React PDF表示例 经过测试： Firefox 86.0（Ubuntu）和Chrome版本89.0.4389.82（64位）（Ubuntu） 让我知道你们是否喜欢 :victory_hand: 为什么这个仓库甚至存在？ 我见过无处不在的人问你该如何在react-pdf中建立表格。 可悲的事实是，图书馆中仍然没有为您提供帮助的官方组件。 但是，这并不意味着这是不可能的，实际上，这甚至还不难，您只需要在一小部分CSS上变得肮脏即可。 在此存储库中，我构建了一个简单的示例，该示例说明如何使用，和一些忍者CSS轻松模拟表的外观。 怎么跑 克隆存储库 在终端中，转到存储库文件夹： cd react-pdf-table-example 使用yarn或npm安装依赖项，无论哪种方式： yarn install或npm install 使用以下命令启动项目： yarn start或npm

keithley2002 labview example,非常实用.

CityEngine-Philadelphia_example案例操作详解

FullCalendar示例项目 一组简单的示例项目，展示了如何在各种构建设置中使用FullCalendar。 请阅读每个项目子目录中的自述文件。

从swfupload官网下载 MIT协议 支持 flashplayer8 到 10

Python Machine Learning By Example by Yuxi (Hayden) Liu English | 31 May 2017 | ASIN: B01MT7ATL5 | 254 Pages | AZW3 | 3.86 MB Key Features Learn the fundamentals of machine learning and build your own intelligent applications Master the art of building your own machine learning systems with this example-based practical guide Work with important classification and regression algorithms and other machine learning techniques Book Description Data science and machine learning are some of the top buzzwords in the technical world today. A resurging interest in machine learning is due to the same factors that have made data mining and Bayesian analysis more popular than ever. This book is your entry point to machine learning. This book starts with an introduction to machine learning and the Python language and shows you how to complete the setup. Moving ahead, you will learn all the important concepts such as, exploratory data analysis, data preprocessing, feature extraction, data visualization and clustering, classification, regression and model performance evaluation. With the help of various projects included, you will find it intriguing to acquire the mechanics of several important machine learning algorithms – they are no more obscure as they thought. Also, you will be guided step by step to build your own models from scratch. Toward the end, you will gather a broad picture of the machine learning ecosystem and best practices of applying machine learning techniques. Through this book, you will learn to tackle data-driven problems and implement your solutions with the powerful yet simple language, Python. Interesting and easy-to-follow examples, to name some, news topic classification, spam email detection, online ad click-through prediction, stock prices forecast, will keep you glued till you reach your goal. What you will learn Exploit the power of Python to handle data extraction, manipulation, and exploration techniques Use Python to visualize data spread across multiple dimensions and extract useful features Dive deep into the world of analytics to predict situations correctly Implement machine learning classification and regression algorithms from scratch in Python Be amazed to see the algorithms in action Evaluate the performance of a machine learning model and optimize it Solve interesting real-world problems using machine learning and Python as the journey unfolds About the Author Yuxi (Hayden) Liu is currently a data scientist working on messaging app optimization at a multinational online media corporation in Toronto, Canada. He is focusing on social graph mining, social personalization, user demographics and interests prediction, spam detection, and recommendation systems. He has worked for a few years as a data scientist at several programmatic advertising companies, where he applied his machine learning expertise in ad optimization, click-through rate and conversion rate prediction, and click fraud detection. Yuxi earned his degree from the University of Toronto, and published five IEEE transactions and conference papers during his master's research. He finds it enjoyable to crawl data from websites and derive valuable insights. He is also an investment enthusiast. Table of Contents Getting Started with Python and Machine Learning Exploring the 20 newsgroups data set Spam email detection with Naive Bayes News topic classification with Support Vector Machine Click-through prediction with tree-based algorithms Click-through rate prediction with logistic regression Stock prices prediction with regression algorithms Best practices

感觉比rust编程指南之类循规蹈矩的书籍更容易上手。 毕竟呆头呆脑地学几天语法的成本太高了。 建议直接读所需要的项目的代码，不懂的语法直接查这本书。

几个用java写的小程序，实现了bio和nio

The Python Standard Library by Example --Doug Hellmann 那本的源代码哟，欢迎下载！

Matlab读取BMP文件代码示例CUDA物理项目141/241 大卫·摩尔（David Moore）更新于2019年2月4日。 此代码按原样对10,000个粒子运行O（n ^ 2）CUDA模拟。 Galaxy1.txt是一个包含100,000个粒子的星系，并且在kernel.cu中将“ nSkip”设置为10，这意味着每获取十分之一粒子，其质量便乘以10。 编译说明 要运行，请将所有文件解压缩到目录中。 打开终端并cd进入目录。 然后输入“ make run”。 这会： 创建“ out”目录。 将kernel.cu编译为nbody可执行文件 运行./nbody，它输出30个.bmp文件 使用convert将位图放入.gif中 源代码如何工作： 大约50行kernel.cu是CUDA代码，其余是C ++。 ImageUtil.cpp和ImageUtil.h是我为输出图像编写的一些低质量实用程序。 我不建议您在项目中使用它们，但是它们对于调试很有用。 最好像往常一样在matlab或python中进行绘图。 在尝试简化三个单独的资源时编写了源代码文件kernel.cu . GPU Gems