NeuroShell 2 NeuroShell 2 is our legacy neural network product targeted towards computer science instructors and students. It contains classic algorithms and architectures popular with graduate school professors and computer science students. • • • • • • • • • • NeuroShell 2 combines powerful neural network architectures, a Microsoft® Windows icon driven user interface, sophisticated utilities, and popular options to give users the ultimate neural network experimental environment. It is recommended for academic users only, or those users who are concerned with classic neural network paradigms like backpropagation.
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Users interested in solving real problems should consider the NeuroShell Predictor, NeuroShell Classifier, or the NeuroShell Trader. Beginner's Neural Networks NeuroShell 2 includes both Beginner's and Advanced Systems in addition to Runtime Facilities. NeuroShell 2 includes interfaces for beginners and experts alike. The Beginner's System in NeuroShell 2 is designed for novices and first-time users. It includes a simplified set of procedures for building and executing a complete, powerful neural network application. The Beginner's System uses our powerful enhanced backpropagation paradigm, and defaults neural network parameters such as learning rate, momentum, and number of hidden neurons.
To use the system you enter data, specify the inputs and outputs, and train the neural network. You are then able to apply the trained neural network to new data and export the results to other programs. Advanced Neural Networks The NeuroShell 2 Advanced Options screen displays the independent modules that may be used to create a neural network application. The Advanced System in NeuroShell 2 gives experienced neural network users the ability to create and execute 16 different neural network architectures, with more user control compared to the Beginner's System. Spreadsheet Format Users can choose to enter their data in their familiar spreadsheet program or work in the NeuroShell 2 Datagrid. The program uses spreadsheet files as its internal format so you can view or edit them yourself. In fact, NeuroShell 2 will call your favorite Windows spreadsheet program.
File Import and Export NeuroShell 2 imports ASCII, binary, and spreadsheet files. The program imports and exports ASCII and binary file formats. Malayalam serial flowers tv youtube. The File Export Module allows you to convert NeuroShell 2 files to ASCII or binary files. It will also merge data into an existing spreadsheet or print a file.
Data Preprocessing The Rules Module allows you to create If/Then/Else rules to pre-process data. The Symbol Translate module converts alphanumeric data or strings into numbers which can be processed by the neural network.
For example, you may want to convert 'cold', 'warm', and 'hot' to 1, 2, and 3 respectively. The Rules Module allows you to create If/Then/Else type rules to preprocess data prior to feeding it to the neural network. For example, you can use two input variables to create a third. You may believe that if you combine the price of gold and IBM stock and the total is greater than 400, the economy is improving.
You can translate this information into a new neural network input as follows: If gold + IBM > 400, then economy (the new variable) = 1, else economy =0. You may also use the Rules Module to post-process the neural network's predictions and classifications.
Test Set Extract NeuroShell 2 gives you great flexibility in choosing which columns of your spreadsheet are inputs and which are outputs. NeuroShell 2 makes it easy to pull out test and production data sets from the training data. The Test Set Extract Module offers four different methods for selecting data: • N percent randomly chosen.• Every Nth pattern.• Block designation.• By row marker in the data.• Combination block designation and random. The Design Module offers a palette of 16 different neural network architectures/paradigms for different types of data. (Users may customize each neural network architecture by setting parameters.). User Control Users can specify their own learning rate, momentum, activation functions, and initial weight ranges on a layer basis in the Design Module. Elect rotational or random pattern selection.