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Networked learning. Networked learning is a process of developing and maintaining connections with people and information, and communicating in such a way so as to support one another's learning. The central term in this definition is connections. It adopts a relational stance in which learning takes place both in relation to others and in ...
A neural network is a group of interconnected units called neurons that send signals to one another. Neurons can be either biological cells or mathematical models. While individual neurons are simple, many of them together in a network can perform complex tasks. There are two main types of neural network. In neuroscience, a biological neural ...
v. t. e. In machine learning, a neural network (also artificial neural network or neural net, abbreviated ANN or NN) is a model inspired by the structure and function of biological neural networks in animal brains. [1] [2] An ANN consists of connected units or nodes called artificial neurons, which loosely model the neurons in a brain.
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Since the kernel output is the same length as width, its area is 55×55.) AlexNet is the name of a convolutional neural network (CNN) architecture, designed by Alex Krizhevsky in collaboration with Ilya Sutskever and Geoffrey Hinton, who was Krizhevsky's Ph.D. advisor at the University of Toronto. [1] [2]
U-Net is a convolutional neural network that was developed for biomedical image segmentation at the Computer Science Department of the University of Freiburg. The network is based on a fully convolutional neural network [2] whose architecture was modified and extended to work with fewer training images and to yield more precise segmentation .
Pacific Open Learning Health Net (POLHN / ˈ p ɒ l h ən /) is a non-profit established in 2003, focused on distance education for health professionals working in the Pacific. Since 2004, it has provided free online courses for continuing professional development in a wide range of health and science related disciplines.
In the more general subject of "geometric deep learning", certain existing neural network architectures can be interpreted as GNNs operating on suitably defined graphs. A convolutional neural network layer, in the context of computer vision , can be seen as a GNN applied to graphs whose nodes are pixels and only adjacent pixels are connected by ...