Peer Herholz's starred repositories
AI_in_CogNeuro
Materials for AI in Cognitive Neuroscience Summer School, 2024
version-control-course-uhh-eur-ws24
This repository contains the source code for the course website of the full-semester course on "Track, organize and share your work: An introduction to Git for research" at University of Hamburg and Erasmus University Rotterdam.
ml_and_brain_2023
CMPUT 624 at UAlberta / Machine Learning & the Brain Repo. Course Landing page with links to videos, code and slides.
cortico_cereb_connectivity
Cortico cerebellar connectivity models from the Diedrichsenlab
multimodal
TorchMultimodal is a PyTorch library for training state-of-the-art multimodal multi-task models at scale.
multimodal-deep-learning
This repository contains various models targetting multimodal representation learning, multimodal fusion for downstream tasks such as multimodal sentiment analysis.
bids_sm_model_spec_gui
GUI for generating BIDS Stats Model model spec json files
advanced-git
Course materials for a one-day workshop about advanced features of git & github
intro-to-git
Course materials for a one-day workshop introducing the essentials of using git and GitHub
neuro4ml.github.io
Neuroscience for machine learners course
methods_in_neuro
A list of links and resources for my Methods in Neuroimaging Course
audio-ai-timeline
A timeline of the latest AI models for audio generation, starting in 2023!
PythonNumericalDemos
Well-documented Python demonstrations for spatial data analytics, geostatistical and machine learning to support my courses.
Audio-Classification
Code for YouTube series: Deep Learning for Audio Classification
llm-course
Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks.
containers-introduction-training
Course website "Docker and Singularity for reproducible research: getting started with containers"
leaf-pytorch
PyTorch implementation of the LEAF audio frontend
leaf-audio
LEAF is a learnable alternative to audio features such as mel-filterbanks, that can be initialized as an approximation of mel-filterbanks, and then be trained for the task at hand, while using a very small number of parameters.
mriqc-classification
Classification exploration of MRIQC outputs