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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.
For a single-layer perceptron with multiple output units, since the weights of one output unit are completely separate from all the others', the same algorithm can be run for each output unit. For multilayer perceptrons , where a hidden layer exists, more sophisticated algorithms such as backpropagation must be used.
The Mark I Perceptron achieved 99.8% accuracy on a test dataset with 500 neurons in a single layer. The size of the training dataset was 10,000 example images. It took 3 seconds for the training pipeline to go through a single image.
Download QR code; Print/export ... It can be derived as the backpropagation algorithm for a single-layer neural network with ... The perceptron uses the Heaviside ...
Keras is an open-source library that provides a Python interface for artificial neural networks. Keras was first independent software, then integrated into the TensorFlow library, and later supporting more. "Keras 3 is a full rewrite of Keras [and can be used] as a low-level cross-framework language to develop custom components such as layers ...
R. D. Joseph (1960) [18] mentions an even earlier perceptron-like device: [13] "Farley and Clark of MIT Lincoln Laboratory actually preceded Rosenblatt in the development of a perceptron-like device." However, "they dropped the subject." In 1960, Joseph [18] also discussed multilayer perceptrons with an adaptive hidden layer.
The biologically inspired Hodgkin–Huxley model of a spiking neuron was proposed in 1952. This model describes how action potentials are initiated and propagated. . Communication between neurons, which requires the exchange of chemical neurotransmitters in the synaptic gap, is described in various models, such as the integrate-and-fire model, FitzHugh–Nagumo model (1961–1962), and ...
In particular, this shows that a perceptron network with a single infinitely wide hidden layer can approximate arbitrary functions. Such an f {\displaystyle f} can also be approximated by a network of greater depth by using the same construction for the first layer and approximating the identity function with later layers.