-
Notifications
You must be signed in to change notification settings - Fork 3
/
index.html
433 lines (333 loc) · 14.8 KB
/
index.html
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
<!DOCTYPE html>
<html>
<title>Deep Learning Clinic (DLC) Syllabus</title>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1">
<link rel="stylesheet" href="https://www.w3schools.com/w3css/4/w3.css">
<link rel="stylesheet" href="https://fonts.googleapis.com/css?family=Inconsolata">
<style>
body, html {
height: 100%;
font-family: "Inconsolata", sans-serif;
}
.bgimg {
background-position: center;
background-size: cover;
background-image: url("background.jpeg");
min-height: 100%;
}
.menu {
display: none;
}
</style>
<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>
</div>
</div>
</div>
<!-- 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><br></br>
<span class="w3-tag" style="font-size:20px">Fall 2019</span>
</div>
</header>
<!-- Add a background color and large text to the whole page -->
<div class="w3-sand w3-grayscale w3-large">
<!-- About Container -->
<div class="w3-container" id="description">
<div class="w3-content" style="max-width:700px">
<h5 class="w3-center w3-padding-64"><span class="w3-tag w3-wide">Description</span></h5>
<!-- <center>Welcome to the Deep Learning Clinic (DLC) 2019!</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. -->
DLC has an instructional session (Tuesday) and an office hour session (Wednesday).
<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 office hours,
<!-- 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>
</div>
</div>
<!-- Course Info Container -->
<div class="w3-container" id="info">
<div class="w3-content" style="max-width:700px">
<h5 class="w3-center w3-padding-64"><span class="w3-tag w3-wide">Information</span></h5>
<p><strong>Instructor:</strong> <p> <a href=http://www.cs.cornell.edu/~jinsun/>Jin Sun</a></p></p>
<p><strong>Contact:</strong><p> jinsun AT cornell.edu</p> <p>*Please include the tag 'DLC' in the email subject.</p></p>
<p><strong>Hours and Locations:</strong></p>
<p> Tue 9:30am-10:45am, Lecture Session in Bloomberg 061 </p>
<p> Wed 2:00pm-3:00pm, Office Hour in Bloomberg 316 </p>
<p><strong>Slack Channel </strong><a href=https://cornelltechdlc.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>Slides</th> -->
</tr>
<!-- ---------------------------------------- -->
<tr>
<td>9/3</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>9/10</td> <!--Date-->
<td>Deep Learning Frameworks</td> <!--Title-->
<!-- <td>
Set up popular machine learning and deep learning tools and environments such as Anaconda and PyTorch.
<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> <!--PDF-->
<a href='./DLF.html'>Webpage</a>
</td>
</tr>
<!-- ---------------------------------------- -->
<tr>
<td>9/17</td> <!--Date-->
<td>Brief Introduction to Machine Learning</td> <!--Title-->
<!-- <td>
Introduction/review of core machine learning concepts, including: supervised learning, unsupervised learning, classification, regression, loss functions, performance evaluation metrics, and etc.
</td> -->
<td> <!--PDF-->
<a href='./pdfs/DLC_lec2.pdf'>Slides (PDF)</a>
</td>
</tr>
<!-- ---------------------------------------- -->
<tr>
<td>9/24</td> <!--Date-->
<td>Brief Introduction to Deep Learning</td> <!--Title-->
<!-- <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> <!--PDF-->
<a href='./pdfs/DLC_lec3.pdf'>Slides (PDF)</a>
</td>
</tr>
<!-- ---------------------------------------- -->
<tr>
<td>10/1</td> <!--Date-->
<td>Prepare Data</td> <!--Title-->
<!-- <td>
</td> -->
<td> <!--PDF-->
<a href='./pdfs/DLC_lec4.pdf'>Slides (PDF)</a>
</td>
</tr>
<!-- ---------------------------------------- -->
<tr>
<td>10/8</td> <!--Date-->
<td>Neural Network architectures</td> <!--Title-->
<!-- <td>
</td> -->
<td> <!--PDF-->
<a href='./pdfs/DLC_lec5.pdf'>Slides (PDF)</a>
</td>
</tr>
<!-- ---------------------------------------- -->
<tr>
<td>10/22</td> <!--Date-->
<td style="text-decoration: line-through;">Tricks on Training Neural Networks</td> <!--Title-->
<!-- <td>
</td> -->
<td> <!--PDF-->
<!--<a href='./pdfs/DLC_lec6.pdf'>Slides (PDF)</a> -->
</td>
</tr>
<!-- ---------------------------------------- -->
<tr>
<td>10/29</td> <!--Date-->
<!--<td>Machine Learning Model Evaluation</td> [>Title<]-->
<td>Tricks on Training Neural Networks</td> <!--Title-->
<!-- <td>
</td> -->
<td> <!--PDF-->
<a href='./pdfs/DLC_lec6.pdf'>Slides (PDF)</a>
</td>
</tr>
<!-- ---------------------------------------- -->
<tr>
<td>11/5</td> <!--Date-->
<td><em>Case Study</em>: Transfer Learning</td> <!--Title-->
<!-- <td>
</td> -->
<td> <!--PDF-->
<a href='./pdfs/DLC_lec7.pdf'>Slides (PDF)</a>
</td>
</tr>
<!-- ---------------------------------------- -->
<tr>
<td>11/12</td> <!--Date-->
<td><em>Case Study</em>: Sequential Data Modeling</td> <!--Title-->
<!-- <td>
</td> -->
<td> <!--PDF-->
<a href='./pdfs/DLC_lec8.pdf'>Slides (PDF)</a>
</td>
</tr>
<!-- ---------------------------------------- -->
<tr>
<td>11/19</td> <!--Date-->
<td><em>Case Study</em>: Object Detection and Segmentation</td> <!--Title-->
<!-- <td>
</td> -->
<td> <!--PDF-->
<a href='./pdfs/DLC_lec9.pdf'>Slides (PDF)</a>
</td>
</tr>
<!-- ---------------------------------------- -->
<tr>
<td>11/26</td> <!--Date-->
<td><em>Case Study</em>: Generative Adversarial Networks</td> <!--Title-->
<!-- <td>
</td> -->
<td> <!--PDF-->
<a href='./pdfs/DLC_lec10.pdf'>Slides (PDF)</a>
</td>
</tr>
<!-- ---------------------------------------- -->
<tr>
<td>12/3</td> <!--Date-->
<td>Real-World Ready Machine Learning Tools</td> <!--Title-->
<!-- <td>
</td> -->
<td> <!--PDF-->
<a href='./pdfs/DLC_lec11.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>
</div>
</div>
<!-- End page content -->
</div>
<!-- Footer -->
<footer class="w3-center w3-light-grey w3-padding-48 w3-large">
<p>Powered by <a href="https://www.w3schools.com/w3css/default.asp" title="W3.CSS" target="_blank" class="w3-hover-text-green">w3.css</a></p>
<p>Photo by Jin Sun</p>
</footer>
</body>
</html>