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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

In this exercise, we'll explore diverse activation functions for the LSTM model. While our initial training involved the `relu` activation function, we’ll now experiment with the `selu`, `elu`, and `tanh` activations.


# Loading the data

To begin with, let’s initiate this exciting task by importing the necessary libraries and then loading our dataset. This dataset that we’re going to be working on is a CSV file named `processminer-sheet-break-rare-event-dataset.csv`. 

Import the necessary libraries and load the dataset in the following widget:


Learn how to use activation functions in LSTM models and the impact of different activation functions on these models. 

Getting Started

Rare Event Prediction

Multi-Layer Perceptrons (MLPs)

Long Short-Term Memory (LSTM) Networks

Convolutional Neural Networks (CNNs)

Autoencoders

Conclusion

Exercise: Exploring Activation Functions in LSTM Models

Loading the data