Understanding Deep Learning Applications in Rare Event Prediction

This course teaches how to build deep learning models using TensorFlow to predict rare probability events.

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59 Lessons

Certificate of Completion

This course teaches how to build deep learning models using TensorFlow to predict rare probability events.

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This course includes

78 Playgrounds

6 Quizzes

This course includes

78 Playgrounds

6 Quizzes

Course Overview

This course aims to provide a practical understanding of the key constructs of deep learning, including Multi-layer Perceptrons, Long-Short-Term Memory (LSTM) networks, convolutional neural networks, and autoencoders, which focus on rare event prediction. Through this course, you’ll gain hands-on experience developing solutions for rare event prediction. You’ll start by modeling rare events that occur infrequently. Next, you’ll explore machine and deep learning solutions for imbalanced data. You’ll then le...Show More

What You'll Learn

An understanding of deep learning fundamentals, including Multi-layer Perceptrons, LSTMs, convolutional neural networks, and autoencoders

Hands-on experience modeling rare events

The ability to build and train deep learning models using TensorFlow that predict rare events

Hands-on experience implementing LSTMs for capturing long-term dependencies in sequential data

What You'll Learn

An understanding of deep learning fundamentals, including Multi-layer Perceptrons, LSTMs, convolutional neural networks, and autoencoders

Course Content

Getting Started

This chapter introduces predictive patterns, rare event prediction, and solutions for imbalanced data, including practical challenges.

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

Rare Event Prediction

This chapter covers statistical foundations and prediction strategies for rare events, addresses spatio-temporal challenges, and concludes with a quiz.

Statistical Foundations and Prediction Strategies Tackling Spatio-Temporal Challenges Quiz: Rare Event Prediction

Multi-Layer Perceptrons (MLPs)

This chapter covers MLP basics, data prep, dropout, custom functions, and metrics, with insights into networks, optimization, and batch normalization.

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)

Long Short-Term Memory (LSTM) Networks

This chapter delves into LSTM networks, covering LSTM cells, activations, and gradients. It also teaches about LSTM, time series, and bidirectional techniques.

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

Convolutional Neural Networks (CNNs)

This chapter explores CNNs, covering convolution basics, parameter sharing, pooling layers, and kernel operations.

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

Autoencoders

8 Lessons

This chapter covers autoencoders and their use as PCA alternatives, anomaly detection, diverse applications, and how to optimize sparse and autoencoders.

Conclusion

1 Lesson

This chapter summarizes key insights from the course and ideas you’ve learned throughout the course.

Course Author

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Anthony Walker

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Emma Bostian 🐞

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Evan Dunbar

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Carlos Matias La Borde

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Vinay Krishnaiah

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Eric Downs

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DevOps Engineer

Anthony Walker

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Emma Bostian 🐞

@EmmaBostian

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