{"id":13814880,"url":"https://github.com/yunshuipiao/SWBlog","last_synced_at":"2025-05-15T06:33:18.415Z","repository":{"id":138247302,"uuid":"66343440","full_name":"yunshuipiao/SWBlog","owner":"yunshuipiao","description":"machine learning practitioner， android and python","archived":false,"fork":false,"pushed_at":"2021-11-05T03:22:39.000Z","size":584,"stargazers_count":36,"open_issues_count":20,"forks_count":8,"subscribers_count":2,"default_branch":"master","last_synced_at":"2024-08-04T04:06:32.437Z","etag":null,"topics":["ai-first","machine-learning-algorithms","python","sklean","tensorflow"],"latest_commit_sha":null,"homepage":"","language":null,"has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/yunshuipiao.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null}},"created_at":"2016-08-23T07:20:12.000Z","updated_at":"2024-06-12T04:31:55.000Z","dependencies_parsed_at":"2024-01-15T13:34:50.955Z","dependency_job_id":null,"html_url":"https://github.com/yunshuipiao/SWBlog","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/yunshuipiao%2FSWBlog","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/yunshuipiao%2FSWBlog/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/yunshuipiao%2FSWBlog/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/yunshuipiao%2FSWBlog/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/yunshuipiao","download_url":"https://codeload.github.com/yunshuipiao/SWBlog/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":225335166,"owners_count":17458223,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["ai-first","machine-learning-algorithms","python","sklean","tensorflow"],"created_at":"2024-08-04T04:02:41.460Z","updated_at":"2024-11-19T10:30:36.123Z","avatar_url":"https://github.com/yunshuipiao.png","language":null,"funding_links":[],"categories":["Others"],"sub_categories":[],"readme":"\n\n\n\n # SWBOLG\n\n\n\n[![GitHub forks](https://img.shields.io/github/forks/yunshuipiao/SWBlog.svg)](https://github.com/yunshuipiao/SWBlog)\n[![GitHub forks](https://img.shields.io/github/stars/yunshuipiao/SWBlog.svg)](https://github.com/yunshuipiao/SWBlog)\n[![GitHub forks](https://img.shields.io/github/license/yunshuipiao/SWBlog.svg)](https://github.com/yunshuipiao/SWBlog)\n\n\n有所思有所悟，个人的技术博客\n\n## 关于用Issue写博客的说明\n\n经过研究，打算将所有博客移动到相关仓库的Issue， 包括新增博客；不仅如此，以后所有仓库都会按照该方法作为参考来记录。\n\n对于我来说，用Issue来写博客有以下优点：\n\n1. label 的支持：看到相关 label 就知道该文章属于哪一类\n2. markdown 语法的支持\n3. 图片的插入\n4. 自带的评论系统，方便交流\n5. 可以配合 Project 来管理博客\n\n\n\n除一般的 label 外，还会有以下 label 进行扩展：\n\n1.  **AMA**： ask me anything， 提出问题\n2. **TODO， DOING**： 表示一个问题，一篇文章，一个特性的进度\n\n \n\n\n## 文章目录\n\n\n### machine learning 资料推荐\n目前学习相关知识所看过的书籍，资料，视频，下面我认为相对较好的机器学习入门路径，仅供参考。  \n(说明：推荐资料全英文，也强烈建议看英文材料。好处在于 看的慢，思考的多，留下的多）  \n1. [Hands-On Machine Learning with Scikit-Learn and TensorFlow](http://shop.oreilly.com/product/0636920052289.do)    \n大多数人以机器学习实战，西瓜书和统计学习方法作为入门材料，前一本书重工程，后两本重理论及推导，可以作为进阶书。而 Hands-On Machine Learning with Scikit-Learn and TensorFlow 的优势在于书籍比较新，且有github配套的源代码实现，书中涉及传统的机器学习方法及其sklearn实现。  \n学习建议：先看一遍书，接着对照着github代码实现一遍，最后再看一遍书。    \n[官方代码实现：python2](https://github.com/ageron/handson-ml)  \n[我的实现：python3, 部分中文注释，更适合, 提供pdf下载，仅供学习](https://github.com/yunshuipiao/hands-on-ml-with-sklearn-tf-python3)\n\n2. 在阅读上述书籍的过程中，对某个传统机器学习算法感兴趣，可以参考上面的机器学习博客自己实现。  \n\n3. [A Neural Network in 11 lines of Python (Part 1)](http://iamtrask.github.io/2015/07/12/basic-python-network/)   \n上述书籍最后大半内容是关于神经网络的了解和使用，tf实现。而这篇博客对我了解反向传播算法，实现最简单的神经网络有很大的帮助。  \n其后续文章介绍梯度下降，dropout防止过拟合，非常值得阅读。  \n\n4. [Neural Networks and Deep Learning](http://neuralnetworksanddeeplearning.com/index.html)    \n后续可以加深对神经网络的了解和深入。这本书的第三章使用74行代码，不借助任何深度学习框架实现神经网络识别手写数字，有了第3步的基础，相信理解不难。    \n[反向传播可视化和链式求导](https://space.bilibili.com/88461692/#/channel/detail?cid=26587)      \n该连接下有几个深度学习介绍的视频，在阅读书籍的过程中可以随时观看视频，对理解反向传播及链式求导有可视化的讲解。    \n\n5. kaggle项目实战   \n此时可以将学到的知识用于解决实际问题， kaggle是最合适的地方。   \n我完成了三个入门题目，分别是[titanic](https://www.kaggle.com/c/titanic), [house price](https://www.kaggle.com/c/house-prices-advanced-regression-techniques), [digit recognizer](https://www.kaggle.com/c/digit-recognizer)。   \n官方有合适的入门解决方案，可以参考官方或者加入自己的解决方法，完成自己的结果提交，查看排名。    \n[我的实现：https://github.com/yunshuipiao/sw-kaggle](https://github.com/yunshuipiao/sw-kaggle)\n\n上述过程也是我自己学习的过程，其中[吴恩达:机器学习](https://www.bilibili.com/video/av9912938)的视频也可利用空闲时间观看，上述资料我看过大部分，因此认为比较适合作为入门资料推荐。\n\n其他资料博客：\n1. [reddit:learnmachinelearning](https://www.reddit.com/r/learnmachinelearning/)  \nreddit机器学习板块，有很多初学者面临的问题及其解答，还有高质量的文章推荐。  \n2. [cs229:machine learning](http://cs229.stanford.edu/syllabus.html)    \n上述吴恩达机器学习课程的讲义， 相比视频，对原理和推导更加深入。  \n\n3. 最后我的github：https://github.com/yunshuipiao  \n此部分内容会保持更新，阅读晚比较好的资料也会更新补充(强调：阅读完)\n\n也欢迎各位同学推荐自己阅读完的资料。\n\n### Android  \n* [Android之自定义AlertDialog完成Rxjava2倒计时](https://github.com/yunshuipiao/SWBlog/issues/11)\n* [关于android 悬浮窗和自启动的设置, 以及获取系统的信息](https://github.com/yunshuipiao/SWBlog/issues/18)\n* [关于android architecture Component的入门资料(二)](https://github.com/yunshuipiao/SWBlog/issues/17)\n* [关于android architecture Component的最简单实践](https://github.com/yunshuipiao/SWBlog/issues/16)\n* [Android之监听来电，权限管理， 多语言方案，双卡拨号](https://github.com/yunshuipiao/SWBlog/issues/12)\n* [Rxjava2的listener和响应式解惑](https://github.com/yunshuipiao/SWBlog/issues/14)\n* [从谷歌官方例子看constraintlayout](https://github.com/yunshuipiao/SWBlog/issues/15)\n\n\n### 函数式编程(FP)  \n* [Android之纯函数-高阶函数简单介绍](https://github.com/yunshuipiao/SWBlog/issues/20)\n* [Android之Kotlin高阶函数-柯里化-偏函数](https://github.com/yunshuipiao/SWBlog/issues/21)\n\n### algorithm\n* [最长递增子序列--动态规划和LCS(最长公共子序列)](https://github.com/yunshuipiao/SWBlog/issues/8)\n* [无序数组中的第k大元素-快速排序和堆排序](https://github.com/yunshuipiao/SWBlog/issues/7)\n* [二叉树的遍历之多种后序遍历](https://github.com/yunshuipiao/SWBlog/issues/6)\n\n### python + scrapy\n\n* [快上车，scrapy爬虫飙车找福利(一)](https://github.com/yunshuipiao/SWBlog/issues/23)\n* [快上车，scrapy爬虫飙车找福利(二)](https://github.com/yunshuipiao/SWBlog/issues/24)\n* [快上车，scrapy爬虫飙车找福利(三)]( https://github.com/yunshuipiao/SWBlog/issues/25)\n* [在服务器的docker中运行scrapy](https://github.com/yunshuipiao/SWBlog/issues/22)\n\n### docker\n* [在服务器的docker中运行scrapy](https://github.com/yunshuipiao/SWBlog/issues/19)\n\n\n\n\n\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fyunshuipiao%2FSWBlog","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fyunshuipiao%2FSWBlog","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fyunshuipiao%2FSWBlog/lists"}