After a short introduction to neural networks generally, a more detailed
presentation of the structure of a feed forward neural network is done, using
mathematical language, functions, matrices and vectors.
Further, emphasis has been made on perceptrons and linear regression done by using
ANN. Central concepts like learning, including weight updates, error minimization
with gradient descent are introduced and studied using these simple networks.
Finally, multilayer perceptrons are defined with their error functions and finally
backpropagation are described precisely using composite functions and the concept of
error signals.
Keywords: Backpropagation, Chain rule, Composite functions, Computing
neurons, Feedforward, Matrices, Multiple perceptron, Neural network,
Perceptron, Transfer function, Vectors.
