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This project is my magistracy resulting work. It is intended to be an example of using neural networks in cryptography. Hashing functions are chosen as the first step in this broad topic. Implementation language is F# running under .NET 4.0 with VS 2010 RC
Darcs is a distributed advanced revision control system written in Haskell. It is similar to Git, Mercurial and Bazaar. User will have own personnel repository and commits his changes to it. Later the changes are pushed to the centralized repository. Every repository is a branch and it provides support to integrate the changes between them. It provides support to send the changes by email.
A handwritten number recognition system was developed by using image processing and neural network technique. The system was developed in Java. Other applications which make use of image processing and neural network technique will be published too.
Neuroph is lightweight Java Neural Network Framework which can be used to develop common neural network architectures. Small number of basic classes which correspond to basic NN concepts, and GUI editor makes it easy to learn and use.
ffnet is a fast and easy-to-use feed-forward neural network training solution for python. Many nice features are implemented: arbitrary network connectivity, automatic data normalization, very efficient training tools, network export to fortran code.
Mercurial is fast and powerful. Mercurial offers you the power and speed to efficiently handle projects of any size and kind. Every clone contains the whole project history, so committing, branching, tagging and merging are local operations which makes them fast and convenient. You can use a multitude of workflows and easily enhance its functionality with extensions.
SHARK provides libraries for the design of adaptive systems, including methods for linear and nonlinear optimization (e.g., evolutionary and gradient-based algorithms), kernel-based algorithms and neural networks, and other machine learning techniques. Please vist http://image.diku.dk/shark for the alpha release of Shark 3.0.
An open-source C++ library of machine learning by New York University's machine learning lab, led by Yann LeCun. In particular, implementations of convolutional neural networks with energy-based models along with a GUI, demos and tutorials.