你知道的 redis 原先是开源的，里面的次数限制是可以修改的，如果你懒得去源找，可以用这个，自己去改

sublime text中的乱码现象需要gbk encoding support插件解决。但是自带的插件打开文件总是产生.dump缓存，每次需要打开两次，用此资源替换Sublime Text 2\Pristine Packages下面的原有文件即可解决打开缓存文件问题

Sublime Text3

sol-11_4-text-x86.iso 最新Solaris原版Solaris 11.4系统镜像，需要的可以下载安装

Sublime Text 3编辑器，Mac可用（dmg格式），直接双击即可使用，m1可用，

绿色破解版，本人亲测十分好用，现在放出来供大家使用。

将“MTEXT”类型转换为“TEXT”类型（多行文字转单行文字）的lisp代码

MATLAB Text Analytics Toolbox官方教程，包含实例和参考手册

sublime Text,Sublime_Text3_Stable_Build,有历史版本，此为文档，打开后有长期有效的百度网盘的链接和提取码，欢迎使用。

Derive useful insights from your data using Python. Learn the techniques related to natural language processing and text analytics, and gain the skills to know which technique is best suited to solve a particular problem. Text Analytics with Python teaches you both basic and advanced concepts, including text and language syntax, structure, semantics. You will focus on algorithms and techniques, such as text classification, clustering, topic modeling, and text summarization. A structured and comprehensive approach is followed in this book so that readers with little or no experience do not find themselves overwhelmed. You will start with the basics of natural language and Python and move on to advanced analytical and machine learning concepts. You will look at each technique and algorithm with both a bird's eye view to understand how it can be used as well as with a microscopic view to understand the mathematical concepts and to implement them to solve your own problems. This book Provides complete coverage of the major concepts and techniques of natural language processing (NLP) and text analytics Includes practical real-world examples of techniques for implementation, such as building a text classification system to categorize news articles, analyzing app or game reviews using topic modeling and text summarization, and clustering popular movie synopses and analyzing the sentiment of movie reviews Shows implementations based on Python and several popular open source libraries in NLP and text analytics, such as the natural language toolkit (nltk), gensim, scikit-learn, spaCy and Pattern What you will learn Natural Language concepts Analyzing Text syntax and structure Text Classification Text Clustering and Similarity analysis Text Summarization Semantic and Sentiment analysis Readership The book is for IT professionals, analysts, developers, linguistic experts, data scientists, and anyone with a keen interest in linguistics, analytics, and generating insights from