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e. Extreme learning machines are feedforward neural networks for classification, regression, clustering, sparse approximation, compression and feature learning with a single layer or multiple layers of hidden nodes, where the parameters of hidden nodes (not just the weights connecting inputs to hidden nodes) need to be tuned.
Deep learning is the subset of machine learning methods based on neural networks with representation learning. The adjective "deep" refers to the use of multiple layers in the network. Methods used can be either supervised, semi-supervised or unsupervised. [2]
Max Welling ( postdoc) Zoubin Ghahramani ( postdoc) Alex Graves ( postdoc) Website. www .cs .toronto .edu /~hinton /. Geoffrey Everest Hinton CC FRS FRSC [12] (born 6 December 1947) is a British-Canadian computer scientist and cognitive psychologist, most noted for his work on artificial neural networks.
First Neural Network Machine: Marvin Minsky and Dean Edmonds build the first neural network machine, able to learn, the SNARC. 1952: Machines Playing Checkers: Arthur Samuel joins IBM's Poughkeepsie Laboratory and begins working on some of the first machine learning programs, first creating programs that play checkers. 1957: Discovery: Perceptron
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 ...
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Diagram of the feature learning paradigm in machine learning for application to downstream tasks, which can be applied to either raw data such as images or text, or to an initial set of features for the data. Feature learning is intended to result in faster training or better performance in task-specific settings than if the data was inputted ...
Generalized Hebbian algorithm. The generalized Hebbian algorithm ( GHA ), also known in the literature as Sanger's rule, is a linear feedforward neural network for unsupervised learning with applications primarily in principal components analysis. First defined in 1989, [1] it is similar to Oja's rule in its formulation and stability, except it ...
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