Name: Michael Notter
Type: User
Bio: Senior ML researcher & neuroscientist fascinated by hidden patterns, innovating in neuroimaging, computer vision, AR/VR, vital signs & multi-sensor sensing.
Twitter: miyka_el
Location: Lausanne, Switzerland
Michael Notter's Projects
How to 3D print your brain from a T1 MRI image.
Python interface for generating coordinate tables and region labels from statistical MRI images
Binder metapackage for usage, docs, and chat
Code and documentation for the Brains for Publication proposal for the 2016 OHBM Hackathon
Small Notebook to create fake fMRI results (for tutorial or class) - run it online at ->
Keep scientific data under control with git and git-annex
fmriflows is a consortium of many (dependent) fMRI analysis pipelines, including anatomical and functional pre-processing, univariate 1st and 2nd-level analysis, as well as multivariate pattern analysis.
How to create fancy GIFs from an MRI brain image
A little project for tracking hackathons
Slides and Notebooks used for the Neuroimaging Journal Club at UAB in March 2020
Notebooks containing my solution for the TReNDS Neuroimaging challenge on Kaggle
Keras documentation, hosted live at keras.io
Python based EEG analysis tool that provides a rough data overview
Repo for my personal page: https://miykael.github.io/
A toolkit to optimize ML models for deployment for Keras and TensorFlow, including quantization and pruning.
Repository to provide framework to run workshop_pybrain on mybinder
Collect different kinds of satellite images from https://worldview.earthdata.nasa.gov
nbconvert as a web service: Render Jupyter Notebooks as static web pages
A set of notebooks to introduce neuroscientists to concepts in information visualization.
Workflows and interfaces for neuroimaging packages
Beginner's guide for Nipype
A Docker image for a basic nipype environment
Learn Nipype with these tutorial notebooks - go here to see them online -->
Contains the code to create a fun gif in which Noah Kalina ages 20 years in 20 seconds.
Collection of different approaches to tackle the PAC2018 challenge
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent