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- W2892355103 abstract "I describe an Artificial Neural Network, where we have mapped words to individual neurons instead of having them as variables to be fed into a network. The process of changing training cases will be equivalent to a Dropout procedure where we replace some (or all) of the words/neurons in the previous training case with new ones. Each neuron/word then takes in as input, all the b weights of the other neurons, and weights them all with its personal a weight. To learn this network uses the backpropagation algorithm after calculating an error from the output of an output neuron that will be a traditional neuron. This network then has a unique topology and functions with no inputs. We will use coordinate gradient decent to learn where we alternate between training the a weights of the words and the b weights. The Idealistic Neural Network, is an extremely shallow network that can represent non-linearity complexity in a linear outfit." @default.
- W2892355103 created "2018-09-27" @default.
- W2892355103 creator A5015522680 @default.
- W2892355103 date "2018-09-01" @default.
- W2892355103 modified "2023-09-26" @default.
- W2892355103 title "Idealistic Neural Networks" @default.
- W2892355103 hasPublicationYear "2018" @default.
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