João Camacho's starred repositories
github-readme-stats
:zap: Dynamically generated stats for your github readmes
Data-Science-For-Beginners
10 Weeks, 20 Lessons, Data Science for All!
machine-learning-notes
My continuously updated Machine Learning, Probabilistic Models and Deep Learning notes and demos (2000+ slides) 我不间断更新的机器学习,概率模型和深度学习的讲义(2000+页)和视频链接
cheatsheets
Official Matplotlib cheat sheets
matplotplusplus
Matplot++: A C++ Graphics Library for Data Visualization 📊🗾
whatsapp-for-linux
An unofficial WhatsApp desktop application for Linux.
Stalker_GAMMA
S.T.A.L.K.E.R. Anomaly G.A.M.M.A. modpack definition files.
numba-scipy
numba_scipy extends Numba to make it aware of SciPy
clickmodels
ClickModels is a small set of Python scripts for the user click models initially developed at Yandex. A Click Model is a probabilistic graphical model used to predict search engine click data from past observations. This project is aimed to deal with click models used in Information Retrieval (see next README.md) and intended to be easy-to-read and easy-to-modify. If it's not, please let me know how to improve it :)
freelancer-hd-edition
Freelancer: HD Edition is a mod that aims to improve every aspect of the game Freelancer (2003) while keeping the look and feel as close to vanilla as possible.
tutorial-pip-package
Tutorial on how to create your own pip package
beg-cobol-for-programmers
Source code for 'Beginning COBOL for Programmers' by Michael Coughlan
latex-input
Enter Unicode characters using LaTeX notation
cobol-is-fun
This repository contains fun COBOL applications used in tutorials and code patterns on IBM Developer.
search-MjoLniR
Github mirror - our actual code is hosted with Gerrit (please see https://www.mediawiki.org/wiki/Developer_access for contributing)
agentphantom
Automated GitHub profile content using the USGS API, Plotly and GitHub Actions.
TicTacTOBOL
❎ A basic "game" in COBOL for learning
COBOL-Guide
COBOL Guide
Necropolis
Necropolis is a package manager for COBOL
neuronalGPR
Source code producing all figures and tables of the paper "Neuronal Gaussian Process Regression"