lua-torch-nn - 0~20171002-g8726825+dfsg-4 main

This package provides an easy and modular way to build and train
simple or complex neural networks using Torch Framework:
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* Modules are the bricks used to build neural networks.
Each are themselves neural networks, but can be combined with
other networks using containers to create complex neural networks:
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+ Module: abstract class inherited by all modules.
+ Containers: container classes.
+ Transfer functions: non-linear functions.
+ Simple layers: simple network layer like `Linear`.
+ Table layers: layers for manipulating `table`s.
+ Convolution layers: several kinds of convolutions.
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* Criterions compute a gradient according to a given loss function
given an input and a target:
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+ Criterions: a list of all criterions.
+ `MSECriterion`: the Mean Squared Error criterion used for regression;
+ `ClassNLLCriterion`: the Negative Log Likelihood criterion used for
classification.
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* Additional documentation:
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+ Overview of the package essentials including modules, containers
and training.
+ Training: how to train a neural network using optim.
+ Testing: how to test your modules.
+ Experimental Modules: a package containing experimental modules and
criteria.
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This package is a core part of the Torch Framework.