Artificial Neural Networks

Advanced Linear Algebra Calculus Artificial neural networks are a computational tool, based on the properties of biological neural systems. Neural networks excel in a number of problem areas where conventional von Neumann computer systems have traditionally been slow and inefficient. This book is going to discuss the creation and use of artificial neural networks. Table of Contents Introduction Overview Artificial Neural Networks/Print Version Neural Network Basics Biological Neural Networks History MATLAB Neural Networking Toolbox Activation Functions ANN Models Feed-Forward Networks Radial Basis Function Networks Recurrent Networks Echo State Networks Hopfield Networks Self-Organizing Maps Competitive Models ART Models Boltzmann Machines Committee of Machines Autoencoders Convolutional Neural Networks Teaching and Learning Learning Paradigms Error-Correction Learning Hebbian Learning Competitive Learning Boltzmann Learning ART Learning Self-Organizing Maps Applications Pattern Recognition Clustering Feature Detection Series Prediction Data Compression Curve Fitting Optimization Control Future Work Criticisms and Problems Artificial Intelligence Resources Resources Licensing machine learning A 50%