This lecture introduces you to the basics of the Amazon Web Services public cloud. It covers the fundamentals of cloud computing and goes through both the motivations and processes involved in moving your research computing to the cloud.
This lesson continues from part one of the lecture Ontologies, Databases, and Standards, diving deeper into a description of ontologies and knowledg graphs.
This lecture describes how to build research workflows, including a demonstrate using DataJoint Elements to build data pipelines.
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.
This module covers the concepts of gradient descent and the backpropagation algorithm and is a part of the Deep Learning Course at NYU's Center for Data Science.
This lesson provides a detailed description of some of the modules and architectures involved in the development of neural networks.
This lecture covers the concept of parameter sharing: recurrent and convolutional nets and is a part of the Deep Learning Course at NYU's Center for Data Science.
This lecture covers the concept of convolutional nets in practice and is a part of the Deep Learning Course at NYU's Center for Data Science.
This lecture discusses the concept of natural signals properties and the convolutional nets in practice and is a part of the Deep Learning Course at NYU's Center for Data Science.
This lecture covers the concept of recurrent neural networks: vanilla and gated (LSTM) and is a part of the Deep Learning Course at NYU's Center for Data Science.
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 (LV-EBMs) and is a part of the Deep Learning Course at NYU's Center for Data Science.
This lecture covers the concept of inference in latent variable energy based models (LV-EBMs) and is a part of the Deep Learning Course at NYU's Center for Data Science.
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 (LV-EBMs) and is a part of the Deep Learning Course at NYU's Center for Data Science.
This tutorial covers the concept of training latent variable energy based models (LV-EBMs) and is is a part of the Deep Learning Course at NYU's Center for Data Science.
This lesson breaks down the principles of Bayesian inference and how it relates to cognitive processes and functions like learning and perception. It is then explained how cognitive models can be built using Bayesian statistics in order to investigate how our brains interface with their environment.
This lesson corresponds to slides 1-64 in the PDF below.
This talk gives an overview of the Human Brain Project, a 10-year endeavour putting in place a cutting-edge research infrastructure that will allow scientific and industrial researchers to advance our knowledge in the fields of neuroscience, computing, and brain-related medicine.
This lecture gives an introduction to the European Academy of Neurology, its recent achievements and ambitions.
In this lecture, you will learn about current methods, approaches, and challenges to studying human neuroanatomy, particularly through the lense of neuroimaging data such as fMRI and diffusion tensor imaging (DTI).
This lesson delves into the human nervous system and the immense cellular, connectomic, and functional sophistication therein.