Lee_Amazine

CDMA网维认证模拟试题主设备专业华为设备

CDMA网维认证模拟试题（主设备专业－华为设备）全套题库，包含单选，多选，填空，判断和问答共86页。

DOC

1.25MB

2020-07-17 15:26

Professional F Sharp2.0

F#是由微软发展的为微软.NET语言提供运行环境的程序设计语言。它是基于Ocaml的，而Ocaml是基于ML函数程序设计语言的。这是一个用于显示.NET在不同编程语言间互通的程序设计。F#自2002年开始研发，2005年发布了第一个版本，2007年底正式从研发专案转移至产品部门，并决定将F#置入VisualStudio.NET2010。截止目前（2009年1月6日现在），最新的F#预览版为F#September2008CTP，版本号为1.9.6.2。VisualStudio2010英文版已经于2010年4

RAR

18.14MB

2019-09-23 21:26

Data Science from Scratch First Principles with Python

Data science libraries, frameworks, modules, and toolkits are great for doing data science, but they’re also a good way to dive into the discipline without actually understanding data science. In this book, you’ll learn how many of the most fundamental data science tools and algorithms work by implementing them from scratch. If you have an aptitude for mathematics and some programming skills, author Joel Grus will help you get comfortable with the math and statistics at the core of data science, and with hacking skills you need to get started as a data scientist. Today’s messy glut of data holds answers to questions no one’s even thought to ask. This book provides you with the know-how to dig those answers out. Get a crash course in Python Learn the basics of linear algebra, statistics, and probability—and understand how and when they're used in data science Collect, explore, clean, munge, and manipulate data Dive into the fundamentals of machine learning Implement models such as k-nearest Neighbors, Naive Bayes, linear and logistic regression, decision trees, neural networks, and clustering Explore recommender systems, natural language processing, network analysis, MapReduce, and databases to get started as a data scientist. Today’s messy glut of data holds answers to questions no one’s even thought to ask. This book provides you with the know-how to dig those answers out. Get a crash course in Python Learn the basics of linear algebra, statistics, and probability—and understand how and when they're used in data science Collect, explore, clean, munge, and manipulate data Dive into the fundamentals of machine learning Implement models such as k-nearest Neighbors, Naive Bayes, linear and logistic regression, decision trees, neural networks, and clustering Explore recommender systems, natural language processing, network analysis, MapReduce, and databases

7Z

9.89MB

2019-01-02 06:26