This lecture provides an introduction to the Brain Imaging Data Structure (BIDS), a standard for organizing human neuroimaging datasets.

This is a continuation of the talk on the cellular mechanisms of neuronal communication, this time at the level of brain microcircuits and associated global signals like those measureable by electroencephalography (EEG). This lecture also discusses EEG biomarkers in mental health disorders, and how those cortical signatures may be simulated digitally.

This lesson describes the principles underlying functional magnetic resonance imaging (fMRI), diffusion-weighted imaging (DWI), tractography, and parcellation. These tools and concepts are explained in a broader context of neural connectivity and mental health. 

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.

This is the Introductory Module to 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.

This module covers the concepts of gradient descent and the backpropagation algorithm and is a part of the Deep Learning Course at NYU's Center for Data Science.

This lecture covers the concept of parameter sharing: recurrent and convolutional nets and is a part of the Deep Learning Course at NYU's Center for Data Science.

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

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.

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.

This lecture is a foundationational lecture for the concept of energy-based models with a particular focus on the joint embedding method and latent variable energy-based models (LV-EBMs) and is a part of the Deep Learning Course at NYU's Center for Data Science.

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. 

This lecture is a foundationational lecture for the concept of energy-based models with a particular focus on the joint embedding method and latent variable energy based models (LV-EBMs) and is a part of the Deep Learning Course at NYU's Center for Data Science.

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.

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.

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 I, Energy-Based Models II, Energy-Based Models III, and an Introduction to Data Science or a Graduate Level Machine Learning course.

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 I, Energy-Based Models II, Energy-Based Models III, Energy-Based Models IV, and an Introduction to Data Science or a Graduate Level Machine Learning course.

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 I, Energy-Based Models II, Energy-Based Models III, Energy-Based Models IV, and an Introduction to Data Science or a Graduate Level Machine Learning course.

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 I, Energy-Based Models II, Energy-Based Models III, Energy-Based Models IV, Energy-Based Models V, and an Introduction to Data Science or a Graduate Level Machine Learning course.

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 I, Energy-Based Models II, Energy-Based Models III, Energy-Based Models IV, Energy-Based Models V, and an Introduction to Data Science or a Graduate Level Machine Learning course.