JoJoHu's repositories
matrixcalc
MIT IAP short course: Matrix Calculus for Machine Learning and Beyond
ML_course
EPFL Machine Learning Course, Fall 2021
dmol-book
Deep learning for molecules and materials book
aml_bayes
Code for the paper: Adversarial Machine Learning: Bayesian Perspectives
jax_notebooks
Repository of Jupyter notebook tutorials for teaching the Deep Learning Course at the University of Amsterdam (MSc AI), Fall 2022/Spring 2022
falkon
Large-scale, multi-GPU capable, kernel solver
numerical-tours
Numerical Tours of Signal Processing
probai-2022
Materials of the Nordic Probabilistic AI School 2022.
gpss_labs
Repository for labs
lassonet
Feature selection in neural networks
optimization_course
A course on Optimization Methods
get-started-with-JAX
The purpose of this repo is to make it easy to get started with JAX, Flax, and Haiku. It contains my "Machine Learning with JAX" series of tutorials (YouTube videos and Jupyter Notebooks) as well as the content I found useful while learning about the JAX ecosystem.
coding-for-economists
This repository hosts the code behind the online book, Coding for Economists.
pyprobml
Python code for "Probabilistic Machine learning" book by Kevin Murphy
liboptpy
Implementation of various optimization methods
pyHSICLasso
Versatile Nonlinear Feature Selection Algorithm for High-dimensional Data
brglm2
Estimation and inference from generalized linear models using explicit and implicit methods for bias reduction
scientific-visualization-book
An open access book on scientific visualization using python and matplotlib
chebpy
A Python implementation of Chebfun
seminars-fivt
Seminars on optimization methods
bayesoptbook.github.io
Companion webpage for the book "Bayesian Optimization" by Roman Garnett
LMP
Linear Models with Python
AManPG
Implementation of chenshixiang/AManPG in R, Python, Julia.
scikit-fda
Functional Data Analysis Python package
newcorrelationstatistics
xi correlation method adapted for python
awesome-self-supervised-learning
A curated list of awesome self-supervised methods