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Understanding Deep Learning Applications in Rare Event Prediction
1.
Getting Started
Introduction
Predictive Patterns and Rare Event Prediction
The Impact and Prediction of Rare Events
Tackling the Paper Sheet-Break Challenge
Machine and Deep Learning Solutions for Imbalanced Data
Quiz: Getting Started
2.
Rare Event Prediction
Statistical Foundations and Prediction Strategies
Tackling Spatio-Temporal Challenges
Quiz: Rare Event Prediction
3.
Multi-Layer Perceptrons (MLPs)
Unraveling the Basics of Neural Networks
Understanding Multi-Layer Perceptrons
Data Preparation and Scaling
Neural Network Construction
Using Dropout to Combat Overfitting
Refining Neural Networks with Class Weights
Activating Neural Network Potential with Advanced Functions
Custom Activation Functions
Enhancing Model Selection with Custom Metrics
Optimizing and Evaluating the Final Neural Network Model
Exercise: Applying Batch Normalization in Neural Networks
Quiz: Multi-Layer Perceptrons (MLPs)
4.
Long Short-Term Memory (LSTM) Networks
Understanding Long Short-Term Memory Networks
Intricacies of Long Short-Term Memory Cells
Understanding LSTM Activations and Stabilized Gradients
LSTM Layer Operations and Network Architectures
Preparing and Analyzing Time Series Data with LSTM Models
Restricted Stateless LSTM Network for Baseline Modeling
Unrestricted LSTM with Dropouts and Backward Input
Bidirectional LSTM for Enhanced Temporal Learning
Advanced Techniques in LSTM Models
Exercise: Exploring Activation Functions in LSTM Models
Quiz: Long Short-Term Memory (LSTM) Networks
5.
Convolutional Neural Networks (CNNs)
Convolutional Networks in the High-Dimensional Data Maze
Decoding Convolution
Understanding Parameter Sharing, Weak Filters, and Equivariance
Role of Pooling Layers in Convolutional Network Regularization
Kernels in Convolutional Network Operations
Padding, Strides, and Dilation
Architecture of Convolutional Networks
Applying Convolutional Networks to Multivariate Time Series
Modeling Multivariate Time Series
Efficient Pooling Strategies
Maximizing Efficiency with Complete Statistics
Strategic Pooling Approaches
Optimizing Feature Map Pooling
Advancing Pooling Techniques
Constructing an Optimized Baseline Convolutional Network
Exercise: Time Series Analysis with Longer Lookbacks
Quiz: Convolution Neural Networks
6.
Autoencoders
Autoencoders as Neural Network Version of PCA
Diverse Autoencoders and Their Applications
Anomaly Detection with Autoencoders
Building Sparse Autoencoders
Advanced Autoencoders for Sequence and Image Data
Enhancing Autoencoder Models
Optimizing Autoencoder Architectures
Quiz: Autoencoders
7.
Conclusion
Closing Thoughts
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Understanding Deep Learning Applications in Rare Event Prediction
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