-
Notifications
You must be signed in to change notification settings - Fork 11
/
Copy pathindex.html
287 lines (251 loc) · 13.5 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
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="utf-8">
<meta http-equiv="X-UA-Compatible" content="IE=edge">
<meta name="viewport" content="width=device-width, initial-scale=1">
<title>Stanford University CS224d: Deep Learning for Natural Language Processing</title>
<!-- bootstrap -->
<link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.2.0/css/bootstrap.min.css">
<link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.2.0/css/bootstrap-theme.min.css">
<!-- Google fonts -->
<link href='http://fonts.googleapis.com/css?family=Roboto:400,300' rel='stylesheet' type='text/css'>
<!-- Google Analytics -->
<script>
(function(i,s,o,g,r,a,m){i['GoogleAnalyticsObject']=r;i[r]=i[r]||function(){
(i[r].q=i[r].q||[]).push(arguments)},i[r].l=1*new Date();a=s.createElement(o),
m=s.getElementsByTagName(o)[0];a.async=1;a.src=g;m.parentNode.insertBefore(a,m)
})(window,document,'script','//www.google-analytics.com/analytics.js','ga');
ga('create', 'UA-60458624-1', 'auto');
ga('send', 'pageview');
</script>
<link rel="stylesheet" type="text/css" href="style.css" />
</head>
<body>
<div id="header">
<a href="http://nlp.stanford.edu/">
<img src="http://nlp.stanford.edu/sentiment/images/nlp-logo.gif" style="height:50px; float: left; margin-left: 20px;">
</a>
<h1>CS224d: Deep Learning for Natural Language Processing</h1>
<div style="clear:both;"></div>
</div>
<div id="teaser">
<img src="images/treeFrontSentiment.png">
</div>
<div class="sechighlight">
<div class="container sec">
<h2>Course Description</h2>
<div id="coursedesc">
Natural language processing (NLP) is one of the most important technologies of the information age. Understanding complex language utterances is also a crucial part of artificial intelligence. Applications of NLP are everywhere because people communicate most everything in language: web search, advertisement, emails, customer service, language translation, radiology reports, etc. There are a large variety of underlying tasks and machine learning models powering NLP applications.
Recently, deep learning approaches have obtained very high performance across many different NLP tasks. These models can often be trained with a single end-to-end model and do not require traditional, task-specific feature engineering.
In this spring quarter course students will learn to implement, train, debug, visualize and invent their own neural network models. The course provides a deep excursion into cutting-edge research in deep learning applied to NLP.
The final project will involve training a complex recurrent neural network and applying it to a large scale NLP problem.
On the model side we will cover word vector representations, window-based neural networks, recurrent neural networks, long-short-term-memory models, recursive neural networks, convolutional neural networks as well as some very novel models involving a memory component.
Through lectures and programming assignments students will learn the necessary engineering tricks for making neural networks work on practical problems.
</div>
<div>
<table class="table">
<tr class="active">
<th>Previous Years Project Reports</th>
</tr>
<tr>
<td> <a href="reports_2015.html"> 2015 Reports </a> </td>
</tr>
<tr>
<td> <a href="reports_2016.html"> 2016 Reports </a> </td>
</tr>
</table>
</div>
</div>
</div>
<div class="container sec">
<div class="row">
<div class="col-md-4">
<h2>Course Instructor</h2>
<div class="instructor">
<a href="http://socher.org">
<div class="instructorphoto"><img src="https://www.metamind.io/static/images/team/richard.jpg"></div>
<div>Richard Socher</div>
</a>
</div>
</div>
<div class="col-md-8">
<h2>Teaching Assistants</h2>
<div class="instructor">
<div class="instructorphoto"><a
href="https://www.linkedin.com/in/jameshong1993"><img src="images/james_hong.jpg"> </a></div>
<div> <a href="https://www.linkedin.com/in/jameshong1993"> James Hong </a></div>
</div>
<div class="instructor">
<div class="instructorphoto"><a href="https://www.linkedin.com/in/sameep-bagadia-47a40699"><img src="images/sameep.jpg"> </a></div>
<div> <a href="https://www.linkedin.com/in/sameep-bagadia-47a40699"> Sameep Bagadia </a></div>
</div>
<div class="instructor">
<div class="instructorphoto"><a
href="https://www.linkedin.com/in/david-dindi-2a532a48"><img src="images/david.jpg"> </a></div>
<div> <a href="https://www.linkedin.com/in/david-dindi-2a532a48"> David Dindi</a></div>
</div>
<div class="instructor">
<div class="instructorphoto"><a href="http://web.stanford.edu/~rbharath/"><img src="images/bharath.jpg"> </a></div>
<div> <a href="http://web.stanford.edu/~rbharath/"> B. Ramsundar </a></div>
</div>
<div class="instructor">
<div class="instructorphoto"><a href="https://www.linkedin.com/in/naveenariva"><img src="images/naveen_default.jpg"> </a></div>
<div> <a href="https://www.linkedin.com/in/naveenariva">N. Arivazhagan</a></div>
</div>
<div class="instructor">
<div class="instructorphoto"><a href="https://www.linkedin.com/in/qiaojing-yan-4534b9112"><img src="images/qiaojing.jpg"> </a></div>
<div> <a href="https://www.linkedin.com/in/qiaojing-yan-4534b9112">Qiaojing Yan</a></div>
</div>
</div>
</div>
</div>
<div class="sechighlight">
<div style="text-align:center; padding:40px 0px 40px 0px;">
<!-- <button type="button" class="btn btn-success btn-lg">Course Notes (updated each week)</button> -->
<a href="syllabus.html">
<button type="button" class="btn btn-success btn-lg">Detailed Syllabus (with materials)</button>
</a>
<a href="https://piazza.com/class/ilx0v32x8ce7dh">
<button type="button" class="btn btn-warning btn-lg">Piazza forum</button>
</a>
<!-- <a href="https://www.youtube.com/channel/UCsGC3XXF1ThHwtDo18d7WVw/videos">
<button type="button" class="btn btn-warning btn-lg">Lecture Videos</button>
</a> -->
</div>
</div>
<div class="container sec">
<div class="row">
<div class="col-md-4">
<h2>Class Time and Location</h2>
Spring quarter (March - June, 2016)<br>
Lecture: Tuesday, Thursday 3:00-4:20<br>
Location: <a href="http://www-cs.stanford.edu/about/gates-computer-science-building">Gates B1</a>
</div>
<div class="col-md-4">
<h2>Office Hours</h2>
<b>Richard</b>: Tue 4:30-6:30pm, Huang Basement<br>
(for research and project discussions)<br><br>
TAs:<br>
<b>David</b>: Mon 6:00-8:00pm, Huang 138<br>
<b>Bharath</b>: Teus 1:00-3:00pm, Huang Basement<br>
<b>James</b>: Wed, 5:30-7:30pm, Gates B26<br>
<b>Sameep</b>: Thur, 12:45-2:45pm, Gates B21<br>
<b>Naveen</b>: Fri, 1:00-3:00pm, Huang Basement<br>
<b>Qiaojing</b>: Sun, 4:00-6:00pm, Gates B24<br>
</div>
<div class="col-md-4">
<h2>Grading Policy</h2>
Assignment #1: 15%<br>
Assignment #2: 15%<br>
Assignment #3: 15%<br>
Midterm: 15%<br>
Final Project: 40%<br>
<!--
<a href="assignment1/index.html">Assignment #1</a>: 15%<br>
<a href="assignment2/index.html">Assignment #2</a>: 15%<br>
<a href="assignment3/index.html">Assignment #3</a>: 15%<br>
Midterm: 15%<br>
<a href="project.html">Final Project</a>: 40%<br> -->
</div>
</div>
</div>
<div class="container sec">
<div class="row">
<div class="col-md-4">
<h2>Course Discussions</h2>
Stanford students: <a href="https://piazza.com/class/ilx0v32x8ce7dh">Piazza </a> (for Stanford students)
<br>
Online discussions: <a href="http://www.reddit.com/r/CS224d">Reddit Group </a> (for non-Stanford students)
<br>
Our Twitter account: <a href="https://twitter.com/cs224d">@CS224d</a>
</div>
<div class="col-md-4">
<h2>Assignment Details</h2>
See the <a href="assignments.html">Assignment Page</a> for more details on how to hand in your assignments.
</div>
<div class="col-md-4">
<h2>Course Project Details</h2>
See the <a href="project.html">Project Page</a> for more details on the course project.
</div>
</div>
</div>
<div class="sechighlight">
<div class="container sec">
<h2>Prerequisites</h2>
<ul>
<li><span class="spanh">Proficiency in Python</span><br>All class assignments will be in Python (and use numpy). There is a tutorial <a href="http://cs231n.github.io/python-numpy-tutorial/">here</a> for those who aren't as familiar with Python. If you have a lot of programming experience but in a different language (e.g. C/C++/Matlab/Javascript) you will probably be fine.</li>
<li><span class="spanh">College Calculus, Linear Algebra</span> (e.g. MATH 19 or 41, MATH 51)<br> You should be comfortable taking derivatives and understanding matrix vector operations and notation.</li>
<li><span class="spanh">Basic Probability and Statistics</span> (e.g. CS 109 or other stats course)<br>You should know basics of probabilities, gaussian distributions, mean, standard deviation, etc.</li>
<li><span class="spanh">Equivalent knowledge of CS229 (Machine Learning)</span><br> We will be formulating cost functions, taking derivatives and performing optimization with gradient descent.</li>
</ul>
<h2>Recommended</h2>
<ul>
<li><span class="spanh">Knowledge of natural language processing (CS224N or CS224U)</span><br> We will discuss a lot of different tasks and you will appreciate the power of deep learning techniques even more if you know how much work had been done on these tasks and how related models have solved them.</li>
<li><span class="spanh">Convex optimization</span><br> You may find some of the optimization tricks more intuitive with this background.</li>
<li><span class="spanh">Knowledge of convolutional neural networks (CS231n)</span><br>The first problem set will probably be easier for you. We cannot assume you took this class so there will be ~3 lectures that overlap in content. You can use that time to dive deeper into some aspects. </li>
</ul>
</div>
</div>
<div class="container sec">
<h2>FAQ</h2>
<div class="qqa">
<div class="qq">Is this the first time this class is offered?</div>
<div class="qa">This is the second offering of this course. The class
is designed to introduce students to deep learning for natural language
processing. We will place a particular emphasis on Neural Networks,
which are a class of deep learning models that have recently obtained
improvements in many different NLP tasks.</div>
</div>
<div class="qqa">
<div class="qq">Can I follow along from the outside?</div>
<div class="qa">We'd be happy if you join us! We plan to make the course materials widely available: <b>The assignments, course notes and slides will be available online.</b> We may provide videos. We won't be able to give you course credit.</div>
</div>
<div class="qqa">
<div class="qq">Can I take this course on credit/no cred basis?</div>
<div class="qa">Yes. Credit will be given to those who would have otherwise earned a C- or above.</div>
</div>
<div class="qqa">
<div class="qq">Can I audit or sit in?</div>
<div class="qa">In general we are very open to sitting-in guests if you are a member of the Stanford community (registered student, staff, and/or faculty). Out of courtesy, we would appreciate that you first email us or talk to the instructor after the first class you attend.</div>
</div>
<div class="qqa">
<div class="qq">Can I work in groups for the Final Project?</div>
<div class="qa">Yes, in groups of up to two people.</div>
</div>
<div class="qqa">
<div class="qq">I have a question about the class. What is the best way to reach the course staff?</div>
<div class="qa">Stanford students please use an internal class forum on
Piazza so that other students may benefit from your questions and our
answers. If you have a personal matter, email us at the class mailing
list <b>Will be added shortly</b> <!-- <b>[email protected]</b>-->.</div>
</div>
<div class="qqa">
<div class="qq">Can I combine the Final Project with another course?</div>
<div class="qa">Yes, you may. There are a couple of courses concurrently offered with CS224d that are natural choices, such as CS224u (Natural Language Understanding, by Prof. Chris Potts and Bill MacCartney). If you are taking a related class, please speak to the instructors to receive permission to combine the Final Project assignments.</div>
</div>
<div class="qqa">
<div class="qq">As an SCPD student, how do I make up for poster presentation component?</div>
<div class="qa">For the final poster presentation you can submit a video via youtube about your project.</div>
</div>
<div class="qqa">
<div class="qq">As an SCPD student, how do I take the midterm?</div>
<div class="qa">For the midterm, we can use standard SCPD procedures of having your manager or somebody at your company monitor you during the exam.</div>
</div>
<div class="qqa">
<div class="qq">Will there be virtual office hours for SCPD students</div>
<div class="qa">All office hours will be accesible on google hangouts. The link to the hangout is available on piazza</div>
</div>
</div>
<div class="sechighlight">
<div id="footer">
<div id="classicons">
Webdesign by Andrej Karpathy
</div>
</div>
</div>
<!-- jQuery and Boostrap -->
<script src="https://ajax.googleapis.com/ajax/libs/jquery/1.11.1/jquery.min.js"></script>
<script src="https://maxcdn.bootstrapcdn.com/bootstrap/3.2.0/js/bootstrap.min.js"></script>
</body>
</html>