量子计算：一种应用方法 Jack Hidary教科书“量子计算：一种应用方法”的解决方案。 通过提出拉取请求，随时指出解决方案中的任何错误或错误。

Empirical Modeling and Data Analysis for Engineers and Applied Scientists English | 25 July 2016 | ISBN: 3319327674 | 264 Pages This textbook teaches advanced undergraduate and first-year graduate students in Engineering and Applied Sciences to gather and analyze empirical observations (data) in order to aid in making design decisions. While science is about discovery, the primary paradigm of engineering and “applied science” is design. Scientists are in the discovery business and want, in general, to understand the natural world rather than to alter it. In contrast, engineers and applied scientists design products, processes, and solutions to problems. That said, statistics, as a discipline, is mostly oriented toward the discovery paradigm. Young engineers come out of their degree programs having taken courses such as “Statistics for Engineers and Scientists” without any clear idea as to how they can use statistical methods to help them design products or processes. Many seem to think that statistics is only useful for demonstrating that a device or process actually does what it was designed to do. Statistics courses emphasize creating predictive or classification models – predicting nature or classifying individuals, and statistics is often used to prove or disprove phenomena as opposed to aiding in the design of a product or process. In industry however, Chemical Engineers use designed experiments to optimize petroleum extraction; Manufacturing Engineers use experimental data to optimize machine operation; Industrial Engineers might use data to determine the optimal number of operators required in a manual assembly process. This text teaches engineering and applied science students to incorporate empirical investigation into such design processes.

应用组合学 Applied Combinatorics 2016 Edition Mitchel T. Keller

本书最近好好地研究过了，这里终于要介绍Andrei Alexandrescu了，他是Real公司的项目经理，是GP模板技术的天才，他的高深模板技术影响 了BOOST以及全世界的模板怪杰，本书中他介绍他的库Loki，虽然库十分激进没有实际用途，当时展现的绚烂的特技令人叹服！同时本书是设计 模式用范型实现的经典展现，以及Policy设计模式在其中的极致应用，直接影响了BOOST的adaptor设计（7个Policy）甚至影响了标准库的智能 指针项目（虽然最后被否认了，当时绝对是完美的实现方法）

Python应用机器学习-密歇根大学--Coursera Coursera MOOC的课程材料：密歇根大学的Python应用机器学习，Python专业化应用数据科学课程3

本书从数学的角度对随机信号进行了详尽描述；对卡拉曼滤波及其在INS、GPS中的应用进行了阐述

应用预测建模 (Applied Predictive Modeling 中文版)

Python应用数据科学 ## Python 数据科学简介

在本研究中，我们提出了一种基于自我关注的区域流感预测模型，称为SAIFlu-Net。该模型利用一个较长的短期记忆网络来提取每个区域的时间序列模式，并利用自我注意机制来发现发生模式之间的相似性。为了评估其性能，我们使用每周区域流感数据集对现有的预测模型进行了广泛的实验。结果表明，该模型在均方根误差和皮尔逊相关系数方面均优于其他模型。

Handbook of Applied Cryptography - A. Menezes, P. VanOorschot, S. Vanstone.pdf