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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.

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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

Anomaly detection is unarguably one of the best approaches in rare event detection problems. Especially, if the event is so rare that there are insufficient samples to train a classifier. Fortunately, an (extremely) rare event often appears as an anomaly in a process. They can, therefore, be detected due to their abnormality.


Petsche et al. 1996 have one of the early works in deep learning which developed anomaly detectors for rare event detection. They developed an **autoassociator** to detect an imminent motor failure.

Discover how autoencoders excel in detecting rare events by analyzing reconstruction errors to differentiate between normal and anomalous states.

Getting Started

Rare Event Prediction

Multi-Layer Perceptrons (MLPs)

Long Short-Term Memory (LSTM) Networks

Convolutional Neural Networks (CNNs)

Autoencoders

Conclusion

Anomaly Detection with Autoencoders