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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

# Bidirectional

A regular LSTM, or any RNN, learns the temporal patterns in a forward direction going from the past to the future. Meaning that at any time-step, the cell state learns only from the past. It doesn’t have visibility of the future.
This concept is clearer in the illustration below. As shown in the illustration, the truck of memories (cell state) moves from left to right.




Explore how bidirectional LSTM networks leverage past and future data insights for superior temporal pattern recognition. 

Bidirectional LSTM for Enhanced Temporal Learning

Explore how bidirectional LSTM networks leverage past and future data insights for superior temporal pattern recognition.

Getting Started

Rare Event Prediction

Multi-Layer Perceptrons (MLPs)

Long Short-Term Memory (LSTM) Networks

Convolutional Neural Networks (CNNs)

Autoencoders

Conclusion

Bidirectional LSTM for Enhanced Temporal Learning

Bidirectional