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ranjan c understanding deep learning application rare event prediction 2023

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Textbook in PDF format "It is like a voyage of discovery, seeking not for new territory but new knowledge. It should appeal to those with a good sense of adventure," Dr. Frederick Sanger. I hope every reader enjoys this voyage in deep learning and find their adventure. Think of deep learning as an art of cooking. One way to cook is to follow a recipe. But when we learn how the food, the spices, and the fire behave, we make our creation. And an understanding of the "how" transcends the creation. Likewise, an understanding of the "how" transcends deep learning. In this spirit, this book presents the deep learning constructs, their fundamentals, and how they behave. Baseline models are developed alongside, and concepts to improve them are exemplified. Preface Acknowledgment Website Introduction Examples of Application Rare Diseases Fraud Detection Network Intrusion Detection Detecting Emergencies Click vis-à-vis churn prediction Failures in Manufacturing A Working Example Problem Motivation Paper Manufacturing Process Data Description Machine Learning vs. Deep Learning In this Book Rare Event Prediction Rare Event Problem Underlying Statistical Process Problem definition Objective Loss function Accuracy measures Challenges High-dimensional Multivariate Time Series Early Prediction Imbalanced Data Setup TensorFlow Prerequisites Install Python Install Virtual Environment TensorFlow 2x Installation Testing Sheet Break Problem Dataset Multi-layer Perceptrons Background Fundamentals of MLP Initialization and Data Preparation Imports and Loading Data Data Pre-processing Curve Shifting Data Splitting Features Scaling MLP Modeling Sequential Input Layer Dense Layer Output Layer Model Summary Model Compile Model Fit Results Visualization Dropout What is Co-Adaptation? What Is Dropout? Dropout Layer Class Weights Activation What is Vanishing and Exploding Gradients? Cause Behind Vanishing and Exploding Gradients Gradients and Story of Activations Self-normalization Selu Activation Novel Ideas Implementation Activation Customization Metrics Customization Models Evaluation Rules-of-thumb Exercises Long Short Term Memory Networks Background Fundamentals of LSTM Input to LSTM LSTM Cell State Mechanism Cell Operations Activations in LSTM Parameters Iteration Levels Stabilized Gradient LSTM Layer and Network Structure Input Processing Stateless versus Stateful Return Sequences vs Last Output Initialization and Data Preparation Imports and Data Temporalizing the Data Data Splitting Scaling Temporalized Data Baseline Model—A Restricted Stateless LSTM Input layer LSTM layer Output layer Model Summary Compile and Fit Model Improvements Unrestricted LSTM Network Dropout and Recurrent Dropout Go Backwards Bi-directional Longer Lookback/Timesteps History of LSTMs Summary Rules-of-thumb Exercises Convolutional Neural Networks Background The Concept of Convolution Convolution Properties Parameter Sharing Weak Filters Equivariance to Translation Pooling Regularization via Invariance Modulating between Equivariance and Invariance Multi-channel Input Kernels Convolutional Variants Padding Stride Dilation 1x1 Convolution Convolutional Network Structure Conv1D, Conv2D, and Conv3D Convolution Layer Output Size Pooling Layer Output Size Parameters Multivariate Time Series Modeling Convolution on Time Series Imports and Data Preparation Baseline Learn Longer-term Dependencies Multivariate Time Series Modeled as Image Conv1D and Conv2D Equivalence Neighborhood Model Summary Statistics for Pooling Definitions (Minimal) Sufficient Statistics Complete Statistics Ancillary Statistics Pooling Discoveries Reason behind Max-Pool Superiority Preserve Convolution Distribution Maximum Likelihood Estimators for Pooling Uniform Distribution Normal Distribution Gamma Distribution Weibull Distribution Advanced Pooling Adaptive Distribution Selection Complete Statistics for Exponential Family Multivariate Distribution History of Pooling Rules-of-thumb Exercises Autoencoders Background Architectural Similarity between PCA and Autoencoder Encoding—Projection to Lower Dimension Decoding—Reconstruction to Original Dimension Autoencoder Family Undercomplete Overcomplete Denoising Autoencoder (DAE) Contractive Autoencoder (CAE) Sparse Autoencoder Anomaly Detection with Autoencoders Anomaly Detection Approach Data Preparation Model Fitting Diagnostics Inferencing Feed-forward MLP on Sparse Encodings Sparse Autoencoder Construction MLP Classifier on Encodings Temporal Autoencoder LSTM Autoencoder Convolutional Autoencoder Autoencoder Customization Well-posed Autoencoder Model Construction Orthonormal Weights Sparse Covariance Rules-of-thumb Exercises Appendices Appendix Importance of Nonlinear Activation Appendix Curve Shifting Appendix Simple Plots Appendix Backpropagation Gradients Appendix Data Temporalization Appendix Stateful LSTM Appendix Null-Rectified Linear Unit Appendix 11 Convolutional Network Appendix CNN: Visualization for Interpretation Appendix Multiple (Maximum and Range) Pooling Statistics in a Convolution Network Appendix Convolutional Autoencoder-Classifier Appendix Oversampling SMOTE

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ranjan c understanding deep learning application rare event prediction 2023

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