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. forecasting, data validation, and customer research.
EasyNN used the algorithm as back propagation. The differences from most other back propagation based applications are the data structures and the way the data is presented to the learning algorithm. EasyNN uses double linked lists to store the examples, the nodes and the connections. The lists can be managed quickly in both directions at the same time. The lists can also be extended and contracted dynamically.
The EasyNN estimates the number of neurons required and determines how the network will learn. It maximizes the learning rate and thrust by running a few learning cycles with different values before actual training. EasyNN can then automatically decrease them if there is inconsistent learning or if oscillations in training error take place. The user can change these values, however again to be able to do so with confidence requires considerable knowledge about the software, the data set and the neural network.
A specific advantage of using EasyNN software is that it has validation built in. This is used to avoid the neural network from becoming "overfitted" to the training data. The user can select the percentage of the training data to be used for validation. Another useful tool in EasyNN is the integrated function for handling missing values. This function replaces the missing value with the median of the affected attribute in the training dataset in both training and evaluation.
The data being used here is medical information on possible patients suffering from diabetes. A number of patients may or may not have diabetes and this network is going to be built to predict whether or not a patient has diabetes with the data available.
The first stage in building a network using EasyNN is to provide the software with the appropriate data it is going to need. In this instant a .txt file was imported into EasyNN containing all the data the program require to build the network including the names for each column and their values. Other formats