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This tutorial walks participants through the application of dynamic causal modelling (DCM) to fMRI data using MATLAB. Participants are also shown various forms of DCM, how to generate and specify different models, and how to fit them to simulated neural and BOLD data.

 

This lesson corresponds to slides 158-187 of the PDF below. 

Difficulty level: Advanced
Duration: 1:22:10

This lecture provides a history of data management, recent developments data management, and a brief description of scientific data management.

Difficulty level: Advanced
Duration: 35:10
Speaker: : Thomas Heinis

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

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

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

This tutorial covers LV-EBM to target prop to (vanilla, denoising, contractive, variational) autoencoder and is a part of the Advanced Energy-Based Models module of the the Deep Learning Course at NYU's Center for Data Science. Prerequisites for this course include: Energy-Based Models IEnergy-Based Models IIEnergy-Based Models III, Energy-Based Models IV, and an Introduction to Data Science or a Graduate Level Machine Learning course.

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 NYU's Center for Data Science. Prerequisites for this course include: Energy-Based Models IEnergy-Based Models IIEnergy-Based Models III, Energy-Based Models IV, and an Introduction to Data Science or a Graduate Level Machine Learning course.

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

This tutorial covers the concepts of autoencoders, denoising encoders, and variational autoencoders (VAE) with PyTorch, as well as generative adversarial networks and code. It 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 IIEnergy-Based Models IIIEnergy-Based Models IV, Energy-Based Models V, and an Introduction to Data Science or a Graduate Level Machine Learning course.

Difficulty level: Advanced
Duration: 1:07:50
Speaker: : Alfredo Canziani

This lecture covers advanced concepts of energy-based models. The lecture is a part of the Associative Memories module of the the Deep Learning Course at NYU's Center for Data Science. Prerequisites for this course include: Energy-Based Models IEnergy-Based Models IIEnergy-Based Models IIIEnergy-Based Models IV, Energy-Based Models V, and an Introduction to Data Science or a Graduate Level Machine Learning course.

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

This tutorial covers advanced concept of energy-based models. The lecture is a part of the Associative Memories module of the the Deep Learning Course at NYU's Center for Data Science. 

Difficulty level: Advanced
Duration: 1:12:00
Speaker: : Alfredo Canziani

This lecture provides an introduction to the problem of speech recognition using neural models, emphasizing the CTC loss for training and inference when input and output sequences are of different lengths. It also covers the concept of beam search for use during inference, and how that procedure may be modeled at training time using a Graph Transformer Network. It is a part of the Deep Learning Course at NYU's Center for Data Science. Prerequisites for this module include: Modules 1 - 5 of this course and an Introduction to Data Science or a Graduate Level Machine Learning course.

Difficulty level: Advanced
Duration: 1:55:03
Speaker: : Awni Hannun

This lecture covers the concepts of the architecture and convolution of traditional convolutional neural networks, the characteristics of graph and graph convolution, and spectral graph convolutional neural networks and how to perform spectral convolution, as well as the complete spectrum of Graph Convolutional Networks (GCNs), starting with the implementation of Spectral Convolution through Spectral Networks. It then provides insights on applicability of the other convolutional definition of Template Matching to graphs, leading to Spatial networks. This lecture is a part of the Deep Learning Course at NYU's Center for Data Science. Prerequisites for this module include: Modules 1 - 5 of this course and an Introduction to Data Science or a Graduate Level Machine Learning course.

Difficulty level: Advanced
Duration: 2:00:22
Speaker: : Xavier Bresson

This tutuorial covers the concept of graph convolutional networks and is a part of the Deep Learning Course at NYU's Center for Data Science. Prerequisites for this module include: Modules 1 - 5 of this course and an Introduction to Data Science or a Graduate Level Machine Learning course.

Difficulty level: Advanced
Duration: 57:33
Speaker: : Alfredo Canziani

This lecture covers the concept of model predictive control and is a part of the Deep Learning Course at NYU's Center for Data Science. Prerequisites for this module include: Models 1-6 of this course and an Introduction to Data Science or a Graduate Level Machine Learning course.

Difficulty level: Advanced
Duration: 1:10:22
Speaker: : Alfredo Canziani

This lecture covers the concepts of emulation of kinematics from observations and training a policy. It is a part of the Deep Learning Course at NYU's Center for Data Science. Prerequisites for this module include: Models 1-6 of this course and an Introduction to Data Science or a Graduate Level Machine Learning course.

Difficulty level: Advanced
Duration: 1:01:21
Speaker: : Alfredo Canziani

This lecture covers the concept of predictive policy learning under uncertainty and is a part of the Deep Learning Course at NYU's Center for Data Science. Prerequisites for this module include: Models 1-6 of this course and an Introduction to Data Science or a Graduate Level Machine Learning course.

Difficulty level: Advanced
Duration: 1:14:44
Speaker: : Alfredo Canziani

This lecture covers the concepts of gradient descent, stochastic gradient descent, and momentum. It is a part of the Deep Learning Course at NYU's Center for Data Science. Prerequisites for this module include: Models 1-7 of this course and an Introduction to Data Science or a Graduate Level Machine Learning course.

Difficulty level: Advanced
Duration: 1:29:05
Speaker: : Aaron DeFazio

This lecture continues on the topic of descent from the previous lesson, Optimization I. This lesson is a part of the Deep Learning Course at NYU's Center for Data Science. Prerequisites for this module include: Models 1-7 of this course and an Introduction to Data Science or a Graduate Level Machine Learning course.

Difficulty level: Advanced
Duration: 1:51:32
Speaker: : Alfredo Canziani