Álvaro Deleglise's repositories
cs-video-courses
List of Computer Science courses with video lectures.
ann4brains
Artificial neural networks for brain networks
Applied-Deep-Learning
Applied Deep Learning
BrainEigenmodes
Code supporting 'Geometric constraints on human brain function'
BrainSpace
BrainSpace is an open-access toolbox that allows for the identification and analysis of gradients from neuroimaging and connectomics datasets | available in both Python and Matlab |
communities
Library for detecting and visualizing community structure in graphs
COMS4995-s20
COMS W4995 Applied Machine Learning - Spring 20
deeptime
Python library for analysis of time series data including dimensionality reduction, clustering, and Markov model estimation
GraKeL
A scikit-learn compatible library for graph kernels
kuramoto
Python implementation of the Kuramoto model on graphs
LiteratureDL4Graph
A comprehensive collection of recent papers on graph deep learning
ml-workshop-2-of-4
Intermediate Machine Learning with Scikit-learn, 4h interactive workshop
multiAtlasTT
multi atlas transfer tools for neuroimaging (maTT)
netrd
A library for network {reconstruction, distances, dynamics}
network_TDA_tutorial
This repository is dedicated for the tutorial on network and topological neuroscience.
neurolib
Easy whole-brain modeling for computational neuroscientists 🧠💻👩🏿🔬
NeuroMatch_CompNeuro_2020_course
Content for NMA Computational Neuroscience course
NYU-DLSP21
NYU Deep Learning Spring 2021
persona2vec
Persona2vec: A Flexible Multi-role Representations Learning Framework for Graphs
pybrain_dipy
Two days dipy workshop
pyGAlib
Graph Analisys library based on Python / NumPy
PySurfer
Cortical neuroimaging visualization in Python
rest2vec
rest2vec: Vectorizing the resting-state functional connectome using graph embedding
scikit-learn
scikit-learn: machine learning in Python
scikit-learn-mooc
Machine learning in Python with scikit-learn MOOC
scikit-network
Graph Algorithms
SynthSR
A framework for joint super-resolution and image synthesis, without requiring real training data