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