2625 AT cornell.edu</p> <p>*Please include the tag 'DLC' in the email subject.</p></p>
<p><strong>Hours and Locations:</strong></p>
<p> Wed 8:30am-9:30am, Lab Session in Bloomberg 061 </p>
<p> Fri 8:30am-10am, Lecture Session in Bloomberg 081 </p>
<p><strong>Slack Channel </strong><a href=https://dlc18.slack.com/>link</a></p>
</div>
</div>
<div class="w3-container" id="lectures">
<div class="w3-content" style="max-width:700px">
<h5 class="w3-center w3-padding-64"><span class="w3-tag w3-wide">Lectures</span></h5>
<table class="w3-table-all w3-card-4">
<tr>
<th>Date</th>
<th>Topic</th>
<th>Details</th>
</tr>
<tr>
<td>9/28</td>
<td>Introduction</td>
<td>
Overview of the lab and syllabus outlining what students can expect from the lab.
<p>Exploration of a simple learning problem (<a href=http://playground.tensorflow.org>TensorFlow Playground</a>) to show students the essential steps in solving a machine learning problem.</p>
<p>Class survey on students' background and interested topics.</p>
</td>
<td>
<a href='./pdfs/DLC_lec1.pdf'>Slides (PDF)</a>
</td>
</tr>
<tr>
<td>10/5</td>
<td>Deep Learning Frameworks</td>
<td>
Step-by-step introduction on how to set up a deep learning-ready computing environment and how and when to use popular frameworks and tools, including:
<p><a href=https://pytorch.org/tutorials/ > [Pytorch] </a>
<a href=https://www.tensorflow.org/tutorials/ >[TensorFlow] </a>
<a href=https://keras.io/> [Keras] </a>
<a href=https://github.com/yunjey/pytorch-tutorial/tree/master/tutorials/04-utils/tensorboard> [Tensorboard for visualization] </a>
<p>After this lecture, the students should have a working DL environment and be ready to explore the usage of these tools.</p>
</td>
<td><a href="./DLF.html">Lecture Page</a></td>
</tr>
<tr>
<td>10/12</td>
<td>Brief Introduction to Machine Learning</td>
<td>
Introduction/review of core machine learning concepts, including: supervised learning, unsupervised learning, classification, regression, loss functions, performance evaluation metrics, and etc.
</td>
<td><a href="./pdfs/DLC_lec3.pdf">Slides (PDF)</a></td>
</tr>
<tr>
<td>10/19</td>
<td>Brief Introduction to Deep Learning</td>
<td>
Introduction/review of deep learning topics, including: optimization techniques, network structures (e.g., Fully Connected Nets, Convolutional Nets, Recurrent Nets), Generative Adversarial Networks, Reinforcement Learning, and etc.
</td>
<td><a href="./pdfs/DLC_lec4.pdf">Slides (PDF)</a></td>
</tr>
<tr>
<td>10/26</td>
<td>Deep Learning in the Real World: A Case Study</td>
<td>
A case study on how to: identify a real world problem, collect data, design models, train and evaluate.
</td>
<td><a href="./pdfs/DLC_lec5.pdf">Slides (PDF)</a></td>
</tr>
<tr>
<td>11/2</td>
<td>Data</td>
<td>
How to perform data related tasks such as collection, labeling, and verification.
<p>Details on how to set up data labeling tasks on Amazon Mechanical Turks.</p>
</td>
<td><a href="./pdfs/DLC_lec6.pdf">Slides (PDF)</a></td>
</tr>
<tr>
<td>11/9</td>
<td>Tricks on Training Neural Networks</td>
<td>
Practical tips and tricks on how to train a (good) neural network model, including: pre-processing, post-processing, learning rate, batch size, normalization, network depth, choice between architectures, fine-tuning, and etc.
</td>
<td><a href="./pdfs/DLC_lec7.pdf">Slides (PDF)</a></td>
</tr>
<tr>
<td style="color:#FF0000;"><b>11/21</b></td>
<td>Real-World Ready Machine Learning Tools</td>
<td>
Introduce machine learning tools that are widely used and proven to be effective in real-world problems, such as: Google Cloud Vision, AutoML for model search, Dlib, Face++ API for face detection, Detectron for object detection, NLTK for natural language processing, and etc.
<br></br>
<b><font color="red">[Notice]</font> Due to the upcoming studio sprint this Friday, this week's lecture will be moved to next Wed 8:30-9:30am. This Friday's session (8:30-10:00am) will be lab/office hours.</b>
</td>
</tr>
<tr>
<td>11/23</td>
<td>Thanksgiving, No Class</td>
<td>
</td>
</tr>
<tr>
<td>11/30</td>
<td><b><font color="red">No Class</font></b></td>
<td>
</td>
</tr>
</table>
</div>
</div>
<div class="w3-container" id="ref">
<div class="w3-content" style="max-width:700px">
<h5 class="w3-center w3-padding-64"><span class="w3-tag w3-wide">Reference</span></h5>
<!-- <p>Basic machine learning concepts including but not limited to: data representation (features, train/test sets, preprocessing), evaluation (cross-validation, performance measurement), optimization (gradient descent).</p> -->
<strong>Online Courses</strong>
<br></br>
MIT 6.S191: Introduction to Deep Learning <a href=http://introtodeeplearning.com/>link</a>
<br></br>
Stanford CS231n: Convolutional Neural Networks for Visual Recognition <a href=http://cs231n.stanford.edu/>link</a>
<br></br>
<strong>Free Textbooks</strong>
<br></br>
<em>A Course in Machine Learning</em> by Hal Daume III <a href=http://ciml.info/>link</a>
<br></br>
<em>Deep Learning</em> by Ian Goodfellow and Yoshua Bengio and Aaron Courville <a href=https://www.deeplearningbook.org/>link</a>
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