安装Machine Learning环境

本文详细介绍如何升级Python至3.5以上版本,并列出了一系列用于机器学习的Python包安装指令,包括TensorFlow、Keras、Spacy等,以及Spacy模型的下载方法。
  1. 升级python版本到3.5以上。详情参考Linux升级python版本
  1. 步骤1中也包含了安装pip

  2. 第一批ml环境
    pip install tensorflow
    pip install keras
    pip install beautifulsoup4
    pip install lxml
    pip install nltk
    pip install sklearn
    pip install boto3 // 没用到此包,以后有涉及到访问aws资源再装也ok

  3. 第二批ml环境,主要装spacy
    pip install murmurhash
    pip install preshed
    pip install cymem
    pip install cytoolz
    pip install thinc
    pip install ujson
    pip install spacy
    注:cytoolz和thinc在装spacy的时候,有可能因为版本问题,会被spacy卸载重装低版本

  4. 第三批ML环境,主要下载spacy model
    python -m spacy download en_core_web_lg



作者:Jiu_Ming
链接:https://www.jianshu.com/p/efac9a2d0341
來源:简书
简书著作权归作者所有,任何形式的转载都请联系作者获得授权并注明出处。

Preface Machine learning algorithms dominate applied machine learning. Because algorithms are such a big part of machine learning you must spend time to get familiar with them and really understand how they work. I wrote this book to help you start this journey. You can describe machine learning algorithms using statistics, probability and linear algebra. The mathematical descriptions are very precise and often unambiguous. But this is not the only way to describe machine learning algorithms. Writing this book, I set out to describe machine learning algorithms for developers (like myself). As developers, we think in repeatable procedures. The best way to describe a machine learning algorithm for us is: 1. In terms of the representation used by the algorithm (the actual numbers stored in a file). 2. In terms of the abstract repeatable procedures used by the algorithm to learn a model from data and later to make predictions with the model. 3. With clear worked examples showing exactly how real numbers plug into the equations and what numbers to expect as output. This book cuts through the mathematical talk around machine learning algorithms and shows you exactly how they work so that you can implement them yourself in a spreadsheet, in code with your favorite programming language or however you like. Once you possess this intimate knowledge, it will always be with you. You can implement the algorithms again and again. More importantly, you can translate the behavior of an algorithm back to the underlying procedure and really know what is going on and how to get the most from it. This book is your tour of machine learning algorithms and I’m excited and honored to be your tour guide. Let’s dive in.
### 安装Statistics and Machine Learning Toolbox 对于MATLAB中的工具箱安装,通常有两种主要方法来完成这一操作:通过MATLAB自带的应用程序管理器或者命令行界面。 如果遇到服务器实例无法挂载ISO文件的情况,则可以考虑使用`7z`解压软件先处理ISO文件再进行安装工作。具体来说,在Linux环境下可以通过如下指令安装`7z`并解压缩ISO文件: ```bash sudo apt-get install p7zip-full p7zip-rar 7z x R2018b_glnxa64_dvd1.iso ``` 上述命令会将ISO内的内容提取到当前目录下[^3]。 然而,针对Statistics and Machine Learning Toolbox的具体安装过程,推荐按照官方文档指引来进行。一般情况下,启动MATLAB之后进入主页标签页,点击“Add-Ons”,然后选择“Get Add-Ons”。这将会打开MATLAB Add-On Explorer窗口,在这里可以直接搜索所需的Toolbox名称——即“Statistics and Machine Learning Toolbox”。 另外一种方式是在命令窗输入以下代码以启动在线安装向导: ```matlab web('https://www.mathworks.com/matlabcentral/fileexchange/toolboxes') ``` 此链接指向MathWorks官方网站上的所有可用工具包页面,从中找到对应的统计学与机器学习工具集即可按提示下载和激活。 需要注意的是,当尝试在云平台如AWS上运行带有GPU支持的MATLAB版本时,可能会遭遇类似于日志记录中提到的问题:“No activateCommand set.” 这意味着配置文件缺少必要的激活参数设置。解决办法是编辑相应的配置文件加入activateCommand属性,并确保其路径正确无误[^4]。
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