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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

As we’ve seen throughout this lesson, 
a variety of architectures serve distinct purposes, from dimensionality reduction to denoising. Here's a concise introduction to some key rules of thumb for constructing effective autoencoders.

# Autoencoder constructions

* Add unit-norm constraint on the weights. This prevents ill-conditioning of the model.

* Add a linearly activated dense layer at the end of the encoder and decoder for calibration in most autoencoders.

* The activation on the decoder output layer should be based on the range of the input. For example, `linear` if the input $x$ is in $(−∞, ∞)$ (scaled with `StandardScaler`) or `sigmoid` if $x$ is in ($0$, $1$) (scaled with `MinMaxScaler`).



Learn to optimize autoencoders with constraints and tailored layers for tasks from denoising to sequence translation.

Getting Started

Rare Event Prediction

Multi-Layer Perceptrons (MLPs)

Long Short-Term Memory (LSTM) Networks

Convolutional Neural Networks (CNNs)

Autoencoders

Conclusion

Optimizing Autoencoder Architectures

Autoencoder constructions