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

Trusted by 1.4 million developers working at companies

Anthony Walker

@_webarchitect_

Evan Dunbar

ML Engineer

Carlos Matias La Borde

Software Developer

Souvik Kundu

Front-end Developer

Vinay Krishnaiah

Software Developer

Eric Downs

Musician/Entrepeneur

Kenan Eyvazov

DevOps Engineer

Souvik Kundu

Front-end Developer

Eric Downs

Musician/Entrepeneur

Anthony Walker

@_webarchitect_

Evan Dunbar

ML Engineer

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