SMART（Spectral energy distributions Markov chain Analysis with Radiative Transfer models）是一种开源工具，它实现了贝叶斯马尔可夫链蒙特卡罗（MCMC）方法，以仅使用辐射传输模型拟合星系的紫外-毫米光谱能量分布（SED）。
SMART (Spectral energy distributions Markov chain Analysis with Radiative Transfer models) is an open-source tool that implements a Bayesian Markov chain Monte Carlo (MCMC) method to fit the ultraviolet to millimetre spectral energy distributions (SEDs) of galaxies exclusively with radiative transfer models. (2024-05-04, Others, 0KB, 下载0次)
证明了爱因斯坦的光速恒定假设是一个基于对基础数学的无知的错误假设，指出了量子力学波动方程破坏能量守恒定律的问题，证明了LIGO的引力波观测报告纯属谎言，修正了。。。
Proved that Einstein s assumption of constant speed of light was a mistaken assumption based on ignorance of basic mathematics, pointed out the problem of quantum mechanics wave equations breaking the law of energy conservation, proved that LIGO s gravitational wave observation report was purely a lie, corrected the basic equations of ... (2024-05-04, Others, 0KB, 下载0次)
该项目旨在利用机器学习的力量来解决准确和及时检测乳腺癌的关键需求。通过将KNN与朴素贝叶斯分类相结合，我们寻求开发一种可靠的工具，可以帮助医疗保健专业人员更有效地检测乳腺癌。
This project aims to harness the power of machine learning to address the critical need for accurate and timely breast cancer detection. By combining KNN with Naive Bayes classification, we seek to develop a reliable tool that can assist healthcare professionals for detecting breast cancer more effectively. (2024-04-28, Others, 0KB, 下载0次)
该存储库具有用于肿瘤分类的ML模型：Logistic回归、KNN、朴素贝叶斯、决策树和集成。它区分恶性肿瘤和良性肿瘤，通过预处理缩放进行优化，并为准确的癌症诊断提供比较报告。
This repository features ML models for tumor classification: Logistic Regression, KNN, Naive Bayes, Decision Trees, and Ensembles. It distinguishes malignant from benign tumors, optimizes with preprocessing scaling, and provides a comparative report for accurate cancer diagnosis. (2024-04-27, Others, 0KB, 下载0次)
使用多项式朴素贝叶斯和n元图的电影类型分类涉及使用处理文本数据并假设每个类型类别中的特征之间独立的概率模型，根据电影的情节摘要预测电影的类型。
Movie Genre Classification using Multinomial Naive Bayes and n-grams involves predicting the genre of a movie based on its plot summary using a probabilistic model that handles text data and assumes independence among features within each genre category. (2024-04-07, Others, 0KB, 下载0次)
在线疾病预测系统，允许用户使用面临的症状来识别疾病。（使用多名义朴素贝叶斯ML算法）使用新闻API提供最新健康新闻提供具有Paypal支付集成的支付设施Covid-19数据分析（总病例、痊愈病例、死亡病例）。
Online Disease Prediction System which allows users to identify diseases using the symptoms faced. (Used Multi-Nomial Naive Bayes ML Algorithm) Provide Latest Health News using News API Provide Payment Facilities with Paypal Payment Integration Covid-19 Data analysis (total, recovered, death cases). (2024-03-14, Others, 0KB, 下载0次)
疾病诊断是患者护理的一个关键方面，将从尖端技术的集成中受益匪浅。在本项目中，重点是贝叶斯滤波的应用，这是一种复杂的概率建模方法，用于估计患者患有特定疾病的可能性。
Disease diagnosis, a pivotal aspect of patient care, stands to benefit immensely from the integration of cutting-edge technologies. In this project, the focus is specifically on the application of Bayesian filtering, a sophisticated probabilistic modeling approach, to estimate the likelihood of a patient having a particular disease. (2024-03-03, Others, 0KB, 下载0次)
使用从德克萨斯州收集的可再生能源数据，对施耐德电气进行线性回归。该测试中使用的方法包括异方差、多重共线性、自动（串行）相关性、普通最小二乘法以及更多的计量经济学和统计学方法。
Linear Regression performed for Schneider Electric using renewable energy data collection from Texas. Methodologies utilized in this testing include Heteroscedasticity, multicollinearity, Auto(Serial) correlation, Ordinary Least Squares, and more econometrics and statistical approaches. (2024-03-01, Others, 0KB, 下载0次)
这是2018年互联网在线CTF（Catch-The Flag）Open World黑客和反向工程挑战GitHub官方知识库。希望最好和好运-Cyber Intel团队（斯里兰卡）。
This is The GitHub Official Repository of The Cyber-Intel Online CTF (Catch- The-Flag) 2018 Open-World Hacking and Reverse Engineering Challenge. Hope For The Best and Good Luck - Team Cyber-Intel (Sri Lanka). (2018-01-30, Others, 0KB, 下载0次)
根据加拿大政府实现其和平、正义和强大机构的可持续目标的授权，创建了一个视觉上吸引人的信息图形和美学上令人愉悦的仪表盘，以展示对努纳武特犯罪的见解。在演示轮中获得第一名，在信息图表轮中获得第三名...
Created a visually appealing infographic and aesthetically pleasing dashboard to present insights about crime in Nunavut in accordance with the Canadian government s mandate to achieving their sustainable goal of Peace, Justice, and strong institutions. Rewarded with 1st place in the presentation round and 3rd place in the infographic round by (2021-05-02, Others, 0KB, 下载0次)
这本书的主题，贝叶斯统计，帮助我们更好地推理关于不确定性，就像在学校学习逻辑帮助我们看到 日常逻辑思维中的错误。因为几乎每个人都要处理他们日常生活中的不确定性，正如我们刚才讨论的，这让观众因为这本书很广。数据科学家和研究人员已经在使用统计学将受益于更深入的理解和直觉工具起作用。工程师和程序员将学到很多关于他们可以更好地量化他们必须做出的决定
The subject of this book, Bayesian statistics, helps us get better at reasoning
about uncertainty, just as studying logic in school helps us to see the errors in everyday logical thinking. Given that virtually everyone deals with uncertainty in their daily life, as we just discussed, this makes the audience for this book pretty wide. Data scientists and researchers already using statistics will benefit from a deeper understanding and intuition for how these tools work. Engineers and programmers will learn a lot about how they can better quantify decisions they have to make (2020-08-04, Others, 3338KB, 下载0次)
稀疏矩阵的DIA/ELLPACK/COO/CSR/HYB表示形式，以及各表示形式下的稀疏矩阵乘法（稀疏大矩阵*矢量）的CUDA实现。对于矩阵中每一行稀疏元素个数较统一的情况，ELLPACK表示最佳，其次是HYB（ELL+COO）。
CUDA™ 是一种由NVIDIA推出的通用并行计算架构，该架构使GPU能够解决复杂的计算问题。 它包含了CUDA指令集架构（ISA）以及GPU内部的并行计算引擎。 开发人员现在可以使用C语言来为CUDA™ 架构编写程序
Sparse matrix DIA/ELLPACK/COO/CSR/HYB representation, as well as the representation of the sparse matrix multiplication (large sparse matrix* vector)' s CUDA implementation. For each row of the sparse matrix representing the number of elements the case of unification, ELLPACK that the best, followed by HYB (ELL+COO). NVIDIA CUDA ™ is introduced by a general purpose parallel computing architecture that makes the GPU to solve complex computational problems. It contains the CUDA Instruction Set Architecture (ISA) and the GPU parallel computing engine. Developers can now use the C language to write programs for the CUDA ™ architecture (2013-07-11, Others, 3206KB, 下载62次)
高等代数(北大版)课件(ch01-ch04)
非常详细的课件，基本涵盖了原书的所有内容。
（这一部分包含第一章到第四章）
第一章 多项式
§1.1 数域
§1.2 一元多项式
§1.3 整除的概念
§1.4 最大公因式
§1.5 因式分解定理
§1.6 重因式
§1.7 多项式函数
§1.8 复系数与实系数多项式的因式分解
§1.9 有理系数多项式
§1.10 多元多项式
§1.11 对称多项式
第二章 行列式
§2.1 引言
§2.2 排列
§2.3 n阶行列式
§2.4 n级行列式的性质
§2.5 行列式的计算
§2.6 行列式按一行（列）展开
§2.7 克兰姆(Cramer)法则
§2.8 Laplace定理 行列式的乘法规则
第三章 线性方程组
§3.1 消元法
§3.2 n维向量空间
§3.3 线性相关性
§3.4 矩阵的秩
§3.5 线性方程组有解判别定理
§3.6 线性方程组解的结构
第四章 矩阵
§4.1 矩阵的概念
§4.2 矩阵的运算
§4.3 矩阵乘积的行列式与秩
§4.4 矩阵的逆
§4.5 矩阵的分块
§4.6 初等矩阵
§4.7 分块乘法的初等变换及应用
advanced algebra (pku version) chapter 01-04 (2013-03-31, Others, 14805KB, 下载16次)