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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 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 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 lecture covers advanced concepts of energy based models. The lecture is a part of the Associative memories 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: 2:00:28
Speaker: : Yann LeCun

This lecture provides an introduction to the problem of speech recognition using neural models, emphasizing the CTC loss for training and inference when input and output sequences are of different lengths. It also covers the concept of beam search for use during inference, and how that procedure may be modeled at training time using a Graph Transformer Network. It 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 module include: Modules 1 - 5 of this course and Introduction to Data Science or a Graduate Level Machine Learning.

Difficulty level: Advanced
Duration: 1:55:03
Speaker: : Awni Hannun

This lecture covers the concepts of the architecture and convolution of traditional convolutional neural networks, the characteristics of graph and graph convolution, and spectral graph convolutional neural networks and how to perform spectral convolution, as well as the complete spectrum of Graph Convolutional Networks (GCNs), starting with the implementation of Spectral Convolution through Spectral Networks. It then provides insights on applicability of the other convolutional definition of Template Matching to graphs, leading to Spatial networks. This lecture 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 module include: Modules 1 - 5 of this course and Introduction to Data Science or a Graduate Level Machine Learning.

Difficulty level: Advanced
Duration: 2:00:22
Speaker: : Xavier Bresson

This lecture covers the concepts of gradient descent, stochastic gradient descent, and momentum. It 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 module include: Models 1-7 of this course and Introduction to Data Science or a Graduate Level Machine Learning.

Difficulty level: Advanced
Duration: 1:29:05
Speaker: : Aaron DeFazio

This module explains how neurons come together to create the networks that give rise to our thoughts. The totality of our neurons and their connection is called our connectome. Learn how this connectome changes as we learn, and computes information. We will also learn about physiological phenomena of the brain such as synchronicity that gives rise to brain waves.

Difficulty level: Beginner
Duration: 7:13
Speaker: : Harrison Canning

This lecture covers an Introduction to neuron anatomy and signaling, and different types of models, including the Hodgkin-Huxley model.

Difficulty level: Beginner
Duration: 1:23:01
Speaker: : Gaute Einevoll

Computational models provide a framework for integrating data across spatial scales and for exploring hypotheses about the biological mechanisms underlying neuronal and network dynamics. However, as models increase in complexity, additional barriers emerge to the creation, exchange, and re-use of models. Successful projects have created standards for describing complex models in neuroscience and provide open source tools to address these issues. This lecture provides an overview of these projects and make a case for expanded use of resources in support of reproducibility and validation of models against experimental data.

Difficulty level: Beginner
Duration: 1:00:39
Speaker: : Sharon Crook

Introduction to reproducible research. The lecture provides an overview of the core skills and practical solutions required to practice reproducible research. 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: Beginner
Duration: 1:25:17
Speaker: : Fernando Perez