An Implementation of Deep Neural Network for Regression and Classification

applyDropoutMask | Applies the given dropout mask to the given data row-wise. |

AR | Calculates the Accuracy Ratio of a classifier |

AR.DArch | Calculates the Accruacy Ratio of a given set of probability |

AR.default | Calculates the Accruacy Ratio of a given set of probability |

AR.numeric | Calculates the Accruacy Ratio of a given set of probability |

backpropagate_delta_bn | Calculates the delta functions using backpropagation |

batch_normalization | Batch Normalization Function that normalizes the input before... |

batch_normalization_differential | Function that calcualtes the differentials in the batch... |

calcualte_population_mu_sigma | Calculates the mu and sigmas of a darch instance |

classification_error | Calculates the classification error |

convert_categorical | Data proprosess function that covnerts a categorical input to... |

crossEntropyErr | Calculates the cross entropy error |

finetune_SGD_bn | Updates a deep neural network's parameters using stochastic... |

generateDropoutMask | Generates the dropout mask for the deep neural network |

generateDropoutMasksForDarch | Generates dropout masks for dnn |

matMult | Calculates the outer product of two matricies |

meanSquareErr | Calculates the mean squared error |

new_dnn | Creats a new instance of darch class |

print_weight | Prints out the weight of a deep neural network |

rectified_linear_unit_function | Rectified Linear Unit Function |

reset_population_mu_sigma | Resets the mu and sigmas of a darch instance to 0 and 1 |

rsq | Calculate the RSQ of a regression model Utilitiy function... |

rsq.DArch | Utilitiy function that calcualtes RSQ of a DArch instance |

rsq.lm | Utilitiy function that calcualtes RSQ of a linear model |

run_dnn | Execution function that runs in the batch normalization mode |

train_dnn | Train a deep neural network |

verticalize | Creates a matrix by repeating a row vector N times |

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