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All Projects → yunshuipiao → SWBlog

yunshuipiao / SWBlog

Licence: MIT License
machine learning practitioner， android and python

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tensorflowmachine-learning-algorithmsskleanai-first

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SWBOLG

GitHub forks GitHub forks GitHub forks

有所思有所悟，个人的技术博客

关于用Issue写博客的说明

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。

对于我来说，用Issue来写博客有以下优点：

  1. label 的支持：看到相关 label 就知道该文章属于哪一类
  2. markdown 语法的支持
  3. 图片的插入
  4. 自带的评论系统，方便交流
  5. 可以配合 Project 来管理博客

除一般的 label 外，还会有以下 label 进行扩展：

  1. AMA： ask me anything， 提出问题
  2. TODO， DOING： 表示一个问题，一篇文章，一个特性的进度

文章目录

machine learning 资料推荐

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。
(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）

  1. Hands-On Machine Learning with Scikit-Learn and TensorFlow
    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。
    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。  
    官方代码实现：python2
    我的实现：python3, 部分中文注释，更适合, 提供pdf下载，仅供学习

  2. 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。

  3. A Neural Network in 11 lines of Python (Part 1)
    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。   其后续文章介绍梯度下降，dropout防止过拟合，非常值得阅读。

  4. Neural Networks and Deep Learning   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。
    反向传播可视化和链式求导     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。    

  5. kaggle项目实战
    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。
    我完成了三个入门题目，分别是titanic, house price, digit recognizer。
    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。
    我的实现：https://github.com/yunshuipiao/sw-kaggle

上述过程也是我自己学习的过程，其中吴恩达:机器学习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。

其他资料博客：

  1. reddit:learnmachinelearning
    reddit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。

  2. cs229:machine learning   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。

  3. 最后我的github：https://github.com/yunshuipiao
    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)

也欢迎各位同学推荐自己阅读完的资料。

Android

函数式编程(FP)

algorithm

python + scrapy

docker

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