tiny-os：《操作系统真象还原》一书实现的系统代码

#Programming By Doing ##Learning Java the hard way，或者很可能是唯一的方法，通过做

作者: John Kruschke 出版社: Academic Press 副标题: A Tutorial with R, JAGS, and Stan 出版年: 2014-11-17 页数: 776 定价: USD 89.95 装帧: Hardcover ISBN: 9780124058880

Doing Math with Python shows you how to use Python to delve into high school—level math topics like statistics, geometry, probability, and calculus. You'll start with simple projects, like a factoring program and a quadratic-equation solver, and then create more complex projects once you've gotten the hang of things. Along the way, you'll discover new ways to explore math and gain valuable programming skills that you'll use throughout your study of math and computer science. Learn how to: Describe your data with statistics, and visualize it with line graphs, bar charts, and scatter plots Explore set theory and probability with programs for coin flips, dicing, and other games of chance Solve algebra problems using Python's symbolic math functions Draw geometric shapes and explore fractals like the Barnsley fern, the Sierpinski triangle, and the Mandelbrot set Write programs to find derivatives and integrate functions Creative coding challenges and applied examples help you see how you can put your new math and coding skills into practice. You'll write an inequality solver, plot gravity's effect on how far a bullet will travel, shuffle a deck of cards, estimate the area of a circle by throwing 100,000 "darts" at a board, explore the relationship between the Fibonacci sequence and the golden ratio, and more.Whether you're interested in math but have yet to dip into programming or you're a teacher looking to bring programming into the classroom, you'll find that Python makes programming easy and practical. Let Python handle the grunt work while you focus on the math. Table of Contents Chapter 1: Working with Numbers Chapter 2: Visualizing Data with Graphs Chapter 3: Describing Data with Statistics Chapter 4: Algebra and Symbolic Math with SymPy Chapter 5: Playing with Sets and Probability Chapter 6: Drawing Geometric Shapes and Fractals Chapter 7: Solving Calculus Problems Appendix A: Software Installation Appendix B: Overview of Python Topics

Praat语音学软件，原名Praat: doing phonetics by computer，通常简称Praat，是一款跨平台的多功能语音学专业软件，主要用于对数字化的语音信号进行分析、标注、处理及合成等实验，同时生成各种语图和文字报表。praat资源下载，windows系统32位、64位可用。

Chunkchain是一个免费的开源区块链模拟工具。 它旨在支持任何与区块链或分布式分类账技术相关的高等和高等教育课程。 由于动手学习经验对于深入理解至关重要，因此该工具展示了各种用例中的分布式分类帐技术。 其目标是： 查看一些用例区块链如何工作 轻松简单地交换协议或方法 深刻了解背景情况 看到机会和潜力开始致力于自己的想法 用例：聊天平台 基于分布式P2P网络的聊天平台可以使人们对区块链的不同层有更多的了解。 通过在后台启用与消息的单个1：1聊天，应该可以看到交易速度如何依赖于所选的共识机制以及如何购买消息。 区块链类型：公共区块链 共识机制：工作量证明

学习虚幻引擎游戏开发：创建项目，示例和模板以学习虚幻引擎的游戏开发

There is an explosion of interest in Bayesian statistics, primarily because recently created computational methods have finally made Bayesian analysis tractable and accessible to a wide audience. Doing Bayesian Data Analysis, A Tutorial Introduction with R and BUGS, is for first year graduate students or advanced undergraduates and provides an accessible approach, as all mathematics is explained intuitively and with concrete examples. It assumes only algebra and ‘rusty’ calculus. Unlike other textbooks, this book begins with the basics, including essential concepts of probability and random sampling. The book gradually climbs all the way to advanced hierarchical modeling methods for realistic data. The text provides complete examples with the R programming language and BUGS software (both freeware), and begins with basic programming examples, working up gradually to complete programs for complex analyses and presentation graphics. These templates can be easily adapted for a large variety of students and their own research needs.The textbook bridges the students from their undergraduate training into modern Bayesian methods. Accessible, including the basics of essential concepts of probability and random sampling Examples with R programming language and BUGS software Comprehensive coverage of all scenarios addressed by non-bayesian textbooks- t-tests, analysis of variance (ANOVA) and comparisons in ANOVA, multiple regression, and chi-square (contingency table analysis). Coverage of experiment planning R and BUGS computer programming code on website Exercises have explicit purposes and guidelines for accomplishment 作者从概率统计和编程两方面入手，由浅入深地指导读者如何对实际数据进行贝叶斯分析。全书分成三部分，第一部分为基础篇：关于参数、概率、贝叶斯法则及R软件，第二部分为二元比例推断的基本理论，第三部分为广义线性模型。内容包括贝叶斯统计的基本理论、实验设计的有关知识、以层次模型和MCMC为代表的复杂方法等。同时覆盖所有需要用到非贝叶斯方法的情况，其中包括：t检验，方差分析（ANOVA）和ANOVA中的多重比较法，多元线性回归，Logistic回归，序列回归和卡方（列联表）分析。针对不同的学习目标（如R、BUGS等）列出了相应的重点章节；整理出贝叶斯统计中某些与传统统计学可作类比的内容，方便读者快速学习。本中提出的方法都是可操作的，并且所有涉及数学理论的地方都已经用实际例子非常直观地进行了解释。由于并不对读者的统计或

[奥莱理] Doing Data Science (英文版) [奥莱理] Doing Data Science Straight Talk from the Frontline (E-Book) ☆ 图书概要：☆ Now that people are aware that data can make the difference in an election or a business model, data science as an occupation is gaining ground. But how can you get started working in a wide-ranging, interdisciplinary field that’s so clouded in hype? This insightful book, based on Columbia University’s Introduction to Data Science class, tells you what you need to know. In many of these chapter-long lectures, data scientists from companies such as Google, Microsoft, and eBay share new algorithms, methods, and models by presenting case studies and the code they use. If you’re familiar with linear algebra, probability, and statistics, and have programming experience, this book is an ideal introduction to data science. ☆ 出版信息：☆ [作者信息] Rachel Schutt , Cathy O'Neil [出版机构] 奥莱理 [出版日期] 2013年10月31日 [图书页数] 406页 [图书语言] 英语 [图书格式] PDF 格式