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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 neural network training, extreme feature values can lead to issues with vanishing or exploding gradients, making the learning process challenging. While activation function choices can help mitigate these problems, batch normalization is another powerful technique to consider.

You’re tasked with implementing batch normalization in a neural network model to address the potential issues caused by extreme feature values.

# What’s batch normalization?
The feature map effect is addressed using batch normalization, batch normalization helps a decoder by preventing the reconstruction from exploding.


# How to apply batch normalization
Batch normalization is applied between the layers. The syntax for batch normalization is as follows:




Learn how to integrate batch normalization in neural network models to address vanishing and exploding gradients. 

Getting Started

Rare Event Prediction

Multi-Layer Perceptrons (MLPs)

Long Short-Term Memory (LSTM) Networks

Convolutional Neural Networks (CNNs)

Autoencoders

Conclusion

Exercise: Applying Batch Normalization in Neural Networks

What’s batch normalization?

How to apply batch normalization