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NN50.DLL

Artificial Neural Networks are computational paradigms which implement simplified models of their biological counterparts, biological neural networks. Biological Neural Networks are the local assemblages of neurons and their dendrite connections that form the brain. The implementation of Neural Networks for brain-like computations like patterns recognition, decisions making, motory control and many others is made possible by the advent of large scale computers in the late 1950's. Conventional computers rely on programs that solve a problem using a pre-determined series of steps, called algorithms. These programs are controlled by a single, complex central processing unit, and store information at specific locations in memory. Artificial Neural Networks use highly distributed representations and transformations that operate in parallel, have distributed control through many highly interconnected neurons, and store their information in variable strength connections called synapses ? just like a human brain. To train a neural network you must have a data set containing sample parameters which corresponding to the results. The data used for training is usually obtained using historical data in which the outcomes are known. You can also train a neural network by creating sample problems and answers. Once the training process is completed, the neural network will be able to predict answers when new inputs are processed.

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MoneyProfesor

MoneyProfesor makes it very easy to explore the future value of loans, savings and investments using a variety of 'What If..?' scenarios. * Add single or repeating payments or withdrawals * Specify future changes in Interest rates by drawing a graph freehand * Calculate interest using a variety of common schemes (interest calculated daily, credited monthly etc) * Create multiple 'Scenarios' as part of a Study, and save the Study to disk as an XML file * View in a 'Bank Statement' type layout the payments, interest rate changes and interest calculations and payments. Edit these vaules directly in the statement view. * Extend the Scenario to an arbitrary time in the future to see 'what would happen if...?'

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AbszOrb 3D

AbszOrb 3.0 is a fully interactive real-time 3D game, screensaver, simulation in which users are able to texture, arrange and otherwise manipulate universes of spinning balls. AbszOrb is basically a fun and simple example of a particle system in physics. With the brand new free version, you have full flight control to soar through your universe or particle system and with the paid version you can texture orbs and arrange them in your universe. For example, you can texture the balls with your own head and pass universes of "you" along for them to dote on. Because you can add 5 textures, you can put five different family members on orbs too. With AbszOrb you can spend hours just futzing with it, building and texturing massive molecular structures in 3D, soaring freely through them and then, destroying them in the blink of an eye. AbszOrb is a fully interactive real-time application with hit detection, which means the orbs do know when the come into contact with one another - what they do when that happens is mostly predictable, but never exactly. It is a chaotic system, meaning that it generates an infinite range of effects from a very few simple starting conditions. It does have some random elements, but, in this case, chaotic does not mean random, it means unpredictable. While you can guess what will happen as your universe evolves in broad strokes, you?ll find it?s almost impossible to predict a single action with impunity. And that is what makes it so much fun. No matter how long you watch, you will keep seeing new things to surprise and delight you. With AbszOrb, you can watch and create the big bang or you can discover how a black hole operates or you can make purty structures and laugh maniacally as the Supreme Master of the Universe and scream " DESTROY, DESTROY, DESTROY ..........." ahem .... sorry ... got away from me there.

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VK TypeHelp

This computer program serves writers in repetitive tasks: input of long words or standard phrases, and in recovering lost pieces of text. It is of special value for input of formal language text, and in technical and commercial writing. Think of an assistant of the writer, sitting beside him and observing the sequence of his keystrokes. Then, after getting some experience, the assistant will predict and suggest reasonable continuations of the user's text input according to former occurrences of the context. Whenever the user accepts one of the suggestions, the assistant will input it, in place of the user. The user selects, shortens, or accepts a suggestion by simple keystrokes. In contrast to known auto text or text completion tools, this program does neither need explicit decisions and input of text in advance for future utilization by the user, nor does it depend on word or field content boundaries. Completing the set of tools, it will find its fields of application.

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