This lecture covers different perspectives on the study of the mental, focusing on the difference between Mind and Brain.
This lesson provides a brief overview of the Python programming language, with an emphasis on tools relevant to data scientists.
This lesson provides a comprehensive introduction to the command line and 50 popular Linux commands. This is a long introduction (nearly 5 hours), but well worth it if you are going to spend a good part of your career working from a terminal, which is likely if you are interested in flexibility, power, and reproducibility in neuroscience research. This lesson is courtesy of freeCodeCamp.
This lesson gives an introduction to high-performance computing with the Compute Canada network, first providing an overview of use cases for HPC and then a hands-on tutorial. Though some examples might seem specific to the Calcul Québec, all computing clusters in the Compute Canada network share the same software modules and environments.
This lecture covers FAIR atlases, including their background and construction, as well as how they can be created in line with the FAIR principles.
This lesson provides a tutorial on how to handle writing very large data in MatNWB.
This lecture provides reviews some standards for project management and organization, including motivation from the view of the FAIR principles and improved reproducibility.
This lecture covers advanced concepts of energy-based models. The lecture is a part of the Advanced Energy-Based Models module 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, and an Introduction to Data Science or a Graduate Level Machine Learning course.
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 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 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 tutorial covers LV-EBM to target prop to (vanilla, denoising, contractive, variational) autoencoder and is a part of the Advanced Energy-Based Models module 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, Energy-Based Models IV, and an Introduction to Data Science or a Graduate Level Machine Learning course.
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 NYU's Center for Data Science. Prerequisites for this course include: Energy-Based Models I, Energy-Based Models II, Energy-Based Models III, Energy-Based Models IV, and an Introduction to Data Science or a Graduate Level Machine Learning course.
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 NYU's Center for Data Science. 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 an Introduction to Data Science or a Graduate Level Machine Learning course.
This lecture covers advanced concepts of energy-based models. The lecture is a part of the Associative Memories module 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, Energy-Based Models IV, Energy-Based Models V, and an Introduction to Data Science or a Graduate Level Machine Learning course.
This tutorial covers advanced concept of energy-based models. The lecture is a part of the Associative Memories module of the the Deep Learning Course at NYU's Center for Data Science.
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 NYU's Center for Data Science. Prerequisites for this module include: Modules 1 - 5 of this course and an Introduction to Data Science or a Graduate Level Machine Learning course.
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 NYU's Center for Data Science. Prerequisites for this module include: Modules 1 - 5 of this course and an Introduction to Data Science or a Graduate Level Machine Learning course.
This tutuorial covers the concept of graph convolutional networks and is a part of the Deep Learning Course at NYU's Center for Data Science. Prerequisites for this module include: Modules 1 - 5 of this course and an Introduction to Data Science or a Graduate Level Machine Learning course.
This lecture covers the concept of model predictive control and is a part of the Deep Learning Course at NYU's Center for Data Science. Prerequisites for this module include: Models 1-6 of this course and an Introduction to Data Science or a Graduate Level Machine Learning course.
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 NYU's Center for Data Science. Prerequisites for this module include: Models 1-6 of this course and an Introduction to Data Science or a Graduate Level Machine Learning course.