Tutorial on how to use TVB-NEST toolbox on your local computer. Authors: D. Perdikis, L. Domide, M. Schirner, P. Ritter

Tutorial on how to perform multi-scale simulation of Alzheimer's disease on The Virtual Brain Simulation Platform. Authors: L. Stefanovski, P. Triebkorn, M.A. Diaz-Cortes, A. Solodkin, V. Jirsa, A.R. McIntosh, P. Ritter

A brief overview of the Python programming language, with an emphasis on tools relevant to data scientists. 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.

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

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 I, Energy based models II, Energy based models III, Energy based models IV, Energy based models V, and Introduction to Data Science or a Graduate Level Machine Learning.

This tutorial covers advanced concept 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. 

This tutuorial covers the concept of Graph convolutional networks 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 module include: Modules 1 - 5 of this course and Introduction to Data Science or a Graduate Level Machine Learning.

This lecture covers the concepts of emulation of kinematics from observations and training a policy. 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 1-6 of this course and Introduction to Data Science or a Graduate Level Machine Learning.

Jake Vogel gives a hands-on, Jupyter-notebook-based tutorial to apply machine learning in Python to brain-imaging data.

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

This lesson from freeCodeCamp introduces Scikit-learn, the most widely used machine learning Python library.

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.

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.