Interface  Description 

Storage.Matrix 
Abstract matrix interface

Storage.Tensor 
Abstract tensor interface

Storage.Vector 
Abstract vector interface

Class  Description 

DeepLearning 
Deep Learning Neural Net implementation based on MRTask

DeepLearningModel 
The Deep Learning model
It contains a DeepLearningModelInfo with the most uptodate model,
a scoring history, as well as some helpers to indicate the progress

DeepLearningModel.DeepLearningModelOutput 
The Deep Learning model output contains a few extra fields in addition to the metrics in Model.Output
1) Scoring history (raw data)
2) weights/biases (raw data)
3) variable importances (TwoDimTable)

DeepLearningModel.DeepLearningParameters 
Deep Learning Parameters

DeepLearningModelInfo 
This class contains the state of the Deep Learning model
This will be shared: one per node

DeepLearningModelInfo.GradientCheck  
DeepLearningMojoWriter  
DeepLearningScoringInfo 
Lightweight DeepLearning scoring history.

DeepLearningTask  
DeepLearningTask2 
DRemoteTaskbased Deep Learning.

Dropout 
Helper class for dropout training of Neural Nets

MurmurHash 
This is a very fast, noncryptographic hash suitable for general hashbased
lookup.

Neurons 
This class implements the concept of a Neuron layer in a Neural Network
During training, every MRTask F/J thread is expected to create these neurons for every map call (Cheap to make).

Neurons.ExpRectifier  
Neurons.ExpRectifierDropout 
Exponential Rectifier with dropout

Neurons.Input 
Input layer of the Neural Network
This layer is different from other layers as it has no incoming weights,
but instead gets its activation values from the training points.

Neurons.Linear 
Output neurons for regression  Linear units

Neurons.Maxout 
Maxout neurons (picks the max out of the k activation_j = sum(A_ij*x_i) + b_j)
Requires k times the model parameters (weights/biases) as a "normal" neuron

Neurons.MaxoutDropout 
Maxout neurons with dropout

Neurons.Output 
Abstract class for Output neurons

Neurons.Rectifier 
Rectifier linear unit (ReLU) neurons

Neurons.RectifierDropout 
Rectifier linear unit (ReLU) neurons with dropout

Neurons.Softmax 
Output neurons for classification  Softmax

Neurons.Tanh 
Tanh neurons  most common, most stable

Neurons.TanhDropout 
Tanh neurons with dropout

Storage  
Storage.DenseColMatrix 
Dense column matrix implementation

Storage.DenseRowMatrix 
Dense row matrix implementation

Storage.DenseVector 
Dense vector implementation

Storage.SparseRowMatrix 
Sparse row matrix implementation

Enum  Description 

DeepLearningModel.DeepLearningParameters.Activation 
Activation functions

DeepLearningModel.DeepLearningParameters.ClassSamplingMethod  
DeepLearningModel.DeepLearningParameters.InitialWeightDistribution  
DeepLearningModel.DeepLearningParameters.Loss 
Loss functions
Absolute, Quadratic, Huber, Quantile for regression
Quadratic, ModifiedHuber or CrossEntropy for classification

DeepLearningModel.DeepLearningParameters.MissingValuesHandling 