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Documentation of two types of permutation algorithms
This project provides documentation on two different approaches to permutations. Permutations can be generated by building them or by transposing characters between different character positions. My project addresses the efficiency between CPU speed and memory usage for generating unique permutations from input source texts that have repeated characters.
The challenge is to create an efficient algorithm that can have the capacity to permutate sources texts that are hundreds and thousands of characters long without running out of memory.
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Utility converts the weights file of a MLP Breadboard into a formula
The NeuroSolutions: Formula Generator utility converts the weights file of a default MLP breadboard (1-hidden layer with a TanhAxon in the hidden layer and either a TanhAxon or BiasAxon in the output layer) into a usable formula that can be copied and pasted into your own programs to compute the output of the trained neural network.