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


In this lesson, we explore time series analysis using Long Short-Term Memory (LSTM) models. We begin by importing necessary libraries, including TensorFlow for LSTM functionalities and user-defined libraries for data preprocessing and visualization. Our primary focus is on temporalizing the data, a crucial step for LSTM modeling. We'll explore the significance of temporalization and proceed to split and scale the data, setting the foundation for building robust time series models.

---

# Imports and data

We get started with importing the libraries. LSTM related classes are taken from the TensorFlow library. Also, the user-defined libraries, e.g., `datapreprocessing`, `performancemetrics`, and `simpleplots`, are imported.

Discover how to prepare and analyze time series data with LSTMs, from importing libraries to data temporalization and scaling.

Getting Started

Rare Event Prediction

Multi-Layer Perceptrons (MLPs)

Long Short-Term Memory (LSTM) Networks

Convolutional Neural Networks (CNNs)

Autoencoders

Conclusion

Preparing and Analyzing Time Series Data with LSTM Models

Imports and data