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https://doi.org/10.1142/9789812796851_0003Cited by:0 (Source: Crossref)
Abstract:

The following sections are included:

  • The Basic Model of the Neuron

  • Activation Functions

  • Topologies

  • Learning

    • A Basic Supervised Learning Algorithm

    • A Basic Unsupervised Learning Algorithm

  • The Basic McCulloch Pitts and Perceptron Models

  • Vectors Spaces and Matrix Models

    • ANN Classifiers

    • Vectors and Feature Spaces

    • Representation of Multivariate Data

  • Basic Structure of a Neural Network

  • Basic ANN Operations in terms of Matrices

  • Why Use Matrices in ANNs?

    • Subspace

    • Multiplication of Matrices and Vectors

    • Line Subspace Example

    • The XOR Problem

  • References