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2. Multilayer perceptron - Wikipedia

   en.wikipedia.org/wiki/Multilayer_perceptron

   In 1962, Rosenblatt published many variants and experiments on perceptrons in his book Principles of Neurodynamics, including up to 2 trainable layers by "back-propagating errors". [13] However, it was not the backpropagation algorithm, and he did not have a general method for training multiple layers.

3. Recurrent neural network - Wikipedia

   en.wikipedia.org/wiki/Recurrent_neural_network

   An Elman network is a three-layer network (arranged horizontally as x, y, and z in the illustration) with the addition of a set of context units (u in the illustration). The middle (hidden) layer is connected to these context units fixed with a weight of one. [51] At each time step, the input is fed forward and a learning rule is applied. The ...

4. Frank Rosenblatt - Wikipedia

   en.wikipedia.org/wiki/Frank_Rosenblatt

   The third covers multi-layer and cross-coupled perceptrons, and the fourth back-coupled perceptrons and problems for future study. Rosenblatt used the book to teach an interdisciplinary course entitled "Theory of Brain Mechanisms" that drew students from Cornell's Engineering and Liberal Arts colleges.

5. Multilayer perceptrons - Wikipedia

   en.wikipedia.org/?title=Multilayer_perceptrons&...

   Multilayer perceptrons. Add languages. Add links. Article; Talk; English. ... Download QR code; Print/export Download as PDF; Printable version; In other projects

6. Perceptrons (book) - Wikipedia

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

   What the book does prove is that in three-layered feed-forward perceptrons (with a so-called "hidden" or "intermediary" layer), it is not possible to compute some predicates unless at least one of the neurons in the first layer of neurons (the "intermediary" layer) is connected with a non-null weight to each and every input (Theorem 3.1.1 ...

7. Perceptron - Wikipedia

   en.wikipedia.org/wiki/Perceptron

   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.