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Difficulty level: Beginner
Duration: 6:10
Speaker: : MATLAB®
Difficulty level: Beginner
Duration: 15:10
Speaker: : MATLAB®
Difficulty level: Beginner
Duration: 2:49
Speaker: : MATLAB®
Difficulty level: Beginner
Duration: 6:10
Speaker: : MATLAB®

This tutorial illustrates several ways to approach predictive modeling and machine learning with MATLAB.

Difficulty level: Beginner
Duration: 6:27
Speaker: : MATLAB®
Difficulty level: Beginner
Duration: 3:55
Speaker: : MATLAB®
Difficulty level: Beginner
Duration: 3:52
Speaker: : MATLAB®

A brief overview of the Python programming language, with an emphasis on tools relevant to data scientists. This lecture was part of the 2018 Neurohackademy, a 2-week hands-on summer institute in neuroimaging and data science held at the University of Washington eScience Institute.

Difficulty level: Beginner
Duration: 1:16:36
Speaker: : Tal Yarkoni

This tutorial was part of the 2018 Neurohackademy, a 2-week hands-on summer institute in neuroimaging and data science held at the University of Washington eScience Institute.

Difficulty level: Beginner
Duration: 1:21:40
Speaker: : Tal Yarkoni

This lecture will provide an overview of neuroimaging techniques and their clinical applications.

Difficulty level: Beginner
Duration: 45:29
Speaker: : Dafna Ben Bashat

Lecture on functional brain parcellations and a set of tutorials on bootstrap agregation of stable clusters (BASC) for fMRI brain parcellation which were part of the 2019 Neurohackademy, a 2-week hands-on summer institute in neuroimaging and data science held at the University of Washington eScience Institute.

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

BioImage Suite is an integrated image analysis software suite developed at Yale University. BioImage Suite has been extensively used at different labs at Yale since about 2001.

Difficulty level: Beginner
Duration: 01:47
Speaker: : BioImage Suite
Course:

Fibr is an app for quality control of diffusion MRI images from the Healthy Brain Network, a landmark mental health study that is collecting MRI images and other assessment data from 10,000 New York City area children. The purpose of the app is to train a computer algorithm to analyze the Healthy Brain Network dataset. By playing fibr, you are helping to teach the computer which images have sufficiently good quality and which images do not. 

Difficulty level: Beginner
Duration: 02:26
Speaker: : Ariel Rokem

This lecture provides guidance on the ethical considerations the clinical neuroimaging community faces when applying the FAIR principles to their research. This lecture was part of the FAIR approaches for neuroimaging research session at the 2020 INCF Assembly.

Difficulty level: Beginner
Duration: 13:11
Speaker: : Gustav Nilsonne

This module covers many of the types of non-invasive neurotech and neuroimaging devices including Electroencephalography (EEG), Electromyography (EMG), Electroneurography (ENG), Magnetoencephalography (MEG), functional Near-Infrared Spectroscopy (fNRIs), Magnetic Resonance Imaging (MRI), Positron Emission Tomography (PET), and Computed Tomography

Difficulty level: Beginner
Duration: 13:36
Speaker: : Harrison Canning

This lecture covers advanced concepts of energy based models. The lecture is a part of the Advanced energy based models modules of the the Deep Learning Course at CDS, a course that covered the latest techniques in deep learning and representation learning, focusing on supervised and unsupervised deep learning, embedding methods, metric learning, convolutional and recurrent nets, with applications to computer vision, natural language understanding, and speech recognition. Prerequisites for this course include: Energy based models I, Energy based models II, and Introduction to Data Science or a Graduate Level Machine Learning.

Difficulty level: Advanced
Duration: 1:54:22
Speaker: : Yann LeCun

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 CDS, a course that covered the latest techniques in deep learning and representation learning, focusing on supervised and unsupervised deep learning, embedding methods, metric learning, convolutional and recurrent nets, with applications to computer vision, natural language understanding, and speech recognition. Prerequisites for this course include: Energy based models IEnergy based models II, Energy based models III, and Introduction to Data Science or a Graduate Level Machine Learning.

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

This lecture covers advanced concepts of energy based models. The lecture is a part of the Advanced energy based models modules of the the Deep Learning Course at CDS, a course that covered the latest techniques in deep learning and representation learning, focusing on supervised and unsupervised deep learning, embedding methods, metric learning, convolutional and recurrent nets, with applications to computer vision, natural language understanding, and speech recognition. Prerequisites for this course include: Energy based models IEnergy based models II, Energy based models III, and Introduction to Data Science or a Graduate Level Machine Learning.

Difficulty level: Advanced
Duration: 1:54:43
Speaker: : Yann LeCun

This tutorial covers LV-EBM to target prop to (vanilla, denoising, contractive, variational) autoencoder and a part of the Advanced energy based models modules of the the Deep Learning Course at CDS, a course that covered the latest techniques in deep learning and representation learning, focusing on supervised and unsupervised deep learning, embedding methods, metric learning, convolutional and recurrent nets, with applications to computer vision, natural language understanding, and speech recognition. Prerequisites for this course include: Energy based models IEnergy based models IIEnergy based models III, Energy based models IV, and Introduction to Data Science or a Graduate Level Machine Learning.

Difficulty level: Advanced
Duration: 1:00:34
Speaker: : Alfredo Canziani

This lecture covers advanced concepts of energy based models. The lecture is a part of the Advanced energy based models modules of the the Deep Learning Course at CDS, a course that covered the latest techniques in deep learning and representation learning, focusing on supervised and unsupervised deep learning, embedding methods, metric learning, convolutional and recurrent nets, with applications to computer vision, natural language understanding, and speech recognition. Prerequisites for this course include: Energy based models IEnergy based models IIEnergy based models III, Energy based models IV, and Introduction to Data Science or a Graduate Level Machine Learning.

Difficulty level: Advanced
Duration: 2:00:28
Speaker: : Yann LeCun