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index.html
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<!doctype html>
<html style='font-size:15px !important'>
<head>
<meta charset='UTF-8'><meta name='viewport' content='width=device-width initial-scale=1'>
<title>README</title><link href='https://fonts.loli.net/css?family=Open+Sans:400italic,700italic,700,400&subset=latin,latin-ext' rel='stylesheet' type='text/css' /><style type='text/css'>html {overflow-x: initial !important;}:root { --bg-color:#ffffff; --text-color:#333333; --select-text-bg-color:#B5D6FC; --select-text-font-color:auto; --monospace:"Lucida Console",Consolas,"Courier",monospace; }
html { font-size: 14px; background-color: var(--bg-color); color: var(--text-color); font-family: "Helvetica Neue", Helvetica, Arial, sans-serif; -webkit-font-smoothing: antialiased; }
body { margin: 0px; padding: 0px; height: auto; bottom: 0px; top: 0px; left: 0px; right: 0px; font-size: 1rem; line-height: 1.42857; overflow-x: hidden; background: inherit; tab-size: 4; }
iframe { margin: auto; }
a.url { word-break: break-all; }
a:active, a:hover { outline: 0px; }
.in-text-selection, ::selection { text-shadow: none; background: var(--select-text-bg-color); color: var(--select-text-font-color); }
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body.typora-export { padding-left: 30px; padding-right: 30px; }
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.md-toc-h6 .md-toc-inner { margin-left: 10em; }
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a.md-print-anchor { white-space: pre !important; border-width: initial !important; border-style: none !important; border-color: initial !important; display: inline-block !important; position: absolute !important; width: 1px !important; right: 0px !important; outline: 0px !important; background: 0px 0px !important; text-decoration: initial !important; text-shadow: initial !important; }
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.md-math-block .MathJax_SVG_Display { text-align: center; margin: 0px; position: relative; text-indent: 0px; max-width: none; max-height: none; min-height: 0px; min-width: 100%; width: auto; overflow-y: hidden; display: block !important; }
.MathJax_SVG_Display, .md-inline-math .MathJax_SVG_Display { width: auto; margin: inherit; display: inline-block !important; }
.MathJax_SVG .MJX-monospace { font-family: var(--monospace); }
.MathJax_SVG .MJX-sans-serif { font-family: sans-serif; }
.MathJax_SVG { display: inline; font-style: normal; font-weight: 400; line-height: normal; zoom: 90%; text-indent: 0px; text-align: left; text-transform: none; letter-spacing: normal; word-spacing: normal; overflow-wrap: normal; white-space: nowrap; float: none; direction: ltr; max-width: none; max-height: none; min-width: 0px; min-height: 0px; border: 0px; padding: 0px; margin: 0px; }
.MathJax_SVG * { transition: none 0s ease 0s; }
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.md-diagram-panel > svg { max-width: 100%; }
[lang="flow"] svg, [lang="mermaid"] svg { max-width: 100%; height: auto; }
[lang="mermaid"] .node text { font-size: 1rem; }
table tr th { border-bottom: 0px; }
video { max-width: 100%; display: block; margin: 0px auto; }
iframe { max-width: 100%; width: 100%; border: none; }
.highlight td, .highlight tr { border: 0px; }
svg[id^="mermaidChart"] { line-height: 1em; }
mark { background: rgb(255, 255, 0); color: rgb(0, 0, 0); }
.md-html-inline .md-plain, .md-html-inline strong, mark .md-inline-math, mark strong { color: inherit; }
mark .md-meta { color: rgb(0, 0, 0); opacity: 0.3 !important; }
:root {
--side-bar-bg-color: #fafafa;
--control-text-color: #777;
}
@include-when-export url(https://fonts.loli.net/css?family=Open+Sans:400italic,700italic,700,400&subset=latin,latin-ext);
/* open-sans-regular - latin-ext_latin */
/* open-sans-italic - latin-ext_latin */
/* open-sans-700 - latin-ext_latin */
/* open-sans-700italic - latin-ext_latin */
html {
font-size: 16px;
}
body {
font-family: "Open Sans","Clear Sans", "Helvetica Neue", Helvetica, Arial, sans-serif;
color: rgb(51, 51, 51);
line-height: 1.6;
}
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max-width: 860px;
margin: 0 auto;
padding: 30px;
padding-bottom: 100px;
}
@media only screen and (min-width: 1400px) {
#write {
max-width: 1024px;
}
}
@media only screen and (min-width: 1800px) {
#write {
max-width: 1200px;
}
}
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#write > ol:first-child{
margin-top: 30px;
}
a {
color: #4183C4;
}
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h2,
h3,
h4,
h5,
h6 {
position: relative;
margin-top: 1rem;
margin-bottom: 1rem;
font-weight: bold;
line-height: 1.4;
cursor: text;
}
h1:hover a.anchor,
h2:hover a.anchor,
h3:hover a.anchor,
h4:hover a.anchor,
h5:hover a.anchor,
h6:hover a.anchor {
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}
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h1 code {
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}
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h2 code {
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}
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h3 code {
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}
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}
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h6 code {
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}
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font-size: 2.25em;
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border-bottom: 1px solid #eee;
}
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padding-bottom: .3em;
font-size: 1.75em;
line-height: 1.225;
border-bottom: 1px solid #eee;
}
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}
h4 {
font-size: 1.25em;
}
h5 {
font-size: 1em;
}
h6 {
font-size: 1em;
color: #777;
}
p,
blockquote,
ul,
ol,
dl,
table{
margin: 0.8em 0;
}
li>ol,
li>ul {
margin: 0 0;
}
hr {
height: 2px;
padding: 0;
margin: 16px 0;
background-color: #e7e7e7;
border: 0 none;
overflow: hidden;
box-sizing: content-box;
}
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display: inline-block;
}
ul,
ol {
padding-left: 30px;
}
ul:first-child,
ol:first-child {
margin-top: 0;
}
ul:last-child,
ol:last-child {
margin-bottom: 0;
}
blockquote {
border-left: 4px solid #dfe2e5;
padding: 0 15px;
color: #777777;
}
blockquote blockquote {
padding-right: 0;
}
table {
padding: 0;
word-break: initial;
}
table tr {
border-top: 1px solid #dfe2e5;
margin: 0;
padding: 0;
}
table tr:nth-child(2n),
thead {
background-color: #f8f8f8;
}
table tr th {
font-weight: bold;
border: 1px solid #dfe2e5;
border-bottom: 0;
margin: 0;
padding: 6px 13px;
}
table tr td {
border: 1px solid #dfe2e5;
margin: 0;
padding: 6px 13px;
}
table tr th:first-child,
table tr td:first-child {
margin-top: 0;
}
table tr th:last-child,
table tr td:last-child {
margin-bottom: 0;
}
.CodeMirror-lines {
padding-left: 4px;
}
.code-tooltip {
box-shadow: 0 1px 1px 0 rgba(0,28,36,.3);
border-top: 1px solid #eef2f2;
}
.md-fences,
code,
tt {
border: 1px solid #e7eaed;
background-color: #f8f8f8;
border-radius: 3px;
padding: 0;
padding: 2px 4px 0px 4px;
font-size: 0.9em;
}
code {
background-color: #f3f4f4;
padding: 0 2px 0 2px;
}
.md-fences {
margin-bottom: 15px;
margin-top: 15px;
padding-top: 8px;
padding-bottom: 6px;
}
.md-task-list-item > input {
margin-left: -1.3em;
}
@media print {
html {
font-size: 13px;
}
table,
pre {
page-break-inside: avoid;
}
pre {
word-wrap: break-word;
}
}
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background-color: #f8f8f8;
}
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padding: 1rem;
font-size: 85%;
line-height: 1.45;
background-color: #f7f7f7;
border: 0;
border-radius: 3px;
color: #777777;
margin-top: 0 !important;
}
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bottom: .375rem;
}
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background: #fafafa;
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top: .375rem;
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top: .285714286rem;
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top: .285714286rem;
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border-radius: 3px;
padding: 2px 0px 0px 4px;
font-size: 0.9em;
color: inherit;
}
.md-tag {
color: #a7a7a7;
opacity: 1;
}
.md-toc {
margin-top:20px;
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<div id='write' class = ''><h1><a name="ml-notes" class="md-header-anchor"></a><span>ML-notes</span></h1><p><a href='https://github.com/Sakura-gh/ML-notes/issues'><img src="https://img.shields.io/github/issues/Sakura-gh/ML-notes?color=ffa07a" referrerpolicy="no-referrer" alt="GitHub issues"></a><span> </span><a href='https://github.com/Sakura-gh/ML-notes/network'><img src="https://img.shields.io/github/forks/Sakura-gh/ML-notes?color=20b2aa" referrerpolicy="no-referrer" alt="GitHub forks"></a><span> </span><a href='https://github.com/Sakura-gh/ML-notes/stargazers'><img src="https://img.shields.io/github/stars/Sakura-gh/ML-notes?color=66cdaa" referrerpolicy="no-referrer" alt="GitHub stars"></a><span> </span><a href='https://github.com/Sakura-gh/ML-notes/blob/master/LICENSE'><img src="https://img.shields.io/github/license/Sakura-gh/ML-notes?color=88cff1" referrerpolicy="no-referrer" alt="GitHub license"></a></p><p><span>notes about machine learning</span></p><p><span>很喜欢一句话：</span><strong><span>应用之道，存乎一心</span></strong><span>，与大家共勉</span></p><p><span>ps：如果我的笔记对你有帮助，给个star叭！查看机器学习笔记的PDF订阅版(</span><strong><span>301页</span></strong><span>)以及更多计算机相关笔记，欢迎大家关注微信公众号"Sakura的知识库"~</span></p><center><img src="https://cdn.jsdelivr.net/gh/Sakura-gh/ML-notes/img/wx.jpg"></center><h5><a name="ml-assignments" class="md-header-anchor"></a><span>ML-Assignments</span></h5><p><span>ML配套Assignments (ppt+code)：</span><a href='https://github.com/Sakura-gh/ML-assignments' target='_blank' class='url'>https://github.com/Sakura-gh/ML-assignments</a></p><p><span>内容包括：Regression, Classification, CNN, RNN, Explainable AI, Adversarial Attack, Network Compression, Seq2Seq, GAN, Transfer Learning, Meta Learning, Life-long Learning, Reforcement Learning. </span></p><h5><a name="pages" class="md-header-anchor"></a><span>pages</span></h5><p><span>the github page is: </span><a href='https://Sakura-gh.github.io/ML-notes' target='_blank' class='url'>https://Sakura-gh.github.io/ML-notes</a></p><p><span>you can also visit gitee page for quicker Internet in China: </span><a href='https://Sakura-gh.gitee.io/ml-notes' target='_blank' class='url'>https://Sakura-gh.gitee.io/ml-notes</a></p><h5><a name="html链接" class="md-header-anchor"></a><span>html链接：</span></h5><p><a href=' https://sakura-gh.github.io/ML-notes/ML-notes-html/1_Introduction.html'><span>1_Introduction</span></a></p><p><a href=' https://sakura-gh.github.io/ML-notes/ML-notes-html/2_Regression-Case-Study.html'><span>2_Regression Case Study</span></a></p><p><a href=' https://sakura-gh.github.io/ML-notes/ML-notes-html/3_Regression-demo(Adagrad).html'><span>3_Regression demo(Adagrad)</span></a></p><p><a href='https://sakura-gh.github.io/ML-notes/ML-notes-html/4_Where-does-the-error-come-from.html'><span>4_Where does the error come from</span></a></p><p><a href='https://sakura-gh.github.io/ML-notes/ML-notes-html/5_Gradient-Descent.html'><span>5_Gradient Descent</span></a></p><p><a href='https://sakura-gh.github.io/ML-notes/ML-notes-html/6_Classification.html'><span>6_Classification</span></a></p><p><a href='https://sakura-gh.github.io/ML-notes/ML-notes-html/7_Logistic-Regression.html'><span>7_Logistic Regression</span></a></p><p><a href='https://sakura-gh.github.io/ML-notes/ML-notes-html/8_Deep-Learning.html'><span>8_Deep Learning</span></a></p><p><a href='https://sakura-gh.github.io/ML-notes/ML-notes-html/9_Backpropagation.html'><span>9_Backpropagation</span></a></p><p><a href='https://sakura-gh.github.io/ML-notes/ML-notes-html/10_Keras.html'><span>10_Keras</span></a></p><p><a href='https://sakura-gh.github.io/ML-notes/ML-notes-html/11_Convolutional-Neural-Network-part1.html'><span>11_Convolutional Neural Network part1</span></a></p><p><a href='https://sakura-gh.github.io/ML-notes/ML-notes-html/12_Convolutional-Neural-Network-part2.html'><span>12_Convolutional Neural Network part2</span></a></p><p><a href='https://sakura-gh.github.io/ML-notes/ML-notes-html/13_Tips-for-Deep-Learning.html'><span>13_Tips for Deep Learning</span></a></p><p><a href='https://sakura-gh.github.io/ML-notes/ML-notes-html/14_Why-Deep.html'><span>14_Why Deep</span></a></p><p><a href='https://sakura-gh.github.io/ML-notes/ML-notes-html/15_Semi-supervised-Learning.html'><span>15_Semi-supervised Learning</span></a></p><p><a href='https://sakura-gh.github.io/ML-notes/ML-notes-html/16_Unsupervised-Learning-Introduction.html'><span>16_Unsupervised Learning Introduction</span></a></p><p><a href='https://sakura-gh.github.io/ML-notes/ML-notes-html/17_Unsupervised-Learning-PCA-part1.html'><span>17_Unsupervised Learning PCA part1</span></a></p><p><a href='https://sakura-gh.github.io/ML-notes/ML-notes-html/18_Unsupervised-Learning-PCA-part2.html'><span>18_Unsupervised Learning PCA part2</span></a></p><p><a href='https://sakura-gh.github.io/ML-notes/ML-notes-html/19_Matrix-Factorization.html'><span>19_Matrix Factorization</span></a></p><p><a href='https://sakura-gh.github.io/ML-notes/ML-notes-html/20_Unsupervised-Learning-Word-Embedding.html'><span>20_Unsupervised Learning Word Embedding</span></a></p><p><a href='https://sakura-gh.github.io/ML-notes/ML-notes-html/21_Unsupervised-Learning-Neighbor-Embedding.html'><span>21_Unsupervised Learning Neighbor Embedding</span></a></p><p><a href='https://sakura-gh.github.io/ML-notes/ML-notes-html/22_Unsupervised-Learning-Deep-Auto-encoder.html'><span>22_Unsupervised Learning Deep Auto-encoder</span></a></p><p><a href='https://sakura-gh.github.io/ML-notes/ML-notes-html/23_Unsupervised-Generation.html'><span>23_Unsupervised Learning Generation</span></a></p><p><a href='https://sakura-gh.github.io/ML-notes/ML-notes-html/24_Transfer-Learning.html'><span>24_Transfer Learning</span></a></p><p><a href='https://sakura-gh.github.io/ML-notes/ML-notes-html/25_Support-Vector-Machine.html'><span>25_Support Vector Machine</span></a></p><p><a href='https://sakura-gh.github.io/ML-notes/ML-notes-html/26_Recurrent-Neural-Network-part1.html'><span>26_Recurrent Neural Network part1</span></a></p><p><a href='https://sakura-gh.github.io/ML-notes/ML-notes-html/27_Recurrent-Neural-Network-part2.html'><span>27_Recurrent Neural Network part2</span></a></p><h5><a name="csdn博客链接" class="md-header-anchor"></a><span>csdn博客链接：</span></h5><p><a href='https://blog.csdn.net/weixin_44406200/article/details/104060561'><span>机器学习系列1-机器学习概念及介绍</span></a></p><p><a href='https://blog.csdn.net/weixin_44406200/article/details/104071036'><span>机器学习系列2-回归案例研究</span></a></p><p><a href='https://blog.csdn.net/weixin_44406200/article/details/104075986'><span>梯度下降代码举例：Gradient Descent Demo(Adagrad)</span></a></p><p><a href='https://blog.csdn.net/weixin_44406200/article/details/104088554'><span>机器学习系列4-模型的误差来源及减少误差的方法</span></a></p><p><a href='https://blog.csdn.net/weixin_44406200/article/details/104256006'><span>机器学习系列5-梯度下降法</span></a></p><p><a href='https://blog.csdn.net/weixin_44406200/article/details/104272160'><span>机器学习系列6-分类问题(概率生成模型)</span></a></p><p><a href='https://blog.csdn.net/weixin_44406200/article/details/104288916'><span>机器学习系列7-逻辑回归</span></a></p><p><a href='https://blog.csdn.net/weixin_44406200/article/details/104299958'><span>机器学习系列8-深度学习简介</span></a></p><p><a href='https://blog.csdn.net/weixin_44406200/article/details/104310991'><span>机器学习系列9-反向传播</span></a></p><p><a href='https://blog.csdn.net/weixin_44406200/article/details/104328947'><span>机器学习系列10-手写数字识别(Keras2.0)</span></a></p><p><a href='https://blog.csdn.net/weixin_44406200/article/details/104370738'><span>机器学习系列11-卷积神经网络CNN part1</span></a></p><p><a href='https://blog.csdn.net/weixin_44406200/article/details/104392592'><span>机器学习系列12-卷积神经网络CNN part2</span></a></p><p><a href='https://blog.csdn.net/weixin_44406200/article/details/104430737'><span>机器学习系列13-深度学习的技巧和优化方法</span></a></p><p><a href='https://blog.csdn.net/weixin_44406200/article/details/104452873'><span>机器学习系列14-为什么要做“深度”学习</span></a></p><p><a href='https://blog.csdn.net/weixin_44406200/article/details/106991717'><span>机器学习系列15-半监督学习</span></a></p><p><a href='https://blog.csdn.net/weixin_44406200/article/details/107029531'><span>机器学习系列16-无监督学习引言</span></a></p><p><a href='https://blog.csdn.net/weixin_44406200/article/details/107082637'><span>机器学习系列17-无监督学习之PCA推导(Ⅰ)</span></a></p><p><a href='https://blog.csdn.net/weixin_44406200/article/details/107082680'><span>机器学习系列18-无监督学习之PCA深入探讨(Ⅱ)</span></a></p><p><a href='https://blog.csdn.net/weixin_44406200/article/details/107099894'><span>机器学习系列19-矩阵分解&推荐系统初步</span></a></p><p><a href='https://blog.csdn.net/weixin_44406200/article/details/107168089'><span>机器学习系列20-无监督学习之词嵌入</span></a></p><p><a href='https://blog.csdn.net/weixin_44406200/article/details/107305230'><span>机器学习系列21-无监督学习之近邻嵌入</span></a></p><p><a href='https://blog.csdn.net/weixin_44406200/article/details/107305267'><span>机器学习系列22-无监督学习之自编码器</span></a></p><p><a href='https://blog.csdn.net/weixin_44406200/article/details/107305305'><span>机器学习系列23-无监督学习之生成模型</span></a></p><p><a href='https://blog.csdn.net/weixin_44406200/article/details/107305326'><span>机器学习系列24-迁移学习</span></a></p><p><a href='https://blog.csdn.net/weixin_44406200/article/details/107693177'><span>机器学习系列25-支持向量机</span></a></p><p><a href='https://blog.csdn.net/weixin_44406200/article/details/107693331'><span>机器学习系列26-循环神经网络RNN(Ⅰ)</span></a></p><p><a href='https://blog.csdn.net/weixin_44406200/article/details/107812374'><span>机器学习系列27-循环神经网络RNN(Ⅱ)</span></a></p><h5><a name="代码链接" class="md-header-anchor"></a><span>代码链接：</span></h5><p><a href=' https://sakura-gh.github.io/ML-notes/code/Gradient-Descent-Demo/Gradient-Descent-Demo.html'><span>Gradient Descent Demo(Adagrad)</span></a></p><p><a href='https://github.com/Sakura-gh/ML-notes/blob/master/code/Digits-Detection/digits-detection.py'><span>手写数字识别(Keras2.0)</span></a></p><p><a href='https://github.com/Sakura-gh/ML-notes/blob/master/code/Digits-Detection/digits-detection-cnn.py'><span>手写数字识别CNN实现(Keras2.0)</span></a></p><p><a href='https://github.com/Sakura-gh/ML-notes/blob/master/keras-tips.md'><span>Keras实战小经验</span></a></p><p><a href='https://github.com/Sakura-gh/ML-notes/blob/master/code/pytorch'><span>PyTorch简易入门</span></a></p><h5><a name="license" class="md-header-anchor"></a><span>LICENSE：</span></h5><p><span>GPL-2.0</span></p><h5><a name="温馨提示" class="md-header-anchor"></a><span>温馨提示：</span></h5><p><span>图片加载可能会有些许缓慢，请耐心等待</span><span>\</span><span>(</span><span>^</span><span>o</span><span>^</span><span>)/</span></p><h5><a name="赞赏作者" class="md-header-anchor"></a><span>赞赏作者：</span></h5><p><span>如果读后有收获，请作者喝杯咖啡吧，您的支持就是我最大的更新动力~ </span></p><center><img src="https://cdn.jsdelivr.net/gh/Sakura-gh/ML-notes/img/zs.png" width="60%"></center><h5><a name="pdf订阅版" class="md-header-anchor"></a><span>PDF订阅版：</span></h5><p><span>关注公众号“Sakura的知识库”可订阅：</span></p><center><img src="https://cdn.jsdelivr.net/gh/Sakura-gh/ML-notes/img/ml-book.png" width="80%"></center><h5><a name="ml-gpu" class="md-header-anchor"></a><span>ML GPU：</span></h5><center><a href="https://tracking.gitads.io/?repo=ML-notes"><img src="https://images.gitads.io/ML-notes" width="80%" alt="GitAds"></a></center></div>
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