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intelligent systems - neural networks
Pages 13 (3263 words)
A Neural Network is defined as an interconnected assembly of simple processing elements, units or nodes, whose functionality is loosely based on the animal brain. The processing ability of the network is stored in the inter-unit connection strengths, or weights, obtained by a process of adaptation to, or learning from, a set of training patterns…
The user can produce training, validating and querying files using the facilities in EasyNN or using any editor, word processor or spreadsheet that supports text files. EasyNN can learn from training data and can self authenticate while learning. It can be queried from a file or interactively. EasyNN can produce spreadsheet like output and results files. All graphs and diagrams are restructured during training and querying so the user can see how the neural networks are working.
The EasyNN-Plus has a number of shortcuts, and power keys which allows an advance user to carry out their task quiet efficiently. Despite the fact that for the new users who are not conversant in it can find it quiet difficult in the beginning and puzzling as to how to use the software, and this may take a lot of time and patience.
At the same time as using EasyNN the user does not in fact learn how to create the neural network, as EasyNN mechanize the process of producing a neural network the steps produce the network is quiet unseen from the user. EasyNN uses mathematical literacy as the backbone of the information provided by the user. This lets EasyNN-Plus to provide several views of a neural network, and also increases the uses of using EasyNN-Plus to produce neural networks for a few different things e.g. ...
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