ashcat2005 Goto Github PK

2023_summer_school

accretiondisks

Accretion Disks

agujeros_negros

Eduard Larrañaga

agujerosnegrosdrgt

andromeda

Ray tracing in curved backgrounds

ashcat2005.github.io

ast8581_2024_spring

Github repository for "Big Data in Astrophysics" - Spring 2024

astroneuron

Machine Learning Applied to Astrophysical Data Analysis

astropy-workshop

Materials for Astropy Workshops

barnes-hut

Barnes-Hut algorithm applied to the modeling of a galaxy.

bhi

bhkeynotes

binary_coalescence

Numerical modeling of the coalescence of a Newtonian binary system due to energy and angular momentum lose due to the emission of gravitational radiation

black-hole-shadows

Black Hole Shadows

ca-sessions-2022-2

Lecture slides, Jupyter notebooks, and other material

ca2023-01

Public Repository for the extended seminar "A numerical code to generate the image of a black hole"

ca2023-02

Public repository for the second colloquium of the Computational Astrophysics research group, "An Introduction to Julia programming"

ca2023-03

Smoothed Particle Hydrodynamics (SPH) Code implemented by the Computational Astrophysics Group 2023.

ca2023-04

Black Holes feature extraction using a Convolutional Neural Network. Computational Astrophysics group 2023

ca_meetings2023

Computational Astrophysics group meetings 2023

celestialcalculations.github.io

Release v3.0

classicalbhs

Deduction of the Schwarzschild and Kerr Metrics and its physical properties.

computationalastrophysics

Public repository for the Computational Astrophysics course at the Observatorio Astronómico Nacional.

computationalastrophysics2022

Repositorio del Curso de Astrofísica Computacional 2022

cs273a-introduction-to-machine-learning

Introduction to machine learning and data mining How can a machine learn from experience, to become better at a given task? How can we automatically extract knowledge or make sense of massive quantities of data? These are the fundamental questions of machine learning. Machine learning and data mining algorithms use techniques from statistics, optimization, and computer science to create automated systems which can sift through large volumes of data at high speed to make predictions or decisions without human intervention. Machine learning as a field is now incredibly pervasive, with applications from the web (search, advertisements, and suggestions) to national security, from analyzing biochemical interactions to traffic and emissions to astrophysics. Perhaps most famously, the $1M Netflix prize stirred up interest in learning algorithms in professionals, students, and hobbyists alike. This class will familiarize you with a broad cross-section of models and algorithms for machine learning, and prepare you for research or industry application of machine learning techniques. Background We will assume basic familiarity with the concepts of probability and linear algebra. Some programming will be required; we will primarily use Matlab, but no prior experience with Matlab will be assumed. (Most or all code should be Octave compatible, so you may use Octave if you prefer.) Textbook and Reading There is no required textbook for the class. However, useful books on the subject for supplementary reading include Murphy's "Machine Learning: A Probabilistic Perspective", Duda, Hart & Stork, "Pattern Classification", and Hastie, Tibshirani, and Friedman, "The Elements of Statistical Learning".

data-driven-astronomy

Data-driven Astronomy Coursera course

data-science-complete-tutorial

For extensive instructor led learning

early-videogames

Python implementation of some early games from the Atari era

einsteinpy

Repository for the EinsteinPy core package :rocket:

ejercicios06

Ejerciciois 06. Volúmenes Finitos. Advección Multidimensional