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This lecture presents an overview of functional brain parcellations, as well as a set of tutorials on bootstrap agregation of stable clusters (BASC) for fMRI brain parcellation.

Difficulty level: Advanced
Duration: 50:28
Speaker: : Pierre Bellec

This short video walks you through the steps of publishing a dataset on brainlife, an open-source, free and secure reproducible neuroscience analysis platform.

Difficulty level: Beginner
Duration: 1:18
Speaker: :

This video shows how to use the brainlife.io interface to edit the participants' info file. This file is the ParticipantInfo.json file of the Brain Imaging Data Structure (BIDS).

Difficulty level: Beginner
Duration: 0:34
Speaker: :

This video will document the process of running an app on brainlife, from data staging to archiving of the final data outputs.

Difficulty level: Beginner
Duration: 3:43
Speaker: :

This video will document the process of visualizing the provenance of each step performed to generate a data object on brainlife.

Difficulty level: Beginner
Duration: 0:21
Speaker: :

This video will document the process of downloading and running the "reproduce.sh" script, which will automatically run all of the steps to generate a data object locally on a user's machine.

Difficulty level: Beginner
Duration: 3:44
Speaker: :

This video will document the process of creating a pipeline rule for batch processing on brainlife.

Difficulty level: Intermediate
Duration: 0:57
Speaker: :

This video will document the process of launching a Jupyter Notebook for group-level analyses directly from brainlife.

Difficulty level: Intermediate
Duration: 0:53
Speaker: :

This brief video walks you through the steps necessary when creating a project on brainlife.io. 

Difficulty level: Beginner
Duration: 1:45
Speaker: :

This brief video rus through how to make an accout on brainlife.io.

Difficulty level: Beginner
Duration: 0:30
Speaker: :

This video will document how to run a correlation analysis between the gray matter volume of two different structures using the output from brainlife app-freesurfer-stats.

Difficulty level: Beginner
Duration: 1:33
Speaker: :

This lecture introduces you to the basics of the Amazon Web Services public cloud. It covers the fundamentals of cloud computing and goes through both the motivations and processes involved in moving your research computing to the cloud.

Difficulty level: Intermediate
Duration: 3:09:12

As a part of NeuroHackademy 2020, this lecture delves into cloud computing, focusing on Amazon Web Services. 

Difficulty level: Beginner
Duration: 01:43:59

This lecture covers concepts associated with neural nets, including rotation and squashing, and is a part of the Deep Learning Course at New York University's Center for Data Science (CDS).

Difficulty level: Intermediate
Duration: 1:01:53
Speaker: : Alfredo Canziani

This lecture covers the concept of neural nets training (tools, classification with neural nets, and PyTorch implementation) and is a part of the Deep Learning Course at NYU's Center for Data Science.

Difficulty level: Intermediate
Duration: 1:05:47
Speaker: : Alfredo Canziani

This lecture discusses the concept of natural signals properties and the convolutional nets in practice and is a part of the Deep Learning Course at NYU's Center for Data Science.

Difficulty level: Intermediate
Duration: 1:09:12
Speaker: : Alfredo Canziani

This lecture covers the concept of recurrent neural networks: vanilla and gated (LSTM) and is a part of the Deep Learning Course at NYU's Center for Data Science.

Difficulty level: Intermediate
Duration: 1:05:36
Speaker: : Alfredo Canziani

This lecture covers the concept of inference in latent variable energy based models (LV-EBMs) and is a part of the Deep Learning Course at NYU's Center for Data Science. 

Difficulty level: Intermediate
Duration: 1:01:04
Speaker: : Alfredo Canziani

This tutorial covers the concept of training latent variable energy based models (LV-EBMs) and is is a part of the Deep Learning Course at NYU's Center for Data Science.

Difficulty level: Intermediate
Duration: 1:04:48
Speaker: : Alfredo Canziani

This lecture covers advanced concept of energy based models. The lecture is a part of the Advanced energy based models modules of the the Deep Learning Course at NYU's Center for Data Science. Prerequisites for this course include: Energy-Based Models IEnergy-Based Models II, Energy-Based Models III, and an Introduction to Data Science or a Graduate Level Machine Learning course.

Difficulty level: Beginner
Duration: 56:41
Speaker: : Alfredo Canziani