Understanding Deep Learning Applications in Rare Event Prediction

Understanding Deep Learning Applications in Rare Event Prediction

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

Advanced

59 Lessons

8h

Certificate of Completion

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

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Explanations

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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

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Course Content

1.

Getting Started

This chapter introduces predictive patterns, rare event prediction, and solutions for imbalanced data, including practical challenges.
2.

Rare Event Prediction

This chapter covers statistical foundations and prediction strategies for rare events, addresses spatio-temporal challenges, and concludes with a quiz.
3.

Multi-Layer Perceptrons (MLPs)

This chapter covers MLP basics, data prep, dropout, custom functions, and metrics, with insights into networks, optimization, and batch normalization.
4.

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.
5.

Convolutional Neural Networks (CNNs)

This chapter explores CNNs, covering convolution basics, parameter sharing, pooling layers, and kernel operations.
6.

Autoencoders

8 Lessons

This chapter covers autoencoders and their use as PCA alternatives, anomaly detection, diverse applications, and how to optimize sparse and autoencoders.
7.

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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