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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

 The cell input-output and the operations here were explained. In this section, the view is zoomed out of the cell, and the network operations at the layer level are explained.

The illustrations below bring an LSTM layer’s internal and external connections into perspective. It provides visibility of the layer’s input-output mechanism. The illustrations provide an understanding of stacking layers around an LSTM layer and the way they interact.



Explore LSTM layer operations, stateful vs. stateless modes, and sequence handling in network architectures.

Getting Started

Rare Event Prediction

Multi-Layer Perceptrons (MLPs)

Long Short-Term Memory (LSTM) Networks

Convolutional Neural Networks (CNNs)

Autoencoders

Conclusion

LSTM Layer Operations and Network Architectures