Practical Introduction to Artificial Neural Networks

An introduction to artificial neural networks

Artificial neural networks are algorithms which have been developed to tackle a range of computational problems. These range from modelling brain function to making predictions of time-dependent phenomena to solving hard (NP-complete) problems. In this introduction we describe a single, yet very important, type of network known as a feedforward network. This network is a mathematical model whic...

Practical on Artificial Neural Networks

Neural networks, more accurately called Artificial Neural Networks (ANNs), are computational models that consist of a number of simple processing units that communicate by sending signals to one another over a large number of weighted connections. They were originally developed from the inspiration of human brains. In human brains, a biological neuron collects signals from other neurons through...

Introduction to Artificial Intelligence OCR using Artificial Neural Networks

Optical character recognition refers to the process of translating images of handwritten, typewritten, or printed text into a format understood by machines for the purpose of editing, indexing/searching, and a reduction in storage size. The OCR process is complicated by noisy inputs, image distortion, and differences between typefaces, sizes, and fonts. Artificial neural networks are commonly u...

Introduction: Modelling perception with artificial neural networks

This book represents a substantial update of a theme issue of the Philosophical Transactions of the Royal Society B Journal, ‘The use of artificial neural networks to study perception in animals’ (Phil Trans R Soc B 2007 March 29; 362(1479)). Most of the 14 papers in that theme issue have been significantly updated and we include a further five entirely new chapters, reflecting emerging themes ...

Introduction to Artificial Neural Networks Training and Applications