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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; }
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.md-math-block:not(:empty)::after { display: none; }
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.md-toc-h6 .md-toc-inner { margin-left: 10em; }
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.md-toc-h4 .md-toc-inner { margin-left: 5em; }
.md-toc-h5 .md-toc-inner { margin-left: 6.5em; }
.md-toc-h6 .md-toc-inner { margin-left: 8em; }
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kbd { margin: 0px 0.1em; padding: 0.1em 0.6em; font-size: 0.8em; color: rgb(36, 39, 41); background: rgb(255, 255, 255); border: 1px solid rgb(173, 179, 185); border-radius: 3px; box-shadow: rgba(12, 13, 14, 0.2) 0px 1px 0px, rgb(255, 255, 255) 0px 0px 0px 2px inset; white-space: nowrap; vertical-align: middle; }
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.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; }
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[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);
html {
font-size: 16px;
}
body {
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color: rgb(51, 51, 51);
line-height: 1.6;
}
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margin: 0 auto;
padding: 30px;
padding-bottom: 100px;
}
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#write > ol:first-child{
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}
a {
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h4,
h5,
h6 {
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margin-bottom: 1rem;
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cursor: text;
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font-size: 0.9em;
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code {
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margin-top: 15px;
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table,
pre {
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margin-top: 0 !important;
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padding: 2px 0px 0px 4px;
font-size: 0.9em;
color: inherit;
}
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color: #a7a7a7;
opacity: 1;
}
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/** focus mode */
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border-left-color: rgba(85, 85, 85, 0.12);
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<div id='write' class = 'is-node'><h1><a name="ml-notes" class="md-header-anchor"></a><span>ML-notes</span></h1><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！</span></p><blockquote><p><span>做了一段时间的笔记，发现真正去做project的时候，自己还是很生疏的，machine learning理论学习得再详尽，最终也还是要落于实践才行，这段时间我将陆续将自己所做的几个Assignment上传至github上，尽量注释详细，并使用多种方法进行对比验证</span></p></blockquote><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="keras实践经验" class="md-header-anchor"></a><span>keras实践经验：</span></h5><p><a href='https://github.com/Sakura-gh/ML-notes/blob/master/keras-tips.md'><span>keras-tips</span></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><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><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><h5><a name="assignments链接" class="md-header-anchor"></a><span>Assignments链接：</span></h5><ul><li><span>PM2.5预测：</span><a href='https://github.com/Sakura-gh/ML-assignments/tree/master/Assignment/Assignment1'><span>Regression</span></a></li><li><span>年收入预测：</span><a href='https://github.com/Sakura-gh/ML-assignments/tree/master/Assignment/Assignment2'><span>Classification</span></a></li></ul><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></div>
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