PyTorch library to facilitate development and standardized evaluation of neural network pruning methods.

Course materials for Dartmouth Course: Storytelling with Data (PSYC 81.09).

PyTorch implementations of deep reinforcement learning algorithms and environments

scripts for adobe illustrator (in javascript)

All Algorithms implemented in Python

👑 Python factor analysis library (PCA, CA, MCA, MFA, FAMD)

NSCI 801 (Queen's U) Quantitative Neuroscience course materials

website

Author's PyTorch implementation of BCQ for continuous and discrete actions

PyTorch implementation of SimCLR: A Simple Framework for Contrastive Learning of Visual Representations

Code and discussion around the MIMIC-IV database

Course notes for MSDS621 at Univ of San Francisco, introduction to machine learning

Python implementations of the k-modes and k-prototypes clustering algorithms, for clustering categorical data

A PyTorch Library for Meta-learning Research

[ECCV 2018] AMC: AutoML for Model Compression and Acceleration on Mobile Devices

Code for: "Fast and Flexible Multi-Task Classification Using Conditional Neural Adaptive Processes" and "TaskNorm: Rethinking Batch Normalization for Meta-Learning"

Python package for covariance matrices manipulation and Biosignal classification with application in Brain Computer interface

Simulation of spiking neural networks (SNNs) using PyTorch.

NEURAL: a neonatal EEG feature set in Matlab

Tutorial codes for modeling brains with neural nets

Matplotlib styles for scientific plotting

Deep learning software to decode EEG or MEG signals

Mother of All BCI Benchmarks

The Incredible PyTorch: a curated list of tutorials, papers, projects, communities and more relating to PyTorch.

python implementations of Analyzing Neural Time Series Textbook

Continual Learning Toolbox for Computer Vision Tasks

Summer course content for Neuromatch Academy

Efficient Transfer Learning with Meta Update for Cross Subject EEG Classification

An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.