Skip to content

Commit 820e462

Browse files
hshi74hshi74
committed
Add A1 for 2023
1 parent cbccad9 commit 820e462

File tree

1 file changed

+123
-123
lines changed

1 file changed

+123
-123
lines changed

‎index.html‎

Lines changed: 123 additions & 123 deletions
Original file line numberDiff line numberDiff line change
@@ -14,26 +14,26 @@
1414
</div>
1515

1616
<div class="home">
17-
<!-- <div class="materials-wrap">
17+
<div class="materials-wrap">
1818
<div class="module-header">Spring 2022 Assignments</div>
1919
<div class="materials-item">
2020
<!-- (To be released) Assignment #1: Image Classification, kNN, SVM, Softmax, Fully Connected Neural Network -->
21-
<a href="assignments2023/assignment1/">Assignment #1: Image Classification, kNN, SVM, Softmax, Fully Connected
22-
Neural Network</a>
23-
</div>
24-
<div class="materials-item">
25-
(To be released) Assignment #2: Fully Connected and Convolutional Nets, Batch Normalization, Dropout, Pytorch &
26-
Network Visualization -->
27-
<!-- <a href="assignments2023/assignment2/">Assignment #2: Fully Connected and Convolutional Nets, Batch Normalization, Dropout, Pytorch & Network Visualization</a> -->
28-
</div>
29-
<div class="materials-item">
30-
(To be released) Assignment #3: Image Captioning with RNNs and Transformers, Network Visualization,
31-
Generative Adversarial Networks, Self-Supervised Contrastive Learning
32-
<!-- <a href="assignments2023/assignment3/">Assignment #3: Image Captioning with RNNs and Transformers, Generative Adversarial Networks, Self-Supervised Contrastive Learning</a> -->
33-
</div>
34-
</div>
21+
<a href="assignments2023/assignment1/">Assignment #1: Image Classification, kNN, SVM, Softmax, Fully Connected
22+
Neural Network</a>
23+
</div>
24+
<div class="materials-item">
25+
(To be released) Assignment #2: Fully Connected and Convolutional Nets, Batch Normalization, Dropout, Pytorch &
26+
Network Visualization -->
27+
<!-- <a href="assignments2023/assignment2/">Assignment #2: Fully Connected and Convolutional Nets, Batch Normalization, Dropout, Pytorch & Network Visualization</a> -->
28+
</div>
29+
<div class="materials-item">
30+
(To be released) Assignment #3: Image Captioning with RNNs and Transformers, Network Visualization,
31+
Generative Adversarial Networks, Self-Supervised Contrastive Learning
32+
<!-- <a href="assignments2023/assignment3/">Assignment #3: Image Captioning with RNNs and Transformers, Generative Adversarial Networks, Self-Supervised Contrastive Learning</a> -->
33+
</div>
34+
</div>
3535

36-
<!-- <div class="materials-wrap">
36+
<!-- <div class="materials-wrap">
3737
<div class="module-header">Spring 2021 Assignments</div>
3838
<div class="materials-item">
3939
<a href="assignments2021/assignment1/">Assignment #1: Image Classification, kNN, SVM, Softmax, Fully Connected Neural Network</a>
@@ -46,7 +46,7 @@
4646
Generative Adversarial Networks, Self-Supervised Contrastive Learning</a>
4747
</div>
4848
</div> -->
49-
<!--
49+
<!--
5050
<div class="materials-item">
5151
<a href="assignments2019/assignment2/">
5252
Assignment #2: Fully Connected Nets, Batch Normalization, Dropout,
@@ -60,7 +60,7 @@
6060
with LSTMs, Network Visualization, Style Transfer, Generative Adversarial Networks
6161
</a>
6262
</div> -->
63-
<!--
63+
<!--
6464
<div class="module-header">Spring 2018 Assignments</div>
6565
6666
<div class="materials-item">
@@ -84,7 +84,7 @@
8484
</div>
8585
-->
8686

87-
<!--
87+
<!--
8888
<div class="module-header">Winter 2016 Assignments</div>
8989
9090
<div class="materials-item">
@@ -108,7 +108,7 @@
108108
</div>
109109
-->
110110

111-
<!--
111+
<!--
112112
<div class="module-header">Winter 2015 Assignments</div>
113113
114114
<div class="materials-item">
@@ -130,150 +130,150 @@
130130
</div>
131131
-->
132132

133-
<div class="module-header">Module 0: Preparation</div>
133+
<div class="module-header">Module 0: Preparation</div>
134134

135-
<div class="materials-item">
136-
<a href="setup-instructions/">
137-
Software Setup
138-
</a>
139-
</div>
135+
<div class="materials-item">
136+
<a href="setup-instructions/">
137+
Software Setup
138+
</a>
139+
</div>
140140

141-
<div class="materials-item">
142-
<a href="python-numpy-tutorial/">
143-
Python / Numpy Tutorial (with Jupyter and Colab)
144-
</a>
145-
</div>
146-
<!--
141+
<div class="materials-item">
142+
<a href="python-numpy-tutorial/">
143+
Python / Numpy Tutorial (with Jupyter and Colab)
144+
</a>
145+
</div>
146+
<!--
147147
<div class="materials-item">
148148
<a href="terminal-tutorial/">
149149
Terminal.com Tutorial
150150
</a>
151151
</div>
152152
-->
153-
<!-- <div class="materials-item">
153+
<!-- <div class="materials-item">
154154
<a href="https://github.com/cs231n/gcloud">
155155
Google Cloud Tutorial
156156
</a>
157157
</div> -->
158-
<!-- <div class="materials-item">
158+
<!-- <div class="materials-item">
159159
<a href="aws-tutorial/">
160160
AWS Tutorial
161161
</a>
162162
</div> -->
163163

164-
<!-- hardcoding items here to force a specific order -->
165-
<div class="module-header">Module 1: Neural Networks</div>
164+
<!-- hardcoding items here to force a specific order -->
165+
<div class="module-header">Module 1: Neural Networks</div>
166166

167-
<div class="materials-item">
168-
<a href="classification/">
169-
Image Classification: Data-driven Approach, k-Nearest Neighbor, train/val/test splits
170-
</a>
171-
<div class="kw">
172-
L1/L2 distances, hyperparameter search, cross-validation
167+
<div class="materials-item">
168+
<a href="classification/">
169+
Image Classification: Data-driven Approach, k-Nearest Neighbor, train/val/test splits
170+
</a>
171+
<div class="kw">
172+
L1/L2 distances, hyperparameter search, cross-validation
173+
</div>
173174
</div>
174-
</div>
175175

176-
<div class="materials-item">
177-
<a href="linear-classify/">
178-
Linear classification: Support Vector Machine, Softmax
179-
</a>
180-
<div class="kw">
181-
parameteric approach, bias trick, hinge loss, cross-entropy loss, L2 regularization, web demo
176+
<div class="materials-item">
177+
<a href="linear-classify/">
178+
Linear classification: Support Vector Machine, Softmax
179+
</a>
180+
<div class="kw">
181+
parameteric approach, bias trick, hinge loss, cross-entropy loss, L2 regularization, web demo
182+
</div>
182183
</div>
183-
</div>
184184

185-
<div class="materials-item">
186-
<a href="optimization-1/">
187-
Optimization: Stochastic Gradient Descent
188-
</a>
189-
<div class="kw">
190-
optimization landscapes, local search, learning rate, analytic/numerical gradient
185+
<div class="materials-item">
186+
<a href="optimization-1/">
187+
Optimization: Stochastic Gradient Descent
188+
</a>
189+
<div class="kw">
190+
optimization landscapes, local search, learning rate, analytic/numerical gradient
191+
</div>
191192
</div>
192-
</div>
193193

194-
<div class="materials-item">
195-
<a href="optimization-2/">
196-
Backpropagation, Intuitions
197-
</a>
198-
<div class="kw">
199-
chain rule interpretation, real-valued circuits, patterns in gradient flow
194+
<div class="materials-item">
195+
<a href="optimization-2/">
196+
Backpropagation, Intuitions
197+
</a>
198+
<div class="kw">
199+
chain rule interpretation, real-valued circuits, patterns in gradient flow
200+
</div>
200201
</div>
201-
</div>
202202

203-
<div class="materials-item">
204-
<a href="neural-networks-1/">
205-
Neural Networks Part 1: Setting up the Architecture
206-
</a>
207-
<div class="kw">
208-
model of a biological neuron, activation functions, neural net architecture, representational power
203+
<div class="materials-item">
204+
<a href="neural-networks-1/">
205+
Neural Networks Part 1: Setting up the Architecture
206+
</a>
207+
<div class="kw">
208+
model of a biological neuron, activation functions, neural net architecture, representational power
209+
</div>
209210
</div>
210-
</div>
211211

212-
<div class="materials-item">
213-
<a href="neural-networks-2/">
214-
Neural Networks Part 2: Setting up the Data and the Loss
215-
</a>
216-
<div class="kw">
217-
preprocessing, weight initialization, batch normalization, regularization (L2/dropout), loss functions
212+
<div class="materials-item">
213+
<a href="neural-networks-2/">
214+
Neural Networks Part 2: Setting up the Data and the Loss
215+
</a>
216+
<div class="kw">
217+
preprocessing, weight initialization, batch normalization, regularization (L2/dropout), loss functions
218+
</div>
218219
</div>
219-
</div>
220220

221-
<div class="materials-item">
222-
<a href="neural-networks-3/">
223-
Neural Networks Part 3: Learning and Evaluation
224-
</a>
225-
<div class="kw">
226-
gradient checks, sanity checks, babysitting the learning process, momentum (+nesterov), second-order methods,
227-
Adagrad/RMSprop, hyperparameter optimization, model ensembles
221+
<div class="materials-item">
222+
<a href="neural-networks-3/">
223+
Neural Networks Part 3: Learning and Evaluation
224+
</a>
225+
<div class="kw">
226+
gradient checks, sanity checks, babysitting the learning process, momentum (+nesterov), second-order methods,
227+
Adagrad/RMSprop, hyperparameter optimization, model ensembles
228+
</div>
228229
</div>
229-
</div>
230230

231-
<div class="materials-item">
232-
<a href="neural-networks-case-study/">
233-
Putting it together: Minimal Neural Network Case Study
234-
</a>
235-
<div class="kw">
236-
minimal 2D toy data example
231+
<div class="materials-item">
232+
<a href="neural-networks-case-study/">
233+
Putting it together: Minimal Neural Network Case Study
234+
</a>
235+
<div class="kw">
236+
minimal 2D toy data example
237+
</div>
237238
</div>
238-
</div>
239239

240-
<div class="module-header">Module 2: Convolutional Neural Networks</div>
240+
<div class="module-header">Module 2: Convolutional Neural Networks</div>
241241

242-
<div class="materials-item">
243-
<a href="convolutional-networks/">
244-
Convolutional Neural Networks: Architectures, Convolution / Pooling Layers
245-
</a>
246-
<div class="kw">
247-
layers, spatial arrangement, layer patterns, layer sizing patterns, AlexNet/ZFNet/VGGNet case studies,
248-
computational considerations
242+
<div class="materials-item">
243+
<a href="convolutional-networks/">
244+
Convolutional Neural Networks: Architectures, Convolution / Pooling Layers
245+
</a>
246+
<div class="kw">
247+
layers, spatial arrangement, layer patterns, layer sizing patterns, AlexNet/ZFNet/VGGNet case studies,
248+
computational considerations
249+
</div>
249250
</div>
250-
</div>
251251

252-
<div class="materials-item">
253-
<a href="understanding-cnn/">
254-
Understanding and Visualizing Convolutional Neural Networks
255-
</a>
256-
<div class="kw">
257-
tSNE embeddings, deconvnets, data gradients, fooling ConvNets, human comparisons
252+
<div class="materials-item">
253+
<a href="understanding-cnn/">
254+
Understanding and Visualizing Convolutional Neural Networks
255+
</a>
256+
<div class="kw">
257+
tSNE embeddings, deconvnets, data gradients, fooling ConvNets, human comparisons
258+
</div>
258259
</div>
259-
</div>
260260

261-
<div class="materials-item">
262-
<a href="transfer-learning/">
263-
Transfer Learning and Fine-tuning Convolutional Neural Networks
264-
</a>
265-
</div>
261+
<div class="materials-item">
262+
<a href="transfer-learning/">
263+
Transfer Learning and Fine-tuning Convolutional Neural Networks
264+
</a>
265+
</div>
266266

267-
<div class="module-header">Student-Contributed Posts</div>
267+
<div class="module-header">Student-Contributed Posts</div>
268268

269-
<div class="materials-item">
270-
<a href="choose-project/">
271-
Taking a Course Project to Publication
272-
</a>
273-
<a href="rnn/">
274-
Recurrent Neural Networks
275-
</a>
276-
</div>
269+
<div class="materials-item">
270+
<a href="choose-project/">
271+
Taking a Course Project to Publication
272+
</a>
273+
<a href="rnn/">
274+
Recurrent Neural Networks
275+
</a>
276+
</div>
277277

278278
</div>
279279
</div>

0 commit comments

Comments
 (0)