/
index.html
275 lines (240 loc) · 8.86 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
---
layout: default
---
<div>
These notes accompany the Stanford CS class <a href="http://cs231n.stanford.edu/">CS231n: Convolutional Neural
Networks for Visual Recognition</a>. For questions/concerns/bug reports, please submit a pull request directly to
our <a href="https://github.com/cs231n/cs231n.github.io">git repo</a>.
<br>
<!-- For questions/concerns/bug reports contact <a href="http://cs.stanford.edu/people/jcjohns/">Justin Johnson</a> regarding the assignments, or contact <a href="http://cs.stanford.edu/people/karpathy/">Andrej Karpathy</a> regarding the course notes. You can also submit a pull request directly to our <a href="https://github.com/cs231n/cs231n.github.io">git repo</a>. -->
<!-- <br> -->
<!-- We encourage the use of the <a href="https://hypothes.is/">hypothes.is</a> extension to annote comments and discuss these notes inline. -->
</div>
<div class="home">
<div class="materials-wrap">
<div class="module-header">Spring 2024 Assignments</div>
<div class="materials-item">
<a href="assignments2024/assignment1/">Assignment #1: Image Classification, kNN, SVM, Softmax, Fully Connected Neural Network</a>
</div>
<div class="materials-item">
<a href="assignments2024/assignment2/">Assignment #2: Fully Connected and Convolutional Nets, Batch Normalization, Dropout, Pytorch & Network Visualization</a>
</div>
<div class="materials-item">
(To be released) Assignment #3: Image Captioning with RNNs and Transformers, Network Visualization,
Generative Adversarial Networks, Self-Supervised Contrastive Learning
<!-- <a href="assignments2023/assignment3/">Assignment #3: Network Visualization, Image Captioning with RNNs and Transformers, Generative Adversarial Networks, Self-Supervised Contrastive Learning</a> -->
</div>
</div>
<!-- <div class="materials-wrap">
<div class="module-header">Spring 2021 Assignments</div>
<div class="materials-item">
<a href="assignments2021/assignment1/">Assignment #1: Image Classification, kNN, SVM, Softmax, Fully Connected Neural Network</a>
</div>
<div class="materials-item">
<a href="assignments2021/assignment2/">Assignment #2: Fully Connected and Convolutional Nets, Batch Normalization, Dropout, Frameworks</a>
</div>
<div class="materials-item">
<a href="assignments2021/assignment3/">Assignment #3: Image Captioning with RNNs and Transformers, Network Visualization,
Generative Adversarial Networks, Self-Supervised Contrastive Learning</a>
</div>
</div> -->
<!--
<div class="materials-item">
<a href="assignments2019/assignment2/">
Assignment #2: Fully Connected Nets, Batch Normalization, Dropout,
Convolutional Nets
</a>
</div>
<div class="materials-item">
<a href="assignments2019/assignment3/">
Assignment #3: Image Captioning with Vanilla RNNs, Image Captioning
with LSTMs, Network Visualization, Style Transfer, Generative Adversarial Networks
</a>
</div> -->
<!--
<div class="module-header">Spring 2018 Assignments</div>
<div class="materials-item">
<a href="assignments2018/assignment1/">
Assignment #1: Image Classification, kNN, SVM, Softmax, Neural Network
</a>
</div>
<div class="materials-item">
<a href="assignments2018/assignment2/">
Assignment #2: Fully-Connected Nets, Batch Normalization, Dropout,
Convolutional Nets
</a>
</div>
<div class="materials-item">
<a href="assignments2018/assignment3/">
Assignment #3: Image Captioning with Vanilla RNNs, Image Captioning
with LSTMs, Network Visualization, Style Transfer, Generative Adversarial Networks
</a>
</div>
-->
<!--
<div class="module-header">Winter 2016 Assignments</div>
<div class="materials-item">
<a href="assignments2016/assignment1/">
Assignment #1: Image Classification, kNN, SVM, Softmax, Neural Network
</a>
</div>
<div class="materials-item">
<a href="assignments2016/assignment2/">
Assignment #2: Fully-Connected Nets, Batch Normalization, Dropout,
Convolutional Nets
</a>
</div>
<div class="materials-item">
<a href="assignments2016/assignment3/">
Assignment #3: Recurrent Neural Networks, Image Captioning,
Image Gradients, DeepDream
</a>
</div>
-->
<!--
<div class="module-header">Winter 2015 Assignments</div>
<div class="materials-item">
<a href="assignment1/">
Assignment #1: Image Classification, kNN, SVM, Softmax
</a>
</div>
<div class="materials-item">
<a href="assignment2/">
Assignment #2: Neural Networks, ConvNets I
</a>
</div>
<div class="materials-item">
<a href="assignment3/">
Assignment #3: ConvNets II, Transfer Learning, Visualization
</a>
</div>
-->
<div class="module-header">Module 0: Preparation</div>
<div class="materials-item">
<a href="setup-instructions/">
Software Setup
</a>
</div>
<div class="materials-item">
<a href="python-numpy-tutorial/">
Python / Numpy Tutorial (with Jupyter and Colab)
</a>
</div>
<!--
<div class="materials-item">
<a href="terminal-tutorial/">
Terminal.com Tutorial
</a>
</div>
-->
<!-- <div class="materials-item">
<a href="https://github.com/cs231n/gcloud">
Google Cloud Tutorial
</a>
</div> -->
<!-- <div class="materials-item">
<a href="aws-tutorial/">
AWS Tutorial
</a>
</div> -->
<!-- hardcoding items here to force a specific order -->
<div class="module-header">Module 1: Neural Networks</div>
<div class="materials-item">
<a href="classification/">
Image Classification: Data-driven Approach, k-Nearest Neighbor, train/val/test splits
</a>
<div class="kw">
L1/L2 distances, hyperparameter search, cross-validation
</div>
</div>
<div class="materials-item">
<a href="linear-classify/">
Linear classification: Support Vector Machine, Softmax
</a>
<div class="kw">
parameteric approach, bias trick, hinge loss, cross-entropy loss, L2 regularization, web demo
</div>
</div>
<div class="materials-item">
<a href="optimization-1/">
Optimization: Stochastic Gradient Descent
</a>
<div class="kw">
optimization landscapes, local search, learning rate, analytic/numerical gradient
</div>
</div>
<div class="materials-item">
<a href="optimization-2/">
Backpropagation, Intuitions
</a>
<div class="kw">
chain rule interpretation, real-valued circuits, patterns in gradient flow
</div>
</div>
<div class="materials-item">
<a href="neural-networks-1/">
Neural Networks Part 1: Setting up the Architecture
</a>
<div class="kw">
model of a biological neuron, activation functions, neural net architecture, representational power
</div>
</div>
<div class="materials-item">
<a href="neural-networks-2/">
Neural Networks Part 2: Setting up the Data and the Loss
</a>
<div class="kw">
preprocessing, weight initialization, batch normalization, regularization (L2/dropout), loss functions
</div>
</div>
<div class="materials-item">
<a href="neural-networks-3/">
Neural Networks Part 3: Learning and Evaluation
</a>
<div class="kw">
gradient checks, sanity checks, babysitting the learning process, momentum (+nesterov), second-order methods,
Adagrad/RMSprop, hyperparameter optimization, model ensembles
</div>
</div>
<div class="materials-item">
<a href="neural-networks-case-study/">
Putting it together: Minimal Neural Network Case Study
</a>
<div class="kw">
minimal 2D toy data example
</div>
</div>
<div class="module-header">Module 2: Convolutional Neural Networks</div>
<div class="materials-item">
<a href="convolutional-networks/">
Convolutional Neural Networks: Architectures, Convolution / Pooling Layers
</a>
<div class="kw">
layers, spatial arrangement, layer patterns, layer sizing patterns, AlexNet/ZFNet/VGGNet case studies,
computational considerations
</div>
</div>
<div class="materials-item">
<a href="understanding-cnn/">
Understanding and Visualizing Convolutional Neural Networks
</a>
<div class="kw">
tSNE embeddings, deconvnets, data gradients, fooling ConvNets, human comparisons
</div>
</div>
<div class="materials-item">
<a href="transfer-learning/">
Transfer Learning and Fine-tuning Convolutional Neural Networks
</a>
</div>
<div class="module-header">Student-Contributed Posts</div>
<div class="materials-item">
<a href="choose-project/">
Taking a Course Project to Publication
</a>
<a href="rnn/">
Recurrent Neural Networks
</a>
</div>
</div>
</div>