Basic Concepts

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