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

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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.

Understanding Deep Learning Applications in Rare Event Prediction

Identifying the unique challenges in rare event prediction not only sharpens our modeling approach but also directly impacts the effectiveness of our solutions in real-world scenarios.

The step after formulating a problem is to identify the modeling challenges. Challenges, if identified, enable a better modeling direction. It tells a practitioner the issues to address during the modeling.

A few acute challenges posed by a rare event problem are:

* High-dimensional multivariate time series process
* Early prediction
* Imbalanced data


Explore modeling challenges in rare event prediction, including data imbalance and early prediction. 

Tackling Spatio-Temporal Challenges

Explore modeling challenges in rare event prediction, including data imbalance and early prediction.

Getting Started

Rare Event Prediction

Multi-Layer Perceptrons (MLPs)

Long Short-Term Memory (LSTM) Networks

Convolutional Neural Networks (CNNs)

Autoencoders

Conclusion

Tackling Spatio-Temporal Challenges