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Understanding Deep Learning Applications in Rare Event Prediction

Name: Understanding Deep Learning Applications in Rare Event Prediction
Price: 59 USD
Author: Chitta Ranjan

Gain insights into deep learning constructs like LSTM networks and autoencoders. Delve into modeling, prediction strategies, and TensorFlow implementation for rare event prediction in real-life scenarios.

59 lessons

Created by Chitta Ranjan

Understanding Deep Learning Applications in Rare Event Prediction

What's included

59 lessons across 8 modules
6 quizzes with instant feedback
AI Tutor on every lesson

Join 3.1 million developers at

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

Learning Roadmap

59 Lessons6 Quizzes

Getting Started

Get familiar with applying deep learning for predicting rare, impactful events with tailored evaluation metrics.

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

Unpack the core of statistical foundations, prediction strategies, and spatio-temporal challenges in rare event prediction.

Statistical Foundations and Prediction Strategies

Tackling Spatio-Temporal Challenges

Quiz: Rare Event Prediction

Multi-Layer Perceptrons (MLPs)

12 Lessons

Examine foundational concepts, data preparation, neural network construction, regularization, custom functions, and advanced metrics in MLPs.

Long Short-Term Memory (LSTM) Networks

11 Lessons

Enhance your skills in LSTM networks for advanced sequential data modeling and prediction.

Convolutional Neural Networks (CNNs)

17 Lessons

Take a look at CNNs for efficient high-dimensional data handling, spatial feature extraction, and time series modeling.

Autoencoders

8 Lessons

See how it works to build and optimize diverse autoencoders for anomaly detection and feature extraction.

Conclusion

Build on your mastery of deep learning techniques in rare event prediction.

Closing Thoughts

Certificate of Completion

Showcase your accomplishment by sharing your certificate of completion.

About this course

WHY I WROTE THIS COURSE

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 learn about rare event “prediction,” its statistical foundations, and prediction strategies. Finally, you’ll implement real-life prediction models using TensorFlow, a crucial framework for building and training models. By the end of this course, you’ll have a strong grasp of advanced machine and deep learning techniques in rare event prediction. These exercises will not only help you solve complex issues but also become a firm footing for further study and innovation in this field.

ABOUT THE AUTHOR

Chitta Ranjan

Chitta is pioneer and a world-renowned AI/ML author and leader. In the past 15+ years, he has built multiple successful AI/ML companies and, currently, leading an advanced ML team at Amazon. He is also an AI educator and a top-read writer.

Learn more about Chitta

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