Git Product home page Git Product logo

python-machine-learning-algorithm's Introduction

Python机器学习算法

写书不易,写一本好书更加不容易,本书上市以来收到了很多读书的赞赏。在本书的出版过程中,由于时间,精力等其他一些因素,虽经过多次修改,但仍然存在错误,谢谢各位读者能够不吝指出,下面是本书的勘误:

勘误地址

这里写图片描述

程序代码是《Python机器学习算法》的示例代码,目前该书在各大商城已经可以购买:

本书特色

本书是一本机器学习入门读物,注重理论与实践的结合。全书主要包括6 个部分,每个部分以典型的机器学习算法为例,从算法原理出发,由浅入深,详细分析算法的理论,并配合目前流行的Python 语言,从零开始,实现每一个算法,以加强对机器学习算法理论的理解和增强实际的算法实践能力,最终达到熟练掌握每一个算法的目的。与其他机器学习类书相比,本书同时包含算法理论的介绍和算法的实践,以理论支撑实践,同时,又将复杂,枯燥的理论用简单易懂的形式表达出来,促进对理论的理解。

目录

这里写图片描述

这里写图片描述

这里写图片描述

这里写图片描述

这里写图片描述

这里写图片描述

这里写图片描述

这里写图片描述

这里写图片描述

这里写图片描述

这里写图片描述

同时,我还为本书设置了QQ群:

这里写图片描述

python-machine-learning-algorithm's People

Contributors

zhaozhiyong19890102 avatar

Stargazers

 avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar

Watchers

 avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar

python-machine-learning-algorithm's Issues

第6章BP神经网络w的初始化方式不太理解?

你好!看到您写的关于BP神经网络的代码,针对w的初始化,搜索了下你使用的是Xavier的公式(http://proceedings.mlr.press/v9/glorot10a/glorot10a.pdf),即w在(-sqre(6/(n+n_hidden)), sqre(6/(n+n_hidden))), 这没有疑问,在具体实现中你的代码如下,先取0到1之间均匀分布随机数,后边应该乘以sqrt(6/(n+n_hidden)),但如下代码为什么会多出来一段,并且数字也不太一致,请教下是什么原因?是有别的初始化公式吗?
w0 = w0 * (8.0 * sqrt(6) / sqrt(n + n_hidden)) -
np.mat(np.ones((n, n_hidden))) *
(4.0 * sqrt(6) / sqrt(n + n_hidden))

没有数据

书到手后,发现没有下载数据的接口。有代码没数据。。。尴尬

第十章kmeans的代码

            if subCenter[i, 0] <> minIndex:  # 需要改变
                change = True
                subCenter[i, ] = np.mat([minIndex, minDist])

改成

            if subCenter[i, 0] <> minIndex:  # 需要改变
                change = True
            subCenter[i, ] = np.mat([minIndex, minDist])

上来点赞

逛书店的时候找到了这本书
包含了很多最新的算法
目前市场上最好的教程
特地上来点赞

Recommend Projects

  • React photo React

    A declarative, efficient, and flexible JavaScript library for building user interfaces.

  • Vue.js photo Vue.js

    🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.

  • Typescript photo Typescript

    TypeScript is a superset of JavaScript that compiles to clean JavaScript output.

  • TensorFlow photo TensorFlow

    An Open Source Machine Learning Framework for Everyone

  • Django photo Django

    The Web framework for perfectionists with deadlines.

  • D3 photo D3

    Bring data to life with SVG, Canvas and HTML. 📊📈🎉

Recommend Topics

  • javascript

    JavaScript (JS) is a lightweight interpreted programming language with first-class functions.

  • web

    Some thing interesting about web. New door for the world.

  • server

    A server is a program made to process requests and deliver data to clients.

  • Machine learning

    Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.

  • Game

    Some thing interesting about game, make everyone happy.

Recommend Org

  • Facebook photo Facebook

    We are working to build community through open source technology. NB: members must have two-factor auth.

  • Microsoft photo Microsoft

    Open source projects and samples from Microsoft.

  • Google photo Google

    Google ❤️ Open Source for everyone.

  • D3 photo D3

    Data-Driven Documents codes.