#计算机科学#General statistics, mathematical programming, and numerical/scientific computing scripts and notebooks in Python
Python notebooks for Numerical Analysis
Introduction to Python: Numerical Analysis for Engineers and Scientist. In 2017, Python became the world's most popular programming language. This course covers the basic syntax, linear algebra, plott...
#计算机科学#Data science and numerical computing with Julia
#博客#Slides/notes and Jupyter notebook demos for an introductory course of numerical analysis/scientific computing
#计算机科学#This repository contains python codes to classify earthquake rupture based on random forest and neural network.
Este é um repositório que contem os notebooks do projeto PET.py do PET - Física da UFRN.
Curso de Métodos Numéricos empleando las herramientas Jupyter Notebook y programado en Python V3.11
(TACAS 24) Python Package for disspaitve quadratization, from Paper "Dissipative quadratizations of polynomial ODE systems"
Complete numerical computing labs with pdf documents. You just need to change coding according to your question requirements.
#算法刷题#WONC-FD (Wavelet-Based Optimization and Numerical Computing for Fault Detection)
Algorithms that use numerical approximation for the problems of mathematical analysis.
本科的专业课练习。(1)数值分析：LU分解、二分法-试位法、二次样条插值、定点迭代法-牛顿切线法-正割法求根、带换主元高斯消去法、拉格朗日插值、最小二乘法、牛顿差商法、积分、贝尔斯托递归求根。(2)生产者-消费者问题、页面置换算法、进程管理、文件管理
This is our Github Repository containing the Python projects for the course: Introduction to Programming and Numerical Analysis at the University of Copenhagen
#计算机科学#Nonconvex Accelerated Gradient Method developed by me; paper published at Statistics and Computing -- "Accelerated gradient methods for sparse statistical learning with nonconvex penalties"
DATA-X: m110 - Numpy - Introduction to Numerical Analysis Using NumPy. These materials introduce developers and data scientists to numerical analysis and data manipulation using NumPy. NumPy is the nu...
Air quality prediction using Interpolation (Lagrange, Newton's Divided Difference, Cubic Spline) for Polynomials
Review of the Noisy Trust Region Method