This lesson provides an introduction the International Neuroinformatics Coordinating Facility (INCF), its mission towards FAIR neuroscience, and future directions. 

This talk describes the NIH-funded SPARC Data Structure, and how this project navigates ontology development while keeping in mind the FAIR science principles. 

This lesson consists of a brief discussion around this sessions previous talks. 

This is the third and final lecture of this course on neuroinformatics infrastructure for handling sensitive data. 

In this lecture, you will learn about virtual research environments (VREs) and their technical limitations, (i.e., a computing platform and the software stack behind it) and the security measures which should be considered during implementation. 

This lesson consists of a panel discussion, wrapping up the INCF Neuroinformatics Assembly 2023 workshop Research Workflows for Collaborative Neuroscience. 

This brief talk outlines the obstacles and opportunities involved in striving for more open and reproducible publishing, highlighting the need for investment in the technical and governance sectors of FAIR data and software. 

This brief video provides a welcome and short introduction to the outline of the INCF Short Course in Neuroinformatics, held Seattle, Washington in October 2023, in coordination with the West Big Data Hub and the University of Washington. 

This opening lecture from INCF's Short Course in Neuroinformatics provides an overview of the field of neuroinformatics itself, as well as laying out an argument for the necessity for developing more sophisticated approaches towards FAIR data management principles in neuroscience. 

This lesson aims to define computational neuroscience in general terms, while providing specific examples of highly successful computational neuroscience projects. 

This lecture covers a wide range of aspects regarding neuroinformatics and data governance, describing both their historical developments and current trajectories. Particular tools, platforms, and standards to make your research more FAIR are also discussed.

This lesson gives an in-depth description of scientific workflows, from study inception and planning to dissemination of results. 

Introduction of the Foundations of Machine Learning in Python course - Day 01.

High-Performance Computing and Analytics Lab, University of Bonn

This lecture gives an introduction to the INCF Short Course: Introduction to Neuroinformatics. 

Presented by the OHBM OpenScienceSIG, this lesson covers how containers can be useful for running the same software on different platforms and sharing analysis pipelines with other researchers.

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.

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

As a part of NeuroHackademy 2021, Noah Benson gives an introduction to Pytorch, one of the two most common software packages for deep learning applications to the neurosciences.

Learn how to use TensorFlow 2.0 in this full tutorial for beginners. This course is designed for Python programmers looking to enhance their knowledge and skills in machine learning and artificial intelligence.

Throughout the 8 modules in this course you will learn about fundamental concepts and methods in ML & AI like core learning algorithms, deep learning with neural networks, computer vision with convolutional neural networks, natural language processing with recurrent neural networks, and reinforcement learning.

In this hands-on tutorial, Dr. Robert Guangyu Yang works through a number of coding exercises to see how RNNs can be easily used to study cognitive neuroscience questions, with a quick demonstration of how we can train and analyze RNNs on various cognitive neuroscience tasks. Familiarity of Python and basic knowledge of Pytorch are assumed.