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This is the first of two workshops on reproducibility in science, during which participants are introduced to concepts of FAIR and open science. After discussing the definition of and need for FAIR science, participants are walked through tutorials on installing and using Github and Docker, the powerful, open-source tools for versioning and publishing code and software, respectively.

Difficulty level: Intermediate
Duration: 1:20:58

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.

Difficulty level: Intermediate
Duration: 1:11:04
Speaker: : Etay Hay

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. 

Difficulty level: Intermediate
Duration: 1:47:22

This lesson goes over the basic mechanisms of neural synapses, the space between neurons where signals may be transmitted. 

Difficulty level: Intermediate
Duration: 7:03
Speaker: : Marcus Ghosh

This lesson describes spike timing-dependent plasticity (STDP), a biological process that adjusts the strength of connections between neurons in the brain, and how one can implement or mimic this process in a computational model. You will also find links for practical exercises at the bottom of this page. 

Difficulty level: Intermediate
Duration: 12:50
Speaker: : Dan Goodman

This lesson explores how researchers try to understand neural networks, particularly in the case of observing neural activity. 

Difficulty level: Intermediate
Duration: 8:20
Speaker: : Marcus Ghosh

This lesson discusses a gripping neuroscientific question: why have neurons developed the discrete action potential, or spike, as a principle method of communication? 

Difficulty level: Intermediate
Duration: 9:34
Speaker: : Dan Goodman

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

Difficulty level: Intermediate
Duration: 56:49

This tutorial covers the fundamentals of collaborating with Git and GitHub.

Difficulty level: Intermediate
Duration: 2:15:50
Speaker: : Elizabeth DuPre

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.

Difficulty level: Intermediate
Duration: 50:17

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.

Difficulty level: Intermediate
Duration: 1:51:03
Speaker: : Yann LeCun

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.

Difficulty level: Intermediate
Duration: 1:59:47

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.

Difficulty level: Intermediate
Duration: 51:40
Speaker: : Yann LeCun

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 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.

Difficulty level: Intermediate
Duration: 1:51:30
Speaker: : Yann LeCun

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 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.

Difficulty level: Intermediate
Duration: 1:48:53
Speaker: : Yann LeCun

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