Applying Neural Networks: A Practical Guide

Abstract

Techniques for building neural networks: introduction what are neural networks? how does neural computing differ from traditional programming? how are neural networks built? how do neural networks learn? what do I need to build an MLP? the neural project life cycle the generalisation-accuracy trade-off implementation details activation and learning equations a simple example: modelling a pendulum. Dat encoding and re-coding: introduction data type classification initial statistical calculations dimensionality reduction scaling a data set neural encoding methods temporal data when to carry out re-coding implementation details. Building a network: introduction designing the MLP training neural networks implementations details. Time varying systems: time varying data sets neural networks for predicting or classifying time series choosing the best method for the task predicting more than one step into the future learning separate paths through state space recurrent networks as models of finite state automata summary of temporal neural networks. Data collection and validation: data collection building the training and test sets data quality calculating entropy values for a data set using a forward-inverse model to serve III posed problems. Output and error analysis: introduction what do the errors mean? error bars and confidence limits methods for visualising errors novelty detection implementation details a simple two class example unbalanced data: a mail shot targeting example auto-associative network novelty detection training a network on confidence limits an example based on credit rating. Network use and analysis: introduction extracting reasons traversing a network summary calculating the derivatives personnel selection: a worked example. Managing a neural network based project: project context development platform project personnel project costs the benefits of neural computing the risks involved with neural computing alternatives to a neural computing approach project time scale project documentation system maintenance. Review of neural applications: introduction to part II. Neural networks and signal processing: introduction signal processing as data preparation pre-processing techniques for visual processing neural filters in the Fourier and temporal domains speech recognition production quality control an artistic style classifier fingerprint analysis summary. Financial and business modelling. (Part contents).