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--- | ||
permalink: /404.html | ||
--- | ||
<script>window.location.href = '/';</script> |
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FROM httpd:2.4 | ||
COPY ./ /usr/local/apache2/htdocs/ |
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<p align="center"> | ||
<a href="https://www.apachecn.org"> | ||
<img width="200" src="http://data.apachecn.org/img/logo.jpg"> | ||
<img width="200" src="docs/img/logo.jpg"> | ||
</a> | ||
<br > | ||
<a href="https://www.apachecn.org/"><img src="https://img.shields.io/badge/%3E-HOME-green.svg"></a> | ||
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@@ -21,13 +21,39 @@ | |
地址A: xxx (欢迎留言,我们完善补充) | ||
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## 下载 | ||
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### Docker | ||
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``` | ||
docker pull apachecn0/ailearning | ||
docker run -tid -p <port>:80 apachecn0/ailearning | ||
# 访问 http://localhost:{port} 查看文档 | ||
``` | ||
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### PYPI | ||
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``` | ||
pip install apachecn-ailearning | ||
apachecn-ailearning <port> | ||
# 访问 http://localhost:{port} 查看文档 | ||
``` | ||
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### NPM | ||
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``` | ||
npm install -g ailearning | ||
ailearning <port> | ||
# 访问 http://localhost:{port} 查看文档 | ||
``` | ||
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## 组织介绍 | ||
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* 合作or侵权,请联系: `[email protected]` | ||
* **我们不是 Apache 的官方组织/机构/团体,只是 Apache 技术栈(以及 AI)的爱好者!** | ||
* **ApacheCN - 学习群【724187166】<a target="_blank" href="//shang.qq.com/wpa/qunwpa?idkey=51040bbd0bf7d0efbfa7256a0331d912a2055a906c324d52b02371d06f3c9878"><img border="0" src="http://data.apachecn.org/img/logo/ApacheCN-group.png" alt="ApacheCN - 学习机器学习群[724187166]" title="ApacheCN - 学习机器学习群[724187166]"></a>** | ||
* **ApacheCN - 学习群【724187166】<a target="_blank" href="//shang.qq.com/wpa/qunwpa?idkey=51040bbd0bf7d0efbfa7256a0331d912a2055a906c324d52b02371d06f3c9878"><img border="0" src="docs/img/ApacheCN-group.png" alt="ApacheCN - 学习机器学习群[724187166]" title="ApacheCN - 学习机器学习群[724187166]"></a>** | ||
|
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> **欢迎任何人参与和完善: 一个人可以走的很快,但是一群人却可以走的更远** | ||
> 一种新技术一旦开始流行,你要么坐上压路机,要么成为铺路石。——Stewart Brand | ||
# 路线图 | ||
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||
|
@@ -45,6 +71,17 @@ | |
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## 1.机器学习 - 基础 | ||
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> 支持版本 | ||
| Version | Supported | | ||
| ------- | ------------------ | | ||
| 3.6.x | :x: | | ||
| 2.7.x | :white_check_mark: | | ||
|
||
注意事项: | ||
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- 机器学习实战: 仅仅只是学习,请使用 python 2.7.x 版本 (3.6.x 只是修改了部分) | ||
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### 基本介绍 | ||
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* 资料来源: Machine Learning in Action(机器学习实战-个人笔记) | ||
|
@@ -60,135 +97,25 @@ | |
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### 学习文档 | ||
|
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<table> | ||
<tr> | ||
<th>模块</th> | ||
<th>章节</th> | ||
<th>类型</th> | ||
<th>负责人(GitHub)</th> | ||
<th>QQ</th> | ||
</tr> | ||
<tr> | ||
<td>机器学习实战</td> | ||
<td><a href="docs/ml/1.机器学习基础.md"> 第 1 章: 机器学习基础</a></td> | ||
<td>介绍</td> | ||
<td><a href="https://github.com/ElmaDavies">@毛红动</a></td> | ||
<td>1306014226</td> | ||
</tr> | ||
<tr> | ||
<td>机器学习实战</td> | ||
<td><a href="docs/ml/2.k-近邻算法.md">第 2 章: KNN 近邻算法</a></td> | ||
<td>分类</td> | ||
<td><a href="https://github.com/youyj521">@尤永江</a></td> | ||
<td>279393323</td> | ||
</tr> | ||
<tr> | ||
<td>机器学习实战</td> | ||
<td><a href="docs/ml/3.决策树.md">第 3 章: 决策树</a></td> | ||
<td>分类</td> | ||
<td><a href="https://github.com/jingwangfei">@景涛</a></td> | ||
<td>844300439</td> | ||
</tr> | ||
<tr> | ||
<td>机器学习实战</td> | ||
<td><a href="docs/ml/4.朴素贝叶斯.md">第 4 章: 朴素贝叶斯</a></td> | ||
<td>分类</td> | ||
<td><a href="https://github.com/wnma3mz">@wnma3mz</a><br/><a href="https://github.com/kailian">@分析</a></td> | ||
<td>1003324213<br/>244970749</td> | ||
</tr> | ||
<tr> | ||
<td>机器学习实战</td> | ||
<td><a href="docs/ml/5.Logistic回归.md">第 5 章: Logistic回归</a></td> | ||
<td>分类</td> | ||
<td><a href="https://github.com/DataMonk2017">@微光同尘</a></td> | ||
<td>529925688</td> | ||
</tr> | ||
<tr> | ||
<td>机器学习实战</td> | ||
<td><a href="docs/ml/6.支持向量机.md">第 6 章: SVM 支持向量机</a></td> | ||
<td>分类</td> | ||
<td><a href="https://github.com/VPrincekin">@王德红</a></td> | ||
<td>934969547</td> | ||
</tr> | ||
<tr> | ||
<td>网上组合内容</td> | ||
<td><a href="docs/ml/7.集成方法-随机森林和AdaBoost.md">第 7 章: 集成方法(随机森林和 AdaBoost)</a></td> | ||
<td>分类</td> | ||
<td><a href="https://github.com/jiangzhonglian">@片刻</a></td> | ||
<td>529815144</td> | ||
</tr> | ||
<tr> | ||
<td>机器学习实战</td> | ||
<td><a href="docs/ml/8.回归.md">第 8 章: 回归</a></td> | ||
<td>回归</td> | ||
<td><a href="https://github.com/DataMonk2017">@微光同尘</a></td> | ||
<td>529925688</td> | ||
</tr> | ||
<tr> | ||
<td>机器学习实战</td> | ||
<td><a href="docs/ml/9.树回归.md">第 9 章: 树回归</a></td> | ||
<td>回归</td> | ||
<td><a href="https://github.com/DataMonk2017">@微光同尘</a></td> | ||
<td>529925688</td> | ||
</tr> | ||
<tr> | ||
<td>机器学习实战</td> | ||
<td><a href="docs/ml/10.k-means聚类.md">第 10 章: K-Means 聚类</a></td> | ||
<td>聚类</td> | ||
<td><a href="https://github.com/xuzhaoqing">@徐昭清</a></td> | ||
<td>827106588</td> | ||
</tr> | ||
<tr> | ||
<td>机器学习实战</td> | ||
<td><a href="docs/ml/11.使用Apriori算法进行关联分析.md">第 11 章: 利用 Apriori 算法进行关联分析</a></td> | ||
<td>频繁项集</td> | ||
<td><a href="https://github.com/WindZQ">@刘海飞</a></td> | ||
<td>1049498972</td> | ||
</tr> | ||
<tr> | ||
<td>机器学习实战</td> | ||
<td><a href="docs/ml/12.使用FP-growth算法来高效发现频繁项集.md">第 12 章: FP-growth 高效发现频繁项集</a></td> | ||
<td>频繁项集</td> | ||
<td><a href="https://github.com/mikechengwei">@程威</a></td> | ||
<td>842725815</td> | ||
</tr> | ||
<tr> | ||
<td>机器学习实战</td> | ||
<td><a href="docs/ml/13.利用PCA来简化数据.md">第 13 章: 利用 PCA 来简化数据</a></td> | ||
<td>工具</td> | ||
<td><a href="https://github.com/lljuan330">@廖立娟</a></td> | ||
<td>835670618</td> | ||
</tr> | ||
<tr> | ||
<td>机器学习实战</td> | ||
<td><a href="docs/ml/14.利用SVD简化数据.md">第 14 章: 利用 SVD 来简化数据</a></td> | ||
<td>工具</td> | ||
<td><a href="https://github.com/marsjhao">@张俊皓</a></td> | ||
<td>714974242</td> | ||
</tr> | ||
<tr> | ||
<td>机器学习实战</td> | ||
<td><a href="docs/ml/15.大数据与MapReduce.md">第 15 章: 大数据与 MapReduce</a></td> | ||
<td>工具</td> | ||
<td><a href="https://github.com/wnma3mz">@wnma3mz</a></td> | ||
<td>1003324213</td> | ||
</tr> | ||
<tr> | ||
<td>Ml项目实战</td> | ||
<td><a href="docs/ml/16.推荐系统.md">第 16 章: 推荐系统(已迁移)</a></td> | ||
<td>项目</td> | ||
<td><a href="https://github.com/apachecn/RecommenderSystems">推荐系统(迁移后地址)</a></td> | ||
<td></td> | ||
</tr> | ||
<tr> | ||
<td>第一期的总结</td> | ||
<td><a href="report/2017-04-08_第一期的总结.md">2017-04-08: 第一期的总结</a></td> | ||
<td>总结</td> | ||
<td>总结</td> | ||
<td>529815144</td> | ||
</tr> | ||
</table> | ||
|
||
| 模块 | 章节 | 类型 | 负责人(GitHub) | QQ | | ||
| --- | --- | --- | --- | --- | | ||
| 机器学习实战 | [第 1 章: 机器学习基础](docs/ml/1.md) | 介绍 | [@毛红动](https://github.com/ElmaDavies) | 1306014226 | | ||
| 机器学习实战 | [第 2 章: KNN 近邻算法](docs/ml/2.md) | 分类 | [@尤永江](https://github.com/youyj521) | 279393323 | | ||
| 机器学习实战 | [第 3 章: 决策树](docs/ml/3.md) | 分类 | [@景涛](https://github.com/jingwangfei) | 844300439 | | ||
| 机器学习实战 | [第 4 章: 朴素贝叶斯](docs/ml/4.md) | 分类 | [@wnma3mz](https://github.com/wnma3mz)<br/>[@分析](https://github.com/kailian) | 1003324213<br/>244970749 | | ||
| 机器学习实战 | [第 5 章: Logistic回归](docs/ml/5.md) | 分类 | [@微光同尘](https://github.com/DataMonk2017) | 529925688 | | ||
| 机器学习实战 | [第 6 章: SVM 支持向量机](docs/ml/6.md) | 分类 | [@王德红](https://github.com/VPrincekin) | 934969547 | | ||
| 网上组合内容 | [第 7 章: 集成方法(随机森林和 AdaBoost)](docs/ml/7.md) | 分类 | [@片刻](https://github.com/jiangzhonglian) | 529815144 | | ||
| 机器学习实战 | [第 8 章: 回归](docs/ml/8.md) | 回归 | [@微光同尘](https://github.com/DataMonk2017) | 529925688 | | ||
| 机器学习实战 | [第 9 章: 树回归](docs/ml/9.md) | 回归 | [@微光同尘](https://github.com/DataMonk2017) | 529925688 | | ||
| 机器学习实战 | [第 10 章: K-Means 聚类](docs/ml/10.md) | 聚类 | [@徐昭清](https://github.com/xuzhaoqing) | 827106588 | | ||
| 机器学习实战 | [第 11 章: 利用 Apriori 算法进行关联分析](docs/ml/11.md) | 频繁项集 | [@刘海飞](https://github.com/WindZQ) | 1049498972 | | ||
| 机器学习实战 | [第 12 章: FP-growth 高效发现频繁项集](docs/ml/12.md) | 频繁项集 | [@程威](https://github.com/mikechengwei) | 842725815 | | ||
| 机器学习实战 | [第 13 章: 利用 PCA 来简化数据](docs/ml/13.md) | 工具 | [@廖立娟](https://github.com/lljuan330) | 835670618 | | ||
| 机器学习实战 | [第 14 章: 利用 SVD 来简化数据](docs/ml/14.md) | 工具 | [@张俊皓](https://github.com/marsjhao) | 714974242 | | ||
| 机器学习实战 | [第 15 章: 大数据与 MapReduce](docs/ml/15.md) | 工具 | [@wnma3mz](https://github.com/wnma3mz) | 1003324213 | | ||
| Ml项目实战 | [第 16 章: 推荐系统(已迁移)](docs/ml/16.md) | 项目 | [推荐系统(迁移后地址)](https://github.com/apachecn/RecommenderSystems) | | | ||
| 第一期的总结 | [2017-04-08: 第一期的总结](docs/report/2017-04-08.md) | 总结 | 总结 | 529815144 | | ||
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### 网站视频 | ||
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> 视频怎么看? | ||
![](http://data.apachecn.org/img/AiLearning/MainPage/ApacheCN-ML-bilibili-compare.jpg) | ||
![](docs/img/ApacheCN-ML-bilibili-compare.jpg) | ||
|
||
1. 理论科班出身-建议去学习 Andrew Ng 的视频(Ng 的视频绝对是权威,这个毋庸置疑) | ||
2. 编码能力强 - 建议看我们的[《机器学习实战-教学版》](https://space.bilibili.com/97678687/#!/channel/detail?cid=22486) | ||
|
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||| | ||
| - | - | | ||
| AcFun | B站 | | ||
| <a title="AcFun(机器学习视频)" href="http://www.acfun.cn/u/12540256.aspx#page=1" target="_blank"><img width="290" src="http://data.apachecn.org/img/AiLearning/MainPage/ApacheCN-ML-AcFun.jpg"></a> | <a title="bilibili(机器学习视频)" href="https://space.bilibili.com/97678687/#!/channel/index" target="_blank"><img width="290" src="http://data.apachecn.org/img/AiLearning/MainPage/ApacheCN-ML-bilibili.jpg"></a> | | ||
| <a title="AcFun(机器学习视频)" href="http://www.acfun.cn/u/12540256.aspx#page=1" target="_blank"><img width="290" src="docs/img/ApacheCN-ML-AcFun.jpg"></a> | <a title="bilibili(机器学习视频)" href="https://space.bilibili.com/97678687/#!/channel/index" target="_blank"><img width="290" src="docs/img/ApacheCN-ML-bilibili.jpg"></a> | | ||
| 优酷 | 网易云课堂 | | ||
| <a title="YouKu(机器学习视频)" href="http://i.youku.com/apachecn" target="_blank"><img width="290" src="http://data.apachecn.org/img/AiLearning/MainPage/ApacheCM-ML-youku.jpg"></a> | <a title="WangYiYunKeTang(机器学习视频)" href="http://study.163.com/course/courseMain.htm?courseId=1004582003" target="_blank"><img width="290" src="http://data.apachecn.org/img/AiLearning/MainPage/ApacheCM-ML-WangYiYunKeTang.png"></a> | | ||
| <a title="YouKu(机器学习视频)" href="http://i.youku.com/apachecn" target="_blank"><img width="290" src="docs/img/ApacheCM-ML-youku.jpg"></a> | <a title="WangYiYunKeTang(机器学习视频)" href="http://study.163.com/course/courseMain.htm?courseId=1004582003" target="_blank"><img width="290" src="docs/img/ApacheCM-ML-WangYiYunKeTang.png"></a> | | ||
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> 【免费】机器/深度学习视频 - 吴恩达 | ||
|
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## 2.深度学习 | ||
|
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> 支持版本 | ||
| Version | Supported | | ||
| ------- | ------------------ | | ||
| 3.6.x | :white_check_mark: | | ||
| 2.7.x | :x: | | ||
|
||
### 入门基础 | ||
|
||
1. [反向传递](/docs/dl/反向传递.md): https://www.cnblogs.com/charlotte77/p/5629865.html | ||
|
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> 目录结构: | ||
* [安装指南](docs/TensorFlow2.x/安装指南.md) | ||
* [Kears 快速入门](docs/TensorFlow2.x/Keras快速入门.md) | ||
* [Keras 快速入门](docs/TensorFlow2.x/Keras快速入门.md) | ||
* [实战项目 1 电影情感分类](docs/TensorFlow2.x/实战项目_1_电影情感分类.md) | ||
* [实战项目 2 汽车燃油效率](docs/TensorFlow2.x/实战项目_2_汽车燃油效率.md) | ||
* [实战项目 3 优化 过拟合和欠拟合](docs/TensorFlow2.x/实战项目_3_优化_过拟合和欠拟合.md) | ||
|
@@ -283,6 +217,13 @@ TensorFlow 2.0学习网址 | |
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## 3.自然语言处理 | ||
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> 支持版本 | ||
| Version | Supported | | ||
| ------- | ------------------ | | ||
| 3.6.x | :white_check_mark: | | ||
| 2.7.x | :x: | | ||
|
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学习过程中-内心复杂的变化!!! | ||
|
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```python | ||
|
@@ -302,7 +243,7 @@ TensorFlow 2.0学习网址 | |
当然谢谢国内很多博客大佬,特别是一些入门的Demo和基本概念。【深入的水平有限,没看懂】 | ||
``` | ||
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![](http://data.apachecn.org/img/AiLearning/nlp/F94581F64C21A1094A473397DFA42F9C.jpg) | ||
![](docs/nlp/img/F94581F64C21A1094A473397DFA42F9C.jpg) | ||
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* **【入门须知】必须了解**: <https://github.com/apachecn/AiLearning/tree/master/docs/nlp> | ||
* **【入门教程】强烈推荐: PyTorch 自然语言处理**: <https://github.com/apachecn/NLP-with-PyTorch> | ||
|
@@ -382,7 +323,7 @@ TensorFlow 2.0学习网址 | |
下面是一些很好的初学者语言建模数据集。 | ||
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1. [古腾堡项目](https://www.gutenberg.org/),一系列免费书籍,可以用纯文本检索各种语言。 | ||
2. 还有更多正式的语料库得到了很好的研究; 例如: | ||
2. 还有更多正式的语料库得到了很好的研究; 例如: | ||
[布朗大学现代美国英语标准语料库](https://en.wikipedia.org/wiki/Brown_Corpus)。大量英语单词样本。 | ||
[谷歌10亿字语料库](https://github.com/ciprian-chelba/1-billion-word-language-modeling-benchmark)。 | ||
|
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|
@@ -422,7 +363,7 @@ mage字幕是为给定图像生成文本描述的任务。 | |
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1. [加拿大第36届议会的协调国会议员](https://www.isi.edu/natural-language/download/hansard/)。成对的英语和法语句子。 | ||
2. [欧洲议会诉讼平行语料库1996-2011](http://www.statmt.org/europarl/)。句子对一套欧洲语言。 | ||
有大量标准数据集用于年度机器翻译挑战; 看到: | ||
有大量标准数据集用于年度机器翻译挑战; 看到: | ||
|
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[统计机器翻译](http://www.statmt.org/) | ||
|
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|
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Learn more about bidirectional Unicode characters
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# 安全政策 Security Policy | ||
|
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## 支持版本 Supported Versions | ||
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Use this section to tell people about which versions of your project are | ||
currently being supported with security updates. | ||
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| Version | Supported | | ||
| ------- | ------------------ | | ||
| 3.6.x | :x: | | ||
| 2.7.x | :white_check_mark: | | ||
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注意事项: | ||
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- 机器学习实战: 仅仅只是学习,请使用 python 2.7.x 版本 (3.6.x 只是修改了部分) | ||
- 深度学习/自然语言处理: 请使用 python 3.6.x 版本 | ||
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## 报告漏洞 Reporting a Vulnerability | ||
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Use this section to tell people how to report a vulnerability. | ||
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Tell them where to go, how often they can expect to get an update on a | ||
reported vulnerability, what to expect if the vulnerability is accepted or | ||
declined, etc. |
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