MachineIntelligenceCore:NeuralNets
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  A  
Conv4x4x1Filter3x1x1s3Double (mic::neural_nets::unit_tests)   GradPID (mic::neural_nets::optimization)   MNISTPatchReconstructionApplication (mic::applications)   Rosenbrock2DLandscape   
Conv5x5x1Filter1x2x2s3Float (mic::neural_nets::unit_tests)   
  H  
MNISTPatchSoftmaxApplication (mic::applications)   
  S  
AdaDelta (mic::neural_nets::optimization)   Conv5x5x1Filter1x3x3s1Float (mic::neural_nets::unit_tests)   Momentum (mic::neural_nets::optimization)   
AdaGrad (mic::neural_nets::optimization)   Conv5x6x1Filter1x4x4s1Float (mic::neural_nets::unit_tests)   HebbianLinear (mic::mlnn::fully_connected)   MultiLayerNeuralNetwork (mic::mlnn)   Sigmoid (mic::mlnn::activation_function)   
AdaGradPID (mic::neural_nets::optimization)   Conv7x7x3Filter3x3x3s2Float (mic::neural_nets::unit_tests)   HebbianNeuralNetwork (mic::mlnn)   
  N  
Simple2LayerRegressionNN (mic::neural_nets::unit_tests)   
Adam (mic::neural_nets::optimization)   Conv8x8x1Filter2x4x4s4Double (mic::neural_nets::unit_tests)   HebbianRule (mic::neural_nets::learning)   Softmax (mic::mlnn::cost_function)   
AdamID (mic::neural_nets::optimization)   ConvHebbian (mic::mlnn::experimental)   
  L  
NormalizedHebbianRule (mic::neural_nets::learning)   Softmax4x1Float   
  B  
Convolution (mic::mlnn::convolution)   NormalizedZerosumHebbianRule (mic::neural_nets::learning)   SparseLinear (mic::mlnn::fully_connected)   
Cropping (mic::mlnn::convolution)   Layer (mic::mlnn)   
  O  
Sphere1DLandscape   
BackpropagationNeuralNetwork (mic::mlnn)   CrossEntropyLoss (mic::neural_nets::loss)   Linear (mic::mlnn::fully_connected)   Sphere20DLandscape   
Beale2DFunction (mic::neural_nets::optimization::artificial_landscapes)   
  D  
Linear1x1Float (mic::neural_nets::unit_tests)   OptimizationArray (mic::neural_nets::optimization)   SphereFunction (mic::neural_nets::optimization::artificial_landscapes)   
Beale2DLandscape   Linear2x3Double (mic::neural_nets::unit_tests)   OptimizationFunction (mic::neural_nets::optimization)   SquaredErrorLoss (mic::neural_nets::loss)   
BinaryCorrelator (mic::mlnn::fully_connected)   DifferentiableFunction (mic::neural_nets::optimization::artificial_landscapes)   Linear2x3Float (mic::neural_nets::unit_tests)   
  P  
  T  
BinaryCorrelatorLearningRule (mic::neural_nets::learning)   Dropout (mic::mlnn::regularisation)   Linear50x100Double (mic::neural_nets::unit_tests)   
  C  
  E  
Linear5x2Float (mic::neural_nets::unit_tests)   Padding (mic::mlnn::convolution)   Tutorial2LayerNN (mic::neural_nets::unit_tests)   
LogLikelihoodLoss (mic::neural_nets::loss)   
  R  
  V  
Conv28x28x1Filter2x28x28s1Double (mic::neural_nets::unit_tests)   ELU (mic::mlnn::activation_function)   Loss (mic::neural_nets::loss)   
Conv2x2x2Filter2x1x1s1Double (mic::neural_nets::unit_tests)   
  G  
  M  
ReLU (mic::mlnn::activation_function)   Vectors3x2Float   
Conv3x3x2Filter3x2x2s1Float (mic::neural_nets::unit_tests)   RMSProp (mic::neural_nets::optimization)   Vectors4x1Float   
Conv4x4x1Filter1x2x2s2Float (mic::neural_nets::unit_tests)   GradientDescent (mic::neural_nets::optimization)   MaxPooling (mic::mlnn::convolution)   Rosenbrock2DFunction (mic::neural_nets::optimization::artificial_landscapes)   Vectors4x1Float2   
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