7 个仓库
Awesome GitHub RepositoriesVectorized Data Processing
Use of vectorized operations on contiguous memory arrays for high-speed numerical calculations.
Distinct from NumPy Array Integration: Focuses on the performance benefit of vectorized operations rather than just the memory binding layer.
Explore 7 awesome GitHub repositories matching scientific & mathematical computing · Vectorized Data Processing. Refine with filters or upvote what's useful.
Awesome Vectorized Data Processing GitHub Repositories
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- prml/prmlt
PRML/PRMLT
6,207在 GitHub 上查看PRMLT provides self-contained MATLAB implementations of every algorithm from the Pattern Recognition and Machine Learning textbook by Christopher Bishop. The code reproduces the book's exact formulas and notation, making each implementation directly traceable to the source material for educational verification and study. The implementations cover the full range of core machine learning methods from the textbook, including classification, clustering, regression, density estimation, and neural network algorithms. Each module is self-contained with heavy comments, and the code uses compact, vect
Compact, vectorized Matlab functions that execute machine learning algorithms efficiently on array data using matrix operations.
MATLAB - hadley/r4ds
hadley/r4ds
5,070在 GitHub 上查看r4ds 是一个数据科学课程和教育资源，专为精通 R 编程语言而设计。它为导入、整理、转换和可视化数据的端到端过程提供了结构化的学习路径。 该项目强调可重复的数据科学指南和全面的数据整理课程。它包括关于用于分层数据可视化的图形语法（grammar of graphics）的专业教程，以及使用 Quarto 创建的融合可执行代码与叙述性文本的技术出版物。 该材料涵盖了广泛的分析能力，包括来自不同来源的数据摄取、关系数据连接以及分类变量的管理。它还涉及数据清洗、数学建模以及多格式专业报告和演示文稿的生成。 该课程侧重于函数式编程和整洁数据（tidy data）原则的实际应用，以创建透明且可重复的分析。
Implements high-speed numerical calculations by applying operations across entire data vectors simultaneously.
R - susanli2016/machine-learning-with-python
susanli2016/Machine-Learning-with-Python
4,583在 GitHub 上查看该项目是一个 Python 机器学习库和数据科学工具包，旨在构建预测模型和分析复杂数据集。它提供了一系列使用 Scikit-Learn 框架实现的常见监督和无监督算法。 该工具包包括一个用于从历史数据生成预测的预测建模套件，以及一个用于应用贝叶斯建模和因果检验的统计分析框架。它还具有一个基于 Matplotlib 的数据可视化套件，用于渲染静态图表和图形，以解释分类器边界和数据趋势。 该项目涵盖了用于识别模式和细分的数据聚类工作流、探索性数据分析，以及使用 Pandas 和 NumPy 进行的数据预处理。
Uses NumPy vectorized operations on contiguous memory arrays to ensure high computational efficiency for mathematical operations.
Jupyter Notebook - cuemacro/finmarketpy
cuemacro/finmarketpy
3,777在 GitHub 上查看finmarketpy is a quantitative trading framework and financial market analysis tool. It provides a Python-based library for simulating trading strategies against historical market data, computing the value of options contracts, and extracting trends from financial datasets. The system includes specialized engines for financial options pricing using numerical calculations and a backtesting library to assess risk and performance before live deployment. It further enables the detection of market seasonality and the execution of event studies to measure asset price behavior around specific time wi
Uses vectorized operations on contiguous memory arrays for high-speed numerical calculations of financial time series.
Python - topfunky/hpple
topfunky/hpple
2,880在 GitHub 上查看This project is a multi-purpose toolkit comprising a static site generator, a predictive modeling tool, and a sports analytics dashboard. It functions as a content syndication engine that converts source files into static HTML and machine-readable XML streams for blogs and professional portfolios. The system features a data processing engine designed for sports performance analytics, using linear and logistic regression to estimate season win totals and calculate win probabilities. It includes a time-series visualization framework that renders these performance trends using high-contrast them
Increases computation speed by processing large datasets using array-based vectorized operations.
Objective-C - jack-cherish/quantitative
Jack-Cherish/quantitative
2,534在 GitHub 上查看This project is a Python quantitative trading framework and library designed for developing, backtesting, and deploying automated financial strategies. It serves as both an algorithmic trading backtester for evaluating historical performance and an event-driven trading engine for executing trades based on quantitative rules. The framework functions as an educational toolkit, providing guided lessons and resources for quantitative finance learning and the application of mathematical models to market data. The system provides capabilities for algorithmic trading automation and financial strate
Utilizes NumPy and Pandas for vectorized array processing to compute financial indicators without row-level loops.
Python - stephencwelch/neural-networks-demystified
stephencwelch/Neural-Networks-Demystified
1,361在 GitHub 上查看Neural Networks Demystified is an educational resource consisting of interactive Python notebooks designed to explain the fundamental mathematical concepts behind neural networks. It serves as a tutorial for understanding how these models process data and learn from patterns through supervised learning implementations. The project functions as a visualization tool that demonstrates core mechanics such as forward propagation and gradient descent. By utilizing notebook-driven execution, it allows for the inspection of intermediate data states and mathematical transformations as they occur durin
Utilizes vectorized operations on arrays to perform high-speed numerical computations.
Jupyter Notebook
