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add progress bars

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1 parent 990d5cb commit 7802a7d80fc5b3ff2c21418b092b7057662780e1 @myungsub myungsub committed May 13, 2016
Showing with 42 additions and 5 deletions.
  1. +1 −0 css/main.css
  2. +41 −5 index.html
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@@ -128,6 +128,7 @@ a:visited { color: #205caa; }
/* Custom CSS rules for progress bar */
.progress {
position: relative;
+ font-size: 16px;
}
.progress span {
font-family: "Arial";
View
@@ -79,30 +79,36 @@
<a href="python-numpy-tutorial/">
Python / Numpy Tutorial
</a>
+ <span style="float:right" class="progress">
+ <progress value="0" max="100"></progress>
+ </span>
</div>
<div class="materials-item">
<a href="ipython-tutorial/">
IPython Notebook Tutorial
</a>
- <span style="float:right" class="progress">
- <progress value="100" max="100"></progress>
- <span>Complete!</span>
+ <span style="float:right;" class="progress">
+ Complete! <progress value="100" max="100"></progress>
</span>
</div>
<div class="materials-item">
<a href="terminal-tutorial/">
Terminal.com Tutorial
</a>
- <progress value="100" max="100"></progress> Complete!
+ <span style="float:right" class="progress">
+ Complete! <progress value="100" max="100"></progress>
+ </span>
</div>
<div class="materials-item">
<a href="aws-tutorial/">
AWS Tutorial
</a>
- <progress value="100" max="100"></progress> Complete!
+ <span style="float:right" class="progress">
+ Complete! <progress value="100" max="100"></progress>
+ </span>
</div>
<!-- hardcoding items here to force a specific order -->
@@ -112,6 +118,9 @@
<a href="classification/">
이미지 분류: 데이터 기반 방법론, k-Nearest Neighbor, train/val/test 구분
</a>
+ <span style="float:right" class="progress">
+ <progress value="150" max="291"></progress>
+ </span>
<div class="kw">
L1/L2 거리, hyperparameter 탐색, 교차검증(cross-validation)
</div>
@@ -121,6 +130,9 @@
<a href="linear-classify/">
선형 분류: Support Vector Machine, Softmax
</a>
+ <span style="float:right" class="progress">
+ <progress value="0" max="100"></progress>
+ </span>
<div class="kw">
parameteric 접근법, bias 트릭, hinge loss, cross-entropy loss, L2 regularization, 웹 데모
</div>
@@ -130,6 +142,9 @@
<a href="optimization-1/">
최적화: 확률 그라디언트 하강(Stochastic Gradient Descent)
</a>
+ <span style="float:right" class="progress">
+ Complete! <progress value="100" max="100"></progress>
+ </span>
<div class="kw">
'지형'으로서의 최적화 목적 함수 (optimization landscapes), 국소 탐색(local search), 학습 속도(learning rate), 해석적(analytic)/수치적(numerical) 그라디언트
</div>
@@ -139,6 +154,9 @@
<a href="optimization-2/">
Backpropagation, Intuition
</a>
+ <span style="float:right" class="progress">
+ <progress value="70" max="300"></progress>
+ </span>
<div class="kw">
연쇄 법칙 (chain rule) 해석, real-valued circuits, 그라디언트 흐름의 패턴
</div>
@@ -148,6 +166,9 @@
<a href="neural-networks-1/">
신경망 파트 1: 네트워크 구조 정하기
</a>
+ <span style="float:right" class="progress">
+ <progress value="32" max="220"></progress>
+ </span>
<div class="kw">
생물학적 뉴런 모델, 활성 함수(activation functions), 신경망 구조, 표현력(representational power)
</div>
@@ -157,6 +178,9 @@
<a href="neural-networks-2-kr/">
신경망 파트 2: 데이터 준비 및 Loss
</a>
+ <span style="float:right" class="progress">
+ <progress value="232" max="308"></progress>
+ </span>
<div class="kw">
전처리, weight 초기값 설정, 배치 정규화(batch normalization), regularization (L2/dropout), 손실함수
</div>
@@ -166,6 +190,9 @@
<a href="neural-networks-3/">
신경망 파트 3: 학습 및 평가
</a>
+ <span style="float:right" class="progress">
+ <progress value="81" max="390"></progress>
+ </span>
<div class="kw">
그라디언트 체크, 버그 점검, 학습 과정 모니터링, momentum (+nesterov), 2차(2nd-order) 방법, Adagrad/RMSprop, hyperparameter 최적화, 모델 ensemble
</div>
@@ -188,6 +215,9 @@
<a href="convolutional-networks/">
컨볼루션 신경망: 구조, Convolution / Pooling 레이어들
</a>
+ <span style="float:right" class="progress">
+ Complete! <progress value="100" max="100"></progress>
+ </span>
<div class="kw">
레이어(층), 공간적 배치, 레이어 패턴, 레이어 사이즈, AlexNet/ZFNet/VGGNet 사례 분석, 계산량에 관한 고려 사항들
</div>
@@ -197,6 +227,9 @@
<a href="understanding-cnn/">
컨볼루션 신경망 분석 및 시각화
</a>
+ <span style="float:right" class="progress">
+ <progress value="14" max="107"></progress>
+ </span>
<div class="kw">
tSNE embeddings, deconvnets, 데이터에 대한 그라디언트, ConvNet 속이기, 사람과의 비교
</div>
@@ -206,6 +239,9 @@
<a href="transfer-learning/">
Transfer Learning and Fine-tuning Convolutional Neural Networks
</a>
+ <span style="float:right" class="progress">
+ <progress value="0" max="100"></progress>
+ </span>
</div>
<div class="module-header">

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