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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 the previous lesson, we constructed convolutional neural networks following the conventional approach of placing the temporal axis along a spatial axis and the multivariate features as channels.
Another approach is placing the features along a second spatial axis. This can be done by reshaping the input samples from `(timesteps, features)` to `(timesteps, features, 1)` tensors. The reshaping is explained in the illustration below. The reshaped time series appears in the shape of an image with a single channel. Therefore, this approach is termed as modeling a multivariate time series as an image.

In the reshaped time series sample, the `timesteps` and `features` are the spatial axes with one channel. Due to two spatial axes, the convolutional network is constructed with `Conv2D`.

First, we construct a `Conv2D` network equivalent to the baseline `Conv1D` network to show their interchangeability. Next, we construct another network with `Conv2D`, which is intended to learn the “local” temporal and spatial dependencies in and between the features.

# `Conv1D` and `Conv2D` equivalence

Let’s discuss the equivalence between `Conv1D` and `Conv2D` by modeling the multivariate time series like an image.

In the code below, a convolutional network equivalent to the baseline network is constructed using `Conv2D`. At its top, a lambda function to reshape the original samples is defined. It changes `X` from `(samples, timesteps, features)` to `(samples, timesteps, features, 1)` tensor.

Explore reshaping multivariate time series for Conv2D network modeling to capture complex spatial and temporal dependencies.

Getting Started

Rare Event Prediction

Multi-Layer Perceptrons (MLPs)

Long Short-Term Memory (LSTM) Networks

Convolutional Neural Networks (CNNs)

Autoencoders

Conclusion

Modeling Multivariate Time Series