Lesson type
Difficulty level
This lesson provides a quick tour of some data repositories and how to download and manipulate data from them.
Difficulty level: Beginner
Duration: 00:49:06
Speaker: : Sebastian Urchs
Course:
KnowledgeSpace (KS) is a data discoverability portal and neuroscience encyclopedia that was developed to make it easier for the neuroscience community to find publicly available datasets that adhere to the FAIR Principles and to provide an integrated view of neuroscience concepts found in Wikipedia and NeuroLex linked with PubMed and 17 of the world's leading neuroscience repositories. In short, KS provides a single point of entry where reseaerchers can search for a neuroscience concept of interest and receive results that include: i. a description of the term found in Wikipedia/NeuroLex, ii. links to publicly available datasets related to the concept of interest, and iii. up-to-date references that support the concept of interests found in PubMed. APIs are available so that developers of other neuroscience research infrastructures can integrate KS components in their infrastructures. If your repository or your favorite repository is not indexed in KS, please contact us.
Difficulty level: Beginner
Duration: 6:14
Speaker: : Heather Topple
In this lesson, attendees will learn about the data structure standards, specifically the Brain Imaging Data Structure (BIDS), an INCF-endorsed standard for organizing, annotating, and describing data collected during neuroimaging experiments.
Difficulty level: Beginner
Duration: 21:56
Speaker: : Michael Schirner
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
Speaker: : Yann LeCun and Alfredo Canziani
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 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 lesson provides a detailed description of some of the modules and architectures involved in the development of neural networks.
Difficulty level: Intermediate
Duration: 1:42:26
Speaker: : Yann LeCun and 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 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
Speaker: : Yann LeCun and Alfredo Canziani
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 panel discussion covers how energy based models are used and is a part of the Deep Learning Course at NYU's Center for Data Science.
Difficulty level: Intermediate
Duration: 10:42
Speaker: : Yann LeCun and 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
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 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 I, Energy-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
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.
Difficulty level: Advanced
Duration: 1:54:43
Speaker: : Yann LeCun