jvines

Jose Vines's starred repositories

julia

The Julia Programming Language

handson-ml2

A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.

Language:Jupyter NotebookApache-2.027688 656 511

Probabilistic-Programming-and-Bayesian-Methods-for-Hackers

aka "Bayesian Methods for Hackers": An introduction to Bayesian methods + probabilistic programming with a computation/understanding-first, mathematics-second point of view. All in pure Python ;)

Language:Jupyter NotebookMIT26672 1372 245

nn-zero-to-hero

Neural Networks: Zero to Hero

Language:Jupyter NotebookMIT11502 286 30

mplcyberpunk

"Cyberpunk style" for matplotlib plots

Language:PythonMIT1671 17 24

Yolo_Label

GUI for marking bounded boxes of objects in images for training neural network YOLO

Language:C++MIT487 8 54

machinelearning-az

Repositorio del Curso de Machine Learning de la A a la Z con R y Python

Language:Jupyter NotebookMIT422 990

lightkurve

A friendly package for Kepler & TESS time series analysis in Python.

Language:PythonMIT411 20 723

acronym

ACRONYM (Acronym CReatiON for You and Me)

Language:PythonMIT381 9 12

fracdiff

Compute fractional differentiation super-fast. Processes time-series to be stationary while preserving memory. cf. "Advances in Financial Machine Learning" by M. Prado.

Language:PythonBSD-3-Clause288 3 43

codeastro

Course Material for the Code/Astro Workshop

Language:Jupyter NotebookBSD-3-Clause163 9 89

Practical-Applications-in-R-for-Psychologists

Lesson files for Practical Applications in R for Psychologists.

Language:RNOASSERTION125 5 19

eleanor

A tool for light curve extraction from the TESS FFIs.

Language:PythonMIT91 5 175

dataviz

Course materials for Kieran Healy's rstudio::conf 2020 data visualization workshop

CC-BY-SA-4.083 110

celerite2

Fast & scalable Gaussian Processes in one dimension

Language:C++MIT68 7 49

allesfitter is a convenient wrapper around the packages ellc (light curve and RV models), dynesty (static and dynamic nested sampling) emcee (Markov Chain Monte Carlo sampling) and celerite (Gaussian Process models).

Language:Jupyter NotebookMIT59 4 73