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This tutorial aims at introducing students to the use of command line terminal which offers more flexibility than built-in graphical user interfaces. We hope to provide students with an understanding of the basic command lines and advantages of working with the bash shell.
How deep learning can be used in neuroimaging analyses? A hands-on example using the nobrainer library and Montreal AI-Neuroscience workshop material.
The objectives of this module are to explore and get to know more about the container system (file system and processes) and understand that ‘containers aren’t magic’
An introduction to fMRI data: the captured signal, the main steps of preprocessing and how functional connectivity is calculated.
The objectives of this module are to: 1) Understand the basis of the signal used in functional magnetic resonance imaging. 2) Know the main steps of preprocessing fMRI data. 3) Know how functional connectivity is calculated, and how it is most commonly used. 4) Know the main brain parcellations and associated technical challenges .
Introduction to HPC infrastructure and parallel computing, using Alliance Canada (formerly Compute Canada), or Brainhack Cloud.
Instructions to install and setup all the tools required for the BrainHack summer school.
In this module, we will introduce the basics of plotting in python with some of most commonly used packages such as matplotlib and seaborn.
The objectives of this module are to learn some of the fundamentals of using deep learning for neuroscience
This repo includes a tutorial for working with dMRI data using DIPY
This is the list of training modules for Brainhack School.
24 modules are currently displayed.
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