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This course teaches how to build deep learning models using TensorFlow to predict rare probability events.

Understanding Deep Learning Application in Rare Event Prediction.png

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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

TensorFlow provides a simple-to-implement #key# API: Application Programming Interface #key# for constructing deep learning models. There are three general approaches:

* Sequential
* Functional
* Model subclassing

The ease of their use is in the same order. Most modeling requirements are covered by the sequential and functional approaches.

# The sequential approach
Sequential is the simplest approach. In this approach, models that have a linear stack of layers and the layers communicate sequentially are constructed. Models in which layers communicate non-sequentially (for example, residual networks) cannot be modeled with a sequential approach. Functional or model subclassing is used in such cases.

Multi-layer Perceptrons (MLPs) are sequential models. Therefore, a sequential model is initialized, as shown below.


Learn about TensorFlow’s sequential model building, from initializing layers to model fitting.

Getting Started

Rare Event Prediction

Multi-Layer Perceptrons (MLPs)

Long Short-Term Memory (LSTM) Networks

Convolutional Neural Networks (CNNs)

Autoencoders

Conclusion

Neural Network Construction

The sequential approach