Neural Network Design (2nd ed.)
Hagan, M. T., Demuth, H. B., Beale, M. H., & De Jesús, O. (2014). Neural network design (2nd ed.). Martin Hagan.
These chapters build core vocabulary on multilayer perceptrons, backpropagation and training workflow. These readings introduce the training objective, the backpropagation procedure, and practical choices that affect optimization and generalization. They support MLO 1 to MLO 5 and prepare you for the Module 2 quiz and homework.
Chapter 7 – Supervised Hebbian Learning (Linear Associator, Hebb Rule, Pseudoinverse Rule, variations)
Chapter 8 – Performance Surfaces and Optimum Points (Taylor series, minima, first and second order conditions, quadratics)
Chapter 15 – Associative and Competitive Learning (Unsupervised Hebb, Instar, Outstar, Kohonen Rule and Self Organizing Map, Recognition Networks)
