Primary aim of the dinrhiw is to be linear algebra library and machine learning library. For this reason dinrhiw implements PCA and neural network codes. Currently, the neural network code only supports:
- hamiltonian monte carlo sampling (HMC) and simple bayesian neural network
- second order L-BFGS search
- gradient descent (backpropagation)
As well as mathematical routines for arbitrary precision mathematics, hermite curve interpolation and many other things.
released on 1 September 2015
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|GPLv3||IanK||1 November 2016|
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This entry (in part or in whole) was last reviewed on 1 November 2016.
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