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  2. Perceptron - Wikipedia

    en.wikipedia.org/wiki/Perceptron

    The perceptron algorithm is also termed the single-layer perceptron, to distinguish it from a multilayer perceptron, which is a misnomer for a more complicated neural network. As a linear classifier, the single-layer perceptron is the simplest feedforward neural network .

  3. Mark I Perceptron - Wikipedia

    en.wikipedia.org/wiki/Mark_I_Perceptron

    The Mark I Perceptron was a pioneering supervised image classification learning system developed by Frank Rosenblatt in 1958. It was the first implementation of an Artificial Intelligence (AI) machine.

  4. Perceptrons (book) - Wikipedia

    en.wikipedia.org/wiki/Perceptrons_(book)

    The perceptron is a neural net developed by psychologist Frank Rosenblatt in 1958 and is one of the most famous machines of its period. [11] [12] In 1960, Rosenblatt and colleagues were able to show that the perceptron could in finitely many training cycles learn any task that its parameters could embody.

  5. Multilayer perceptron - Wikipedia

    en.wikipedia.org/wiki/Multilayer_perceptron

    In deep learning, a multilayer perceptron (MLP) is a name for a modern feedforward neural network consisting of fully connected neurons with nonlinear activation functions, organized in layers, notable for being able to distinguish data that is not linearly separable.

  6. Types of artificial neural networks - Wikipedia

    en.wikipedia.org/wiki/Types_of_artificial_neural...

    In feedforward neural networks the information moves from the input to output directly in every layer. There can be hidden layers with or without cycles/loops to sequence inputs. Feedforward networks can be constructed with various types of units, such as binary McCulloch–Pitts neurons, the simplest of which is the perceptron.

  7. Delta rule - Wikipedia

    en.wikipedia.org/wiki/Delta_rule

    While the delta rule is similar to the perceptron's update rule, the derivation is different. The perceptron uses the Heaviside step function as the activation function g ( h ) {\displaystyle g(h)} , and that means that g ′ ( h ) {\displaystyle g'(h)} does not exist at zero, and is equal to zero elsewhere, which makes the direct application ...

  8. Kernel perceptron - Wikipedia

    en.wikipedia.org/wiki/Kernel_perceptron

    In machine learning, the kernel perceptron is a variant of the popular perceptron learning algorithm that can learn kernel machines, i.e. non-linear classifiers that employ a kernel function to compute the similarity of unseen samples to training samples.

  9. Frank Rosenblatt - Wikipedia

    en.wikipedia.org/wiki/Frank_Rosenblatt

    He received international recognition for the Perceptron. The New York Times billed it as a revolution, with the headline "New Navy Device Learns By Doing", [9] and The New Yorker similarly admired the technological advancement. [7] An elementary Rosenblatt's perceptron. A-units are linear threshold element with fixed input weights.