Intel(R) Math Kernel Library for Deep Neural Networks (Intel(R) MKL-DNN)  1.0.0
Performance library for Deep Learning
Modules
Here is a list of all modules:
[detail level 1234]
 C API
 Primitive operations
 Common primitive operations
 AttributesAn extension for controlling primitive behavior
 Sequence of post operationsAn extension for performing extra operations after a base operation
 MemoryA primitive to describe and store data
 ReorderA primitive to copy data between memory formats
 ConcatA primitive to concatenate data by arbitrary dimension
 SumA primitive to sum data
 ConvolutionThe convolution primitive computes a forward, backward, or weight update for a batched convolution operation on 1D, 2D, or 3D spatial data with bias
 DeconvolutionA primitive to compute deconvolution using different algorithms
 ShuffleA primitive to shuffle data along the axis
 EltwiseA primitive to compute element-wise operations such as parametric rectifier linear unit (ReLU)
 SoftmaxA primitive to perform softmax
 PoolingA primitive to perform max or average pooling
 LRNA primitive to perform local response normalization (LRN) across or within channels
 Batch NormalizationA primitive to perform batch normalization
 Inner productA primitive to compute an inner product
 RNNA primitive to compute the common recurrent layer
 Engine operations
 Execution stream operations
 Service functions
 BLAS functionsA subset of Basic Linear ALgebra (BLAS) functions to perform matrix-matrix multiplication
 Types
 Generic
 Memory
 Operation descriptors
 Engine
 Primitive descriptor iterators
 Primitive descriptors
 Primitive descriptor attributes
 Primitive
 Argument indices
 Queries
 Execution stream
 C++ API
 Utils
 Common data types and enumerationsA proxy to Types in C API
 AttributesAn extension for controlling primitive behavior
 EngineEngine operations
 StreamExecution stream operations
 Memory and memory related operations
 MemoryA primitive to describe and store data
 ReorderA primitive to copy data between memory formats
 ConcatA primitive to concatenate data by arbitrary dimension
 SumA primitive to sum data
 Primitives
 Primitive descriptors
 ConvolutionComputes a forward propagation, backward propagation, or weight update for convolution operation with bias on a batch of multi-dimensional tensors
 DeconvolutionA primitive to compute deconvolution using different algorithms
 LRNA primitive to perform local response normalization (LRN) across or within channels
 PoolingA primitive to perform max or average pooling
 EltwiseA primitive to compute element-wise operations such as rectified linear unit (ReLU)
 SoftmaxA primitive to perform softmax
 Batch normalizationA primitive to perform batch normalization
 Inner ProductA primitive to compute an inner product
 RNNA primitive to compute common recurrent layer
 ShuffleA primitive to shuffle data along the axis