activation_function

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.

Understanding Deep Learning Application in Rare Event Prediction.png

DLAA.tar.gz

key1

UDL1

UDL2

UDL1-copy

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



# Sparse autoencoder construction

This lesson shows that sparse autoencoders can learn useful features and help improve classification tasks.

A sparse autoencoder described earlier is constructed here. The model is overcomplete with the encoding dimension equal to the input dimension.

The sparsity regularization is imposed on the encodings by setting `activity_regularizer=tf.keras.regularizers.L1(l1=0.01)` in the model construction in the code below.


Learn to build sparse autoencoders for improved feature extraction and classification in anomaly detection tasks.

Getting Started

Rare Event Prediction

Multi-Layer Perceptrons (MLPs)

Long Short-Term Memory (LSTM) Networks

Convolutional Neural Networks (CNNs)

Autoencoders

Conclusion

Building Sparse Autoencoders

Sparse autoencoder construction