Intel(R) Math Kernel Library for Deep Neural Networks (Intel(R) MKL-DNN)  0.13
Performance library for Deep Learning
Modules
Here is a list of all modules:
[detail level 1234]
 C API
 Engine operations
 Execution stream operations
 Primitive operations
 AttributesAn extension for controlling primitive behavior
 Sequence of post operationsAn extension for performing extra operations after base operation
 Batch NormalizationA primitive to perform batch normalization

\[dst[n][c][h][w] = \gamma[c] \frac{src[n][c][h][w] - \mu[c]} {\sqrt{\sigma[c] + eps}} + \beta[c],\]

 Common primitive operations
 ConcatA primitive to concatenate data by arbitrary dimension
 ConvolutionA primitive to compute convolution using different algorithms
 Convolution followed by ReLUA merged primitive to compute a convolution followed by relu
 EltwiseA primitive to compute element wise operations like parametric rectifier linear unit (ReLU)
 Inner productA primitive to compute an inner product
 LRNA primitive to perform local response normalization (LRN) across or within channels
 MemoryA primitive to describe data
 PoolingA primitive to perform max, min, or average pooling
 ReLU (deprecated, use Eltwise instead)A primitive to compute a parametric rectifier linear unit (ReLU)
 ReorderA primitive to copy data between memory formats
 SoftmaxA primitive to perform softmax
 SumA primitive to sum data
 Service functions
 Types
 Auxiliary types for memory description
 Operation descriptors
 Engine
 Execution stream
 Generic
 Primitive
 Primitive descriptor attributes
 Primitive descriptor iterators
 Primitive descriptors
 Queries
 C++ API
 Attributes
 Common data types and enumerations
 Engine
 Primitives
 Batch normalization
 Concat
 Convolution
 Eltwise
 Inner Product
 LRN
 Memory
 Pooling
 Reorder
 Softmax
 Sum
 View
 Stream
 Utils