From dc60dabbaccf48df553980317da7fe1636555983 Mon Sep 17 00:00:00 2001 From: metamath Date: Fri, 12 Jul 2019 10:27:10 +0900 Subject: [PATCH] =?UTF-8?q?=EC=8A=A4=ED=83=80=EC=9D=BC=20=EC=88=98?= =?UTF-8?q?=EC=A0=95?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- CNN/transconv_fullconv.ipynb | 4 + EM/Kmeans.ipynb | 6 +- GAN/GANs.ipynb | 15 +-- GAN/change_of_variable.ipynb | 94 ++++++++++++----- PRML/prml-chap2.ipynb | 18 +++- fitting/matrix-derivative.ipynb | 3 + fitting/product-of-gaussian.ipynb | 118 +++++++++++++++------- naive/naive.ipynb | 96 +++++++++++++----- perceptron/perceptron.ipynb | 4 + sampling/double-integral.ipynb | 7 +- simplenet/simplenet.ipynb | 58 ++++++----- svd/svd.ipynb | 14 ++- svm/duality.ipynb | 67 +++++++----- svm/duality_example.ipynb | 162 +++++++++++++++++++----------- 14 files changed, 448 insertions(+), 218 deletions(-) diff --git a/CNN/transconv_fullconv.ipynb b/CNN/transconv_fullconv.ipynb index 7108d7d..8eb60fe 100644 --- a/CNN/transconv_fullconv.ipynb +++ b/CNN/transconv_fullconv.ipynb @@ -36,6 +36,10 @@ "source": [ "# 합성곱 신경망에서 컨벌루션과 트랜스포즈드 컨벌루션의 관계 Relationship between Convolution and Transposed Convolution in CNN \n", "\n", + "

2019.05.09 조준우 metamath@gmail.com

\n", + "\n", + "
\n", + "\n", "이 문서의 목적은 CNN에서 CONV층의 포워드 패스 컨벌루션의 백워드 연산이 상류층 그래디언트를 필터로 하는 컨벌루션이며 이 백워드 패스 컨벌루션이 결국 포워드 패스의 컨벌루션에 대한 트랜스포즈드 컨벌루션transposed convolution이라는 것을 알아보고자 하는 것이다.\n", "\n", "CNN의 CONV층에서 일어나는 포워드 패스 연산을 컨벌루션convolution이라고 이야기하는데 정확하게 표현하면 이 연산을 코릴레이션correlation, [1]이라고 해야한다. 수학적으로 정의된 컨벌루션에 맞게 연산을 하려면 필터를 180도 돌리고 패딩을 줘서 연산을 해야한다. 그런데 신기하게도 코릴레이션의 백워드 패스 연산을 구해보면 정확하게 컨벌루션이 되는 것을 확인할 수 있다. 본 문서에서는 코릴레이션과 컨벌루션을 관행처럼 모두 컨벌루션이라고 이야기하고 구분이 필요한 경우 필터를 돌리지 않는 경우를 포워드 패스 컨벌루션, 필터를 돌려서 풀 컨벌루션하는 경우를 백워드 패스 컨벌루션으로 구분하도록 했다.\n", diff --git a/EM/Kmeans.ipynb b/EM/Kmeans.ipynb index b529e6e..626ac36 100644 --- a/EM/Kmeans.ipynb +++ b/EM/Kmeans.ipynb @@ -4,7 +4,11 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# K 평균 군집화 K-means Clustering\n" + "# K 평균 군집화 K-means Clustering\n", + "\n", + "

2019.07.10 조준우 metamath@gmail.com

\n", + "\n", + "
" ] }, { diff --git a/GAN/GANs.ipynb b/GAN/GANs.ipynb index d726291..5fcda1f 100644 --- a/GAN/GANs.ipynb +++ b/GAN/GANs.ipynb @@ -4,7 +4,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# GANs (Generative Adversarial Networks)" + "# GANs (Generative Adversarial Networks)\n", + "\n", + "

2017.05.10 조준우 metamath@gmail.com

\n", + "\n", + "
\n", + "\n" ] }, { @@ -1278,9 +1283,7 @@ }, { "cell_type": "markdown", - "metadata": { - "collapsed": true - }, + "metadata": {}, "source": [ "결과를 다시 한번 찍어보면 아래처럼 모양이 그럭저럭 잘 나오는 것을 확인할 수 있습니다." ] @@ -1350,9 +1353,7 @@ }, { "cell_type": "markdown", - "metadata": { - "collapsed": true - }, + "metadata": {}, "source": [ "
\n", "위 그래프를 보면 $z$=0 근방에서 기울기가 급하고 0.2~0.8까지 기울기가 완만하다가 0.8 이상에서 다시 기울기가 증가하는 모습을 확인할 수 있습니다. 히스토그림 결과를 보면 8 근방의 값이 목표보다 많이 생성되고 있는것이 확인되는데 이는 $G(\\boldsymbol{z})$의 매핑상태가 0.0~0.2구간보다 기울기가 완만하게 이루어져 있어서 그렇게 된 것입니다. 좀 더 이상적인 함수 $G(\\boldsymbol{z})$는 다음과 같은 모양이 될 것입니다.\n", diff --git a/GAN/change_of_variable.ipynb b/GAN/change_of_variable.ipynb index cb8f252..3b11155 100644 --- a/GAN/change_of_variable.ipynb +++ b/GAN/change_of_variable.ipynb @@ -5,6 +5,9 @@ "metadata": {}, "source": [ "# Change of continuous random variable\n", + "\n", + "

2017.06.09 조준우 metamath@gmail.com

\n", + "\n", "
\n", "\n", "어떤 확률 변수 $Z$가 있고 $X=g(Z)$관계에 의해 정의되는 확률변수 $X$가 있다. \n", @@ -312,9 +315,7 @@ }, { "cell_type": "markdown", - "metadata": { - "collapsed": true - }, + "metadata": {}, "source": [ "## 참고문헌\n", "
\n", @@ -338,18 +339,37 @@ { "data": { "text/html": [ - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", + "\n", + "\n", "" + " h1 { font-family: 'Noto Sans KR' !important; color:#348ABD !important; }\n", + " h2 { font-family: 'Noto Sans KR' !important; color:#467821 !important; }\n", + " h3 { font-family: 'Noto Sans KR' !important; color:#A60628 !important; }\n", + " h4 { font-family: 'Noto Sans KR' !important; color:#7A68A6 !important; } \n", + " \n", + " p:not(.navbar-text) { font-family: 'Noto Serif KR', 'Nanum Myeongjo'; font-size: 11pt; line-height: 200%; text-indent: 10px; }\n", + " li:not(.dropdown):not(.p-TabBar-tab):not(.p-MenuBar-item):not(.jp-DirListing-item):not(.p-CommandPalette-header):not(.p-CommandPalette-item):not(.jp-RunningSessions-item):not(.p-Menu-item) \n", + " { font-family: 'Noto Serif KR', 'Nanum Myeongjo'; font-size: 12pt; line-height: 200%; }\n", + " table { font-family: 'Noto Sans KR' !important; font-size: 11pt !important; } \n", + " li > p { text-indent: 0px; }\n", + " li > ul { margin-top: 0px !important; } \n", + " sup { font-family: 'Noto Sans KR'; font-size: 9pt; } \n", + " code, pre { font-family: D2Coding, 'D2 coding' !important; font-size: 11pt !important; line-height: 130% !important;}\n", + " .code-body { font-family: D2Coding, 'D2 coding' !important; font-size: 11pt !important;}\n", + " .ns { font-family: 'Noto Sans KR'; font-size: 15pt;}\n", + " .summary {\n", + " font-family: 'Georgia'; font-size: 12pt; line-height: 200%; \n", + " border-left:3px solid #D55E00; \n", + " padding-left:20px; \n", + " margin-top:10px;\n", + " margin-left:15px;\n", + " }\n", + " .green { color:#467821 !important; }\n", + " .comment { font-family: 'Noto Sans KR'; font-size: 10pt; }\n", + "\n" ], "text/plain": [ "" @@ -361,19 +381,45 @@ ], "source": [ "%%html\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", + "\n", + "\n", "" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { diff --git a/PRML/prml-chap2.ipynb b/PRML/prml-chap2.ipynb index 68c7dae..e6f9965 100644 --- a/PRML/prml-chap2.ipynb +++ b/PRML/prml-chap2.ipynb @@ -9,14 +9,17 @@ }, "source": [ "# Pattern Recognition and Machine Learning \n", - "\n", + "

2017.11.08 조준우 metamath@gmail.com

\n", + "
\n", "\n", "\n", "원제 : Pattern Recognition and Machine Learning \n", "\n", "지은이 : 크리스토퍼 비숍 Christopher Bishop\n", "\n", - "Springer\n" + "Springer\n", + "\n", + "\n" ] }, { @@ -2266,7 +2269,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 2, "metadata": {}, "outputs": [ { @@ -2288,7 +2291,7 @@ " .ns { font-family: 'Noto Sans KR'; font-size: 15pt;}\n", " .summary {font-family: 'Georgia'; font-size: 12pt; line-height: 200%; \n", " border-left:3px solid #FF0000;padding-left:20px;margin-top:10px; }\n", - "" + "\n" ], "text/plain": [ "" @@ -2318,6 +2321,13 @@ " border-left:3px solid #FF0000;padding-left:20px;margin-top:10px; }\n", "" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { diff --git a/fitting/matrix-derivative.ipynb b/fitting/matrix-derivative.ipynb index fee3cac..7c8c8e9 100644 --- a/fitting/matrix-derivative.ipynb +++ b/fitting/matrix-derivative.ipynb @@ -5,6 +5,9 @@ "metadata": {}, "source": [ "# 벡터, 행렬에 대한 미분\n", + "\n", + "

2018.01.02 조준우 metamath@gmail.com

\n", + "\n", "
\n", "\n" ] diff --git a/fitting/product-of-gaussian.ipynb b/fitting/product-of-gaussian.ipynb index 610b6cc..105028c 100644 --- a/fitting/product-of-gaussian.ipynb +++ b/fitting/product-of-gaussian.ipynb @@ -6,9 +6,10 @@ "source": [ "# 베이즈정리와 정규분포의 곱\n", "\n", + "

2017.08.17 조준우 metamath@gmail.com

\n", "\n", - "# 0. 들어가며\n", "
\n", + "\n", "\n", "\n", "- 베이즈정리에 의해 사후확률분포를 구하려면 가능도likelihood와 사전확률을 곱하고 이를 데이터의 주변확률로 나눠야 한다.
\n", @@ -30,8 +31,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# 1. 두 정규분포의 곱\n", - "
\n", + "## 1. 두 정규분포의 곱\n", "\n", "- 정규분포 두 개를 곱하면 정규화되지 않은 특정 상수가 곱해진 정규분포scaled gaussian가 됨을 알 수 있다. 두 정규분포가\n", "\n", @@ -151,8 +151,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# 2. 베이즈정리와 정규분포의 곱\n", - "
\n", + "## 2. 베이즈정리와 정규분포의 곱\n", "\n", "- 데이터의 분포가 정규분포라고 가정하고 베이즈룰에 따라 데이터에 대한 파라미터의 사후확률을 계산한다.\n", "\n", @@ -164,8 +163,7 @@ "p(\\mu \\,|\\, D) = \\frac{p(D \\,|\\, \\mu, \\sigma^2) \\, p(\\mu \\,|\\, \\mu_0, \\sigma^2_0)}{p(D)} \\tag{2.1}\n", "$$\n", "\n", - "## 2.1 가능도Likelihood\n", - "
\n", + "### 2.1 가능도Likelihood\n", "\n", "- 관측 데이터를 i.i.d로 가정하면 다음과 같이 가능도 함수는 각 데이터의 확률분포의 곱이다.\n", "\n", @@ -178,8 +176,7 @@ "\\end{align*} \\tag{2.2}\n", "$$\n", "\n", - "## 2.2 사전확률Prior\n", - "
\n", + "### 2.2 사전확률Prior\n", "\n", "- 공액 사전확률로 정규분포를 파라미터 $\\mu$의 사전확률분포로 선택한다.\n", "\n", @@ -192,8 +189,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## 2.3 가능도 $\\times$ 사전확률\n", - "
\n", + "### 2.3 가능도 $\\times$ 사전확률\n", "\n", "- 식(2.1)의 분자 계산을 위해 식(2.2)와 식(2.3)을 곱한다.\n", "\n", @@ -298,8 +294,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## 2.3 Marginal Likelihood\n", - "
\n", + "### 2.3 Marginal Likelihood\n", "\n", "- 확률의 합법칙은 다음과 같다.\n", "\n", @@ -396,8 +391,8 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# 3. 일변수 정규분포의 곱으로써의 다변수 정규분포 (참고)\n", - "
\n", + "## 3. 일변수 정규분포의 곱으로써의 다변수 정규분포 (참고)\n", + "\n", "\n", "- 1 장에서 두 일변수 정규분포의 곱이 정규화되지 않은 정규분포가 되는 것을 확인했었다. \n", "\n", @@ -655,8 +650,8 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# 4. 참고문헌\n", - "
\n", + "## 4. 참고문헌\n", + "\n", "\n", "1. Products and Convolutions of Gaussian Probability Density Functions, P.A. Bromiley, http://www.tina-vision.net/docs/memos/2003-003.pdf\n", "\n", @@ -671,24 +666,43 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "metadata": {}, "outputs": [ { "data": { "text/html": [ - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", + "\n", + "\n", "" + " h1 { font-family: 'Noto Sans KR' !important; color:#348ABD !important; }\n", + " h2 { font-family: 'Noto Sans KR' !important; color:#467821 !important; }\n", + " h3 { font-family: 'Noto Sans KR' !important; color:#A60628 !important; }\n", + " h4 { font-family: 'Noto Sans KR' !important; color:#7A68A6 !important; } \n", + " \n", + " p:not(.navbar-text) { font-family: 'Noto Serif KR', 'Nanum Myeongjo'; font-size: 11pt; line-height: 200%; text-indent: 10px; }\n", + " li:not(.dropdown):not(.p-TabBar-tab):not(.p-MenuBar-item):not(.jp-DirListing-item):not(.p-CommandPalette-header):not(.p-CommandPalette-item):not(.jp-RunningSessions-item):not(.p-Menu-item) \n", + " { font-family: 'Noto Serif KR', 'Nanum Myeongjo'; font-size: 12pt; line-height: 200%; }\n", + " table { font-family: 'Noto Sans KR' !important; font-size: 11pt !important; } \n", + " li > p { text-indent: 0px; }\n", + " li > ul { margin-top: 0px !important; } \n", + " sup { font-family: 'Noto Sans KR'; font-size: 9pt; } \n", + " code, pre { font-family: D2Coding, 'D2 coding' !important; font-size: 11pt !important; line-height: 130% !important;}\n", + " .code-body { font-family: D2Coding, 'D2 coding' !important; font-size: 11pt !important;}\n", + " .ns { font-family: 'Noto Sans KR'; font-size: 15pt;}\n", + " .summary {\n", + " font-family: 'Georgia'; font-size: 12pt; line-height: 200%; \n", + " border-left:3px solid #D55E00; \n", + " padding-left:20px; \n", + " margin-top:10px;\n", + " margin-left:15px;\n", + " }\n", + " .green { color:#467821 !important; }\n", + " .comment { font-family: 'Noto Sans KR'; font-size: 10pt; }\n", + "\n" ], "text/plain": [ "" @@ -700,19 +714,45 @@ ], "source": [ "%%html\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", + "\n", + "\n", "" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { diff --git a/naive/naive.ipynb b/naive/naive.ipynb index 0d13e1c..5a69ccd 100644 --- a/naive/naive.ipynb +++ b/naive/naive.ipynb @@ -5,6 +5,10 @@ "metadata": {}, "source": [ "# 나이브 베이즈Naive Bayes\n", + "\n", + "\n", + "

2017.05.19 조준우 metamath@gmail.com

\n", + "\n", "
\n", "\n", "\n", @@ -16,7 +20,6 @@ "metadata": {}, "source": [ "## 1. 문제 정의\n", - "
\n", "\n", "어떤 문서(또는 메세지) $d$가 있을 때 이 문서가 스팸($S$)클래스에 속하는지 일반 문서($ \\neg S$)클래스에 속하는지 분류하는 문제가 있다고 하자. 그리고 우리에게 꽤 많은 단어들에 대해서 그 단어가 스팸메일에 나타날 확률과 일반메일에 나타날 확률에 대한 데이터를 가지고 있다고 하자. 예를 들면 \"Sex는 일반메일에서 3%확률로 나타나고, 스팸메일에서 60%확률로 나타난다\" 이런 데이터를 수만개 단어에 대해서 가지고 있다는 것이다. 기호로 적으면 $P(\\text{'Naive'}|S)$와 $P(\\text{'Naive'}|\\neg S)$를 데이터로 가지고 있는 것이다. 우리가 풀고 싶은 문제는 $P(S\\,|\\,d)$ 와 $P(\\neg S\\,|\\,d)$중 어느쪽이 더 큰가 하는 문제이다. $P(S\\,|\\,d)$ 는 문서 $d$가 주어졌다는 가정하에서 그 문서가 스팸일 확률 즉 조건부 확률을 나타낸다. $P(\\neg S\\,|\\,d)$는 문서 $d$가 주어졌다는 가정하에서 그 문서가 스팸이 아닐 확률을 나타낸다.\n", "해당 문제를 해결하기 위해 주어진 도큐먼트로 부터 판단에 사용할 특징feature를 추출해야 하는데 이때 어떤 확률분포를 사용하는지에 따라 특징벡터가 달라지게 된다. 일단 그 내용은 잠시뒤로 미루고 지금은 $d$로 부터 단어를 추출하여 특징 벡터 $\\boldsymbol{x}=(x_{1}, x_{2}, ... , x_{n})$가 준비되었다하고 이야기를 계속 하자. 그럼 특징벡터 $\\boldsymbol{x}$가 관찰되었을 때 그 벡터가 S에 속할 확률은 베이즈정리를 사용해서 아래와 같이 쓸 수 있다.\n", @@ -38,8 +41,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## 2. 실습\n", - "
" + "## 2. 실습\n" ] }, { @@ -229,12 +231,16 @@ "\n", "우리의 단어장에 10개의 단어가 있다고 가정하자. 그리고 아래 그림처럼 그 단어중 하나를 임의로 뽑아 어떤 $C_{k}$에 속하는 주어진 도큐먼트 $d$에 나타나면 1, 나타나지 않으면 0을 가지는 확률변수 $X$가 있다 하자. 이 이산확률변수의 확률분포는 베르누이 분포가 되고 만약 $X$의 대응 관계가 아래 그림과 같다면 확률질량함수는 $ P(X=x) = p^{x}(1-p)^{1-x} $이다. 여기서 $x$는 $w_i$가 $d$에 나타나면 1, 안나타나면 0을 가지는 바이너리 변수이고 $p=\\frac{4}{10}$이다. \n", "\n", + "\n", " \n", "\n", + "\n", "나이브 베이즈에서 정의되는 확률변수는 위의 경우와 조금 다르다. 각 $w_{i}$마다 확률 변수를 하나씩 대응시켜 아래 그림과 같이 정의 한다. \n", "\n", + "\n", " \n", "\n", + "\n", "이 경우 각 확률변수 $X_i$에 대해 $ P(X_{i}=x) = p_{i}^{x}(1-p_{i})^{1-x} $이고 주어진 도큐먼트는 $C_k$에 속하는 도큐먼트라 했으므로 여기서 $p_{i}$라 함은 $w_i$가 $C_k$에 나타날 확률이 된다. $p_{i}=P(w_{i}\\,|\\,C_{k})$이다. 이제 $P(w_{i}\\,|\\,C_{k})$를 어떻게 구할 것인가 생각해봐야 한다. (우리 문제에서는 $k=2$인 경우로 $C_{1}=S$, $C_{2}= \\neg S $ 로 생각하면 됨) 특정 단어 $w_{i}$가 문서에 나타날 확률분포는 자연계에 분명 존재할 것이다. 우리가 그것을 정확히 모를 뿐이다. $w_{i}$가 제약이 없이 그냥 문서에 나타날 확률(엄밀히 말하면 문서라는것도 일종의 제약이다)과 특정 제약이 있는 예를 들어 스팸문서에 나타날 확률은 엄연히 다를 것이다. 우리는 메일중에서 스팸메일에 나타날 확률, 일반메일에 나타날 확률을 알면 되므로 $P(w_{i}\\,|\\,S)$, $P(w_{i}\\,|\\,\\neg S)$를 알고 싶은 것이다. 자연상태에 존재하는 이 확률분포를 정확히 알 방법은 없다. 따라서 우리가 가지고 있는 데이터를 바탕으로 근사된 $\\hat{P}(w_{i}\\,|\\,S)$, $\\hat{P}(w_{i}\\,|\\,\\neg S)$ 구하여야 한다.\n", "\n", "베르누이 모델에서 단어장 $V$ 중 특정 단어 $w_i$가 특정 문서 클래스에 나타날 확률은\n", @@ -627,9 +633,7 @@ }, { "cell_type": "markdown", - "metadata": { - "collapsed": true - }, + "metadata": {}, "source": [ "두 경우 모두에서 코드 몇줄로는 썩 나쁘지 않은 결과를 나타내었음을 확인할 수 있다.\n" ] @@ -639,7 +643,7 @@ "metadata": {}, "source": [ "## 3. 참고문헌\n", - "
\n", + "\n", "\n", "1. 밑바닥부터 시작하는 데이터 과학Data Science from Scratch, 한빛미디어, Orelly, 조엘 그루스\n", "\n", @@ -650,22 +654,43 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ - "\n", - "\n", + "\n", + "\n", + "\n", + "\n", + "\n", "" + " h1 { font-family: 'Noto Sans KR' !important; color:#348ABD !important; }\n", + " h2 { font-family: 'Noto Sans KR' !important; color:#467821 !important; }\n", + " h3 { font-family: 'Noto Sans KR' !important; color:#A60628 !important; }\n", + " h4 { font-family: 'Noto Sans KR' !important; color:#7A68A6 !important; } \n", + " \n", + " p:not(.navbar-text) { font-family: 'Noto Serif KR', 'Nanum Myeongjo'; font-size: 11pt; line-height: 200%; text-indent: 10px; }\n", + " li:not(.dropdown):not(.p-TabBar-tab):not(.p-MenuBar-item):not(.jp-DirListing-item):not(.p-CommandPalette-header):not(.p-CommandPalette-item):not(.jp-RunningSessions-item):not(.p-Menu-item) \n", + " { font-family: 'Noto Serif KR', 'Nanum Myeongjo'; font-size: 12pt; line-height: 200%; }\n", + " table { font-family: 'Noto Sans KR' !important; font-size: 11pt !important; } \n", + " li > p { text-indent: 0px; }\n", + " li > ul { margin-top: 0px !important; } \n", + " sup { font-family: 'Noto Sans KR'; font-size: 9pt; } \n", + " code, pre { font-family: D2Coding, 'D2 coding' !important; font-size: 11pt !important; line-height: 130% !important;}\n", + " .code-body { font-family: D2Coding, 'D2 coding' !important; font-size: 11pt !important;}\n", + " .ns { font-family: 'Noto Sans KR'; font-size: 15pt;}\n", + " .summary {\n", + " font-family: 'Georgia'; font-size: 12pt; line-height: 200%; \n", + " border-left:3px solid #D55E00; \n", + " padding-left:20px; \n", + " margin-top:10px;\n", + " margin-left:15px;\n", + " }\n", + " .green { color:#467821 !important; }\n", + " .comment { font-family: 'Noto Sans KR'; font-size: 10pt; }\n", + "\n" ], "text/plain": [ "" @@ -677,15 +702,36 @@ ], "source": [ "%%html\n", - "\n", - "\n", + "\n", + "\n", + "\n", + "\n", + "\n", "" ] } diff --git a/perceptron/perceptron.ipynb b/perceptron/perceptron.ipynb index 676b2da..b1dfc2c 100644 --- a/perceptron/perceptron.ipynb +++ b/perceptron/perceptron.ipynb @@ -5,6 +5,10 @@ "metadata": {}, "source": [ "# 퍼셉트론 테스트와 수렴정리Perceptron test and its convergence theorem \n", + "\n", + "\n", + "

2017.05.12 조준우 metamath@gmail.com

\n", + "\n", "
\n", "\n", "\n", diff --git a/sampling/double-integral.ipynb b/sampling/double-integral.ipynb index efbb945..9a69bdf 100644 --- a/sampling/double-integral.ipynb +++ b/sampling/double-integral.ipynb @@ -5,12 +5,15 @@ "metadata": {}, "source": [ "# 야코비안 Jacobian과 치환적분\n", + "\n", + "

2017.10.08 조준우 metamath@gmail.com

\n", + "\n", "
\n", "\n", "야코비안의 의미를 완전히 이해하기 위해 고등학교 치환적분부터 시작해서 다변수 함수에서 이야기하는 야코비안의 의미까지 정리한다. 약간의 다변수 벡터함수에 대한 지식만 있으면 이해할 수 있도록 자세히 풀어적었다.\n", "\n", "## 1. 일변수 함수에서 치환적분과 야코비안\n", - "
\n", + "\n", "\n", "고등학교 치환적분에서 다음과 같은 식이 나온다. $x = g(t)$의 관계가 있을 때 \n", "\n", @@ -364,7 +367,7 @@ "metadata": {}, "source": [ "## 2. 다변수 함수에서 치환적분과 야코비안\n", - "
\n", + "\n", "\n", "많은 문서에서 설명하는 방법이 조금씩 달라서 엄청 햇갈리는데 용어 정리부터 한다.\n", "\n", diff --git a/simplenet/simplenet.ipynb b/simplenet/simplenet.ipynb index c38ec6c..1fd5a17 100644 --- a/simplenet/simplenet.ipynb +++ b/simplenet/simplenet.ipynb @@ -5,6 +5,9 @@ "metadata": {}, "source": [ "# 150줄로 된 간단한 네트워크Simple network with 150 lines\n", + "\n", + "

2017.05.19 조준우 metamath@gmail.com

\n", + "\n", "
\n", "\n", "여기에서는 아주 간단한 네트워크를 직접 구현해 보는 것을 목표로 한다. \n", @@ -12,7 +15,6 @@ "따라서 코드는 절차지향적으로 작성되었으며 Network을 정의하는 부분만 Class를 사용하였다. 나머지는 모두 데이터가 입력되고 웨이트와 곱해지고 하는 네트워크의 절차적 흐름을 최대한 가독성 높게 구현하려고 하였다.\n", "\n", "## 1. 구현 요구 사항\n", - "
\n", "\n", "- 당연히 Numpy로만 구현\n", "\n", @@ -42,7 +44,6 @@ "\n", "\n", "## 2. 구현 세부 사항\n", - "
\n", "\n", "구현에 있어서 가장 어려운 부분은 \n", "\n", @@ -993,9 +994,7 @@ }, { "cell_type": "markdown", - "metadata": { - "collapsed": true - }, + "metadata": {}, "source": [ "| Layers | Actvations | Cost | Epoches | Mini-batch | Leanrning rate | Test Acc. | etc |\n", "|--------------|------------|-------------|---------|------------|----------------|-------|-----|\n", @@ -1017,9 +1016,7 @@ }, { "cell_type": "markdown", - "metadata": { - "collapsed": true - }, + "metadata": {}, "source": [ "이상으로 간단한 네트워크를 작성해보았다. 애초에 의도했던대로 Numpy로만 150줄내외에서 (주석지우고 softmax, cross entropy 축약버전, 그림 그리는 코드, 마지막 테스트 코드 제외하면 165줄;;;;) 구현 요구사항을 만족시키도록 작성했다.\n" ] @@ -1038,29 +1035,31 @@ "\n", "\n", "" + "\n" ], "text/plain": [ "" @@ -1078,22 +1077,24 @@ "\n", "\n", "" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { diff --git a/svd/svd.ipynb b/svd/svd.ipynb index d9e0ebb..d508088 100644 --- a/svd/svd.ipynb +++ b/svd/svd.ipynb @@ -1,5 +1,16 @@ { "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 대칭행렬의 대각화와 특잇값 분해 Symmetric matrix Diagonalization and Singular Value Decomposition\n", + "\n", + "

2018.06.20 조준우 metamath@gmail.com

\n", + "\n", + "
" + ] + }, { "cell_type": "code", "execution_count": 1, @@ -22,9 +33,6 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# 대칭행렬의 대각화와 특잇값 분해\n", - "# Symmetric matrix Diagonalization and Singular Value Decomposition\n", - "\n", "학부 수준 선형대수 강의의 마지막 과정으로 특잇값 분해Singular Value Decomposition, 이하 SVD를 많이 다루게 된다. 선형대수에 나오는 약간 고급 개념을 이용하기 때문에 거의 마지막 과정으로 다루게 되는데 설명 과정중에 필연적으로 나오게 되는 대칭행렬의 대각화에 대한 내용을 잘 찾아볼 수 없어서 여러 문서를 참고하여 정리를 해보았다.\n", "\n", "선형대수에서 많이 햇갈리는 용어와 개념들을 먼저 간단히 정리하고 대칭행렬의 대각화에 대해 이야기한다.\n", diff --git a/svm/duality.ipynb b/svm/duality.ipynb index 800abcf..b53a1c0 100644 --- a/svm/duality.ipynb +++ b/svm/duality.ipynb @@ -1,5 +1,23 @@ { "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 서포트벡터머신Support Vector Machine을 위한 비선형 계획 문제의 쌍대정리\n", + "\n", + "

2017.10.08 조준우 metamath@gmail.com

\n", + "\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "서포트벡터머신(이하 SVM)을 공부하다 보면 마지막 과정에서 쌍대문제라는 것이 등장한다. 최소화 문제인 원래 문제를 라그랑지 승수에 대한 최대화 문제로 바꿔서 풀게 되는데 이 때 바뀐 최대화 문제를 원래 문제의 쌍대 문제라 한다. 이 두 문제가 어째서 같은 해를 주게 되는지에 대한 설명은 좀 진지한 SVM 문헌이 아니고서는 잘 나오지 않는다. 그 이유는 쌍대문제를 푸는 궁극적인 목적이 커널트릭에 있으므로 쌍대문제를 그냥 받아드리고 쌍대문제에서 나타나는 커널함수에 대한 설명을 중점적으로 하기 때문인 것으로 보인다. 본 문서는 쌍대문제가 정의되는 과정을 가능한 자세한 수식과 충분한 그림을 중심으로 이야기함으로 원문제와 쌍대문제의 관계를 알아보는 것을 목적으로 한다. 본 글의 주 참고문헌은 [arora_2] 5.5절 DUALITY IN NONLINEAR PROGRAMMING이며, [nocedal], [bazaraa], [boyd]의 내용을 부분 부분 함께 정리, 보충 설명하였다. 참고로 본 문서에서 벡터 미분은 분모 레이아웃을 따른다. 대부분 참고문헌에서 분모 레이아웃을 쓰기 때문에 기호법을 일치시켜 혼란을 방지하기 위함이다. 벡터 미분의 분자, 분모 레이아웃에 대한 자세한 내용은 [jo]를 참고하면 된다.\n" + ] + }, { "cell_type": "code", "execution_count": 1, @@ -63,20 +81,6 @@ "))" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 서포트벡터머신Support Vector Machine을 위한 비선형 계획 문제의 쌍대정리" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "서포트벡터머신(이하 SVM)을 공부하다 보면 마지막 과정에서 쌍대문제라는 것이 등장한다. 최소화 문제인 원래 문제를 라그랑지 승수에 대한 최대화 문제로 바꿔서 풀게 되는데 이 때 바뀐 최대화 문제를 원래 문제의 쌍대 문제라 한다. 이 두 문제가 어째서 같은 해를 주게 되는지에 대한 설명은 좀 진지한 SVM 문헌이 아니고서는 잘 나오지 않는다. 그 이유는 쌍대문제를 푸는 궁극적인 목적이 커널트릭에 있으므로 쌍대문제를 그냥 받아드리고 쌍대문제에서 나타나는 커널함수에 대한 설명을 중점적으로 하기 때문인 것으로 보인다. 본 문서는 쌍대문제가 정의되는 과정을 가능한 자세한 수식과 충분한 그림을 중심으로 이야기함으로 원문제와 쌍대문제의 관계를 알아보는 것을 목적으로 한다. 본 글의 주 참고문헌은 [arora_2] 5.5절 DUALITY IN NONLINEAR PROGRAMMING이며, [nocedal], [bazaraa], [boyd]의 내용을 부분 부분 함께 정리, 보충 설명하였다. 참고로 본 문서에서 벡터 미분은 분모 레이아웃을 따른다. 대부분 참고문헌에서 분모 레이아웃을 쓰기 때문에 기호법을 일치시켜 혼란을 방지하기 위함이다. 벡터 미분의 분자, 분모 레이아웃에 대한 자세한 내용은 [jo]를 참고하면 된다.\n" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -33559,33 +33563,42 @@ }, "xaxis": { "showspikes": false, - "title": "x", - "titlefont": { - "family": "Courier New, monospace", - "size": 20 + "title": { + "font": { + "family": "Courier New, monospace", + "size": 20 + }, + "text": "x" } }, "yaxis": { "showspikes": false, - "title": "u", - "titlefont": { - "family": "Courier New, monospace", - "size": 20 + "title": { + "font": { + "family": "Courier New, monospace", + "size": 20 + }, + "text": "u" } }, "zaxis": { "showspikes": false, - "title": "L", - "titlefont": { - "family": "Courier New, monospace", - "size": 20 + "title": { + "font": { + "family": "Courier New, monospace", + "size": 20 + }, + "text": "L" } } }, - "title": "Lagrangian", + "title": { + "text": "Lagrangian" + }, "width": 600 } }, + "image/png": 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", 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