Introduction to the Brain Imaging Data Structure (BIDS): a standard for organizing human neuroimaging datasets. 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: Intermediate

Duration: 56:49

Speaker: : Chris Gorgolewski

Tutorial on collaborating with Git and GitHub. This tutorial was 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: Intermediate

Duration: 2:15:50

Speaker: : Elizabeth DuPre

Course:

This lecture and tutorial focuses on measuring human functional brain networks. The lecture and tutorial 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: Intermediate

Duration: 50:44

Speaker: : Caterina Gratton

Course:

Next generation science with Jupyter. This lecture was 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: Intermediate

Duration: 50:28

Speaker: : Elizabeth DuPre

Course:

NWB: An ecosystem for neurophysiology data standardization

Difficulty level: Intermediate

Duration: 29:53

Speaker: : Oliver Ruebel

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. Prerequisites for this course include: Introduction to Data Science or a Graduate Level Machine Learning.

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 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: Introduction to Data Science or a Graduate Level Machine Learning.

Difficulty level: Intermediate

Duration: 1:51:03

Speaker: : Yann LeCun

This lecture covers the concept of neural nets--rotation and squashing and is a part of 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: Introduction to Data Science or a Graduate Level Machine Learning.

Difficulty level: Intermediate

Duration: 1:01:53

Speaker: : Alfredo Canziani

This lecture on modules and architectures is part of 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: Introduction to Data Science or a Graduate Level Machine Learning.

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 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: Introduction to Data Science or a Graduate Level Machine Learning.

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 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: Introduction to Deep Learning and Introduction to Data Science or a Graduate Level Machine Learning.

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 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: Introduction to Deep Learning and Introduction to Data Science or a Graduate Level Machine Learning.

Difficulty level: Intermediate

Duration: 51:40

Speaker: : Yann LeCun

This lecture covers the concept of natural signals properties and the convolutional nets in practice and is a part of 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: Introduction to Deep Learning and Introduction to Data Science or a Graduate Level Machine Learning.

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 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: Introduction to Deep Learning and Introduction to Data Science or a Graduate Level Machine Learning.

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 8LV-EBMs) and is a part of 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: Introduction to Deep Learning, Parameter sharing, and Introduction to Data Science or a Graduate Level Machine Learning.

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 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: Introduction to Deep Learning, Parameter sharing, and Introduction to Data Science or a Graduate Level Machine Learning.

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 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: Introduction to Deep Learning, Parameter sharing, and Introduction to Data Science or a Graduate Level Machine Learning.

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 8LV-EBMs) and is a part of 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: Introduction to Deep Learning, Parameter sharing, and Introduction to Data Science or a Graduate Level Machine Learning.

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 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: Introduction to Deep Learning, Parameter sharing, and Introduction to Data Science or a Graduate Level Machine Learning.

Difficulty level: Intermediate

Duration: 1:04:48

Speaker: : Alfredo Canziani

Course:

DAQCORD is a framework for the design, documentation and reporting of data curation methods in order to advance the scientific rigour, reproducibility and analysis of the data. This lecture covers the rationale for developing the framework, the process in which the framework was developed, and ends with a presentation of the framework. While the driving use case for DAQCORD was clinical traumatic brain injury research, the framework is applicable to clinical studies in other domains of clinical neuroscience research.

Difficulty level: Intermediate

Duration: 17:08

Speaker: : Ari Ercole

- Clinical neuroinformatics (3)
- Programming Languages (1)
- Brain computer interface (1)
- (-) Deep learning (14)
- (-) Standards and Best Practices (4)
- Electroencephalography (EEG) (1)
- Developmental neuroscience (3)
- Image processing (1)
- Reinforcement learning (1)
- (-) Connectivity (1)
- Data management (1)
- Digital brain atlasing (4)
- Data sharing (1)
- Neuroimaging (7)
- Brain networks (1)
- Machine learning (2)
- Data curation (1)
- Data standard (1)
- Image registration (1)
- Image segmentation (1)
- Epilepsy (1)
- Data governance (2)
- Neural coding (16)
- Neuromorphic engineering (1)
- Electrophysiology (17)
- Magnetoencephalography (MEG) (1)
- Simulation (1)
- Cloud computing (1)
- High performance computing (1)
- Neuroanatomy (4)
- Standards and best practices (11)
- Psychology (1)
- Tools (11)
- Neuronal plasticity (15)
- (-) Clinical neuroscience (1)
- Computational neuroscience (66)
- (-) Computer Science (2)
- Data science (3)
- (-) Open science (2)
- Education (1)
- Neuroethics (2)