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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

# Unrestricted LSTM network

There are two choices in LSTM  networks: return sequences vs. return last output at the final LSTM layer. So far, we’re working with the default choice used in the baseline restricted model above. In this lesson, we’ll work on how we can improve the model.

The former choice enables the ultimate LSTM layer to emit a sequence of hidden outputs. Since the last LSTM layer is not restricted to emitting only the final hidden output, this network is called an “unrestricted” LSTM network. A potential benefit of this network is the presence of more intermediate features.
Based on this hypothesis, an unrestricted network is constructed in the code below.

Learn to enhance LSTM models with unrestricted sequences, dropout variations, and reverse processing for improved time series predictions.

Getting Started

Rare Event Prediction

Multi-Layer Perceptrons (MLPs)

Long Short-Term Memory (LSTM) Networks

Convolutional Neural Networks (CNNs)

Autoencoders

Conclusion

Unrestricted LSTM with Dropouts and Backward Input

Unrestricted LSTM network