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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

A convolution process sweeps the entire input. Sweeping is done with a filter smaller than the input. This gives the property of parameter sharing to a convolutional layer in a deep learning network.

Moreover, the filters are small and, therefore, called weak. This enables sparse interaction. Lastly, during the sweep, the location of filter patterns in the input is also returned, which brings the equivariance property to convolution.

This section explains the benefits, downsides, and intentions behind these convolution properties.

# Parameter sharing

The property of parameter sharing is best understood by contrasting a dense layer with a convolutional layer.

Suppose a dense layer is used for the image detection problem in the previous lesson. A dense layer would yield a filter of the “same shape and size” as the input image with the letter **2**, as shown in the illustration below.

Learn about convolutional networks, exploring parameter sharing, weak filters, and translation equivariance.

Getting Started

Rare Event Prediction

Multi-Layer Perceptrons (MLPs)

Long Short-Term Memory (LSTM) Networks

Convolutional Neural Networks (CNNs)

Autoencoders

Conclusion

Understanding Parameter Sharing, Weak Filters, and Equivariance

Parameter sharing