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Explore how to setup an epileptic seizure simulation with the TVB graphical user interface. This lesson will show you how to program the epileptor model in the brain network to simulate a epileptic seizure originating in the hippocampus. It will also show how to upload and view mouse connectivity data, as well as give a short introduction to the python script interface of TVB.

Difficulty level: Intermediate
Duration: 58:06
Speaker: : Paul Triebkorn

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

Lecture on functional brain parcellations and a set of tutorials on bootstrap agregation of stable clusters (BASC) for fMRI brain parcellation which 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: Advanced
Duration: 50:28
Speaker: : Pierre Bellec

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

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 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

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 LearningParameter sharing, and Introduction to Data Science or a Graduate Level Machine Learning.

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 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 LearningParameter 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 LearningParameter sharing, and Introduction to Data Science or a Graduate Level Machine Learning.

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 modules of the 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: Energy based models I, Energy based models II, and Introduction to Data Science or a Graduate Level Machine Learning.

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 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: Energy based models IEnergy based models II, Energy based models III, and Introduction to Data Science or a Graduate Level Machine Learning.

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 a part of the Advanced energy based models modules of the 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: Energy based models IEnergy based models IIEnergy based models III, Energy based models IV, and Introduction to Data Science or a Graduate Level Machine Learning.

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 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: Energy based models IEnergy based models IIEnergy based models III, Energy based models IV, and Introduction to Data Science or a Graduate Level Machine Learning.

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 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: Energy based models IEnergy based models IIEnergy based models IIIEnergy based models IV, Energy based models V, and Introduction to Data Science or a Graduate Level Machine Learning.

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