// doc/dnn3.dox
  
  
  // Copyright 2015   Johns Hopkins University (author: Daniel Povey)
  
  // See ../../COPYING for clarification regarding multiple authors
  //
  // Licensed under the Apache License, Version 2.0 (the "License");
  // you may not use this file except in compliance with the License.
  // You may obtain a copy of the License at
  
  //  http://www.apache.org/licenses/LICENSE-2.0
  
  // THIS CODE IS PROVIDED *AS IS* BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
  // KIND, EITHER EXPRESS OR IMPLIED, INCLUDING WITHOUT LIMITATION ANY IMPLIED
  // WARRANTIES OR CONDITIONS OF TITLE, FITNESS FOR A PARTICULAR PURPOSE,
  // MERCHANTABLITY OR NON-INFRINGEMENT.
  // See the Apache 2 License for the specific language governing permissions and
  // limitations under the License.
  
  namespace kaldi {
  
  /**
    \page dnn3 The "nnet3" setup
  
    \section dnn3_intro Introduction
  
    This documentation covers the latest, "nnet3", DNN setup in Kaldi.
    For an overview of all deep neural network code in Kaldi, explaining
    Karel's version, see \ref dnn.
  
    The nnet3 setup is intended to support more general kinds of networks
    than simple feedforward networks (e.g. things like RNNs and LSTMs)
    in a natural way that should not require any actual coding.  Like the
    nnet2 setup, it supports parallel training across GPUs on multiple
    machines (using an approach based on natural gradient-stabilized SGD
    with model averaging, see <a href=http://arxiv.org/abs/1410.7455> this paper </a>.
  
    The documentation has been broken up into multiple pages: see
      - \subpage dnn3_code_data_types
      - \subpage dnn3_code_compilation
      - \subpage dnn3_code_optimization
      - \subpage dnn3_scripts_context
  
  
  */
  
  
  }