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.

In this presentation by the OHBM OpenScienceSIG, Tom Shaw and Steffen Bollmann cover how containers can be useful for running the same software on different platforms and sharing analysis pipelines with other researchers. They demonstrate how to build docker containers from scratch, using Neurodocker, and cover how to use containers on an HPC with singularity.

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.

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.

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.

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.

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.

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.

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.

Blake Richards gives an introduction to deep learning, with a perspective via inductive biases and emphasis on correctly matching deep learning to the right research questions.

The lesson was presented in the context of the BrainHack School 2020.

Serving as good refresher, Shawn Grooms explains the maths and logic concepts that are important for programmers to understand, including sets, propositional logic, conditional statements, and more.

This compilation is courtesy of freeCodeCamp.

Linear algebra is the branch of mathematics concerning linear equations such as linear functions and their representations through matrices and vector spaces. As such, it underlies a huge variety of analyses in the neurosciences.  This lesson provides a useful refresher which will facilitate the use of Matlab, Octave, and various matrix-manipulation and machine-learning software.

This lesson was created by RootMath.

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

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.

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.

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.

Brought to you by the Canadian Association of Research Libraries.

Keeping data and research materials organized across all phases of the research process is always a challenging process. To help the research community address these challenges, the Center for Open Science developed the Open Science Framework (OSF), a research tool that supports collaboration, data management, and transparency throughout the research lifecycle. The OSF provides avenues for researchers to design a study; collect, analyze, and store data; manage collaborators; and publish research materials. In this webinar, attendees will learn about the many features of the OSF and develop strategies for using the tool within the context of their own research projects. The discussion will be framed around how to best utilize the OSF while also implementing data management and open science best practices.

Speakers Kevin Read, MLIS, MAS is a health sciences librarian at the University of Saskatchewan. He has been providing data services in health sciences libraries for the past 8 years in both Canada and the U.S. He is the current Chair of the Portage Network’s Data Discovery Expert Group, and is in the process of conducting research on how Canadian-funded researchers describe and share their data.