4.7
Advanced
8h
Understanding Deep Learning Applications in Rare Event Prediction
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.
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.
This course aims to provide a practical understanding of the key constructs of deep learning, including Multi-layer Perceptrons,...Show More
WHAT YOU'LL LEARN
An understanding of deep learning fundamentals, including Multi-layer Perceptrons, LSTMs, convolutional neural networks, and autoencoders
Hands-on experience modeling rare events
The ability to build and train deep learning models using TensorFlow that predict rare events
Hands-on experience implementing LSTMs for capturing long-term dependencies in sequential data
An understanding of deep learning fundamentals, including Multi-layer Perceptrons, LSTMs, convolutional neural networks, and autoencoders
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Content
1.
Getting Started
6 Lessons
Get familiar with applying deep learning for predicting rare, impactful events with tailored evaluation metrics.
2.
Rare Event Prediction
3 Lessons
Unpack the core of statistical foundations, prediction strategies, and spatio-temporal challenges in rare event prediction.
3.
Multi-Layer Perceptrons (MLPs)
12 Lessons
Examine foundational concepts, data preparation, neural network construction, regularization, custom functions, and advanced metrics in MLPs.
4.
Long Short-Term Memory (LSTM) Networks
11 Lessons
Enhance your skills in LSTM networks for advanced sequential data modeling and prediction.
5.
Convolutional Neural Networks (CNNs)
17 Lessons
Take a look at CNNs for efficient high-dimensional data handling, spatial feature extraction, and time series modeling.
6.
Autoencoders
8 Lessons
See how it works to build and optimize diverse autoencoders for anomaly detection and feature extraction.
7.
Conclusion
1 Lessons
Build on your mastery of deep learning techniques in rare event prediction.
Certificate of Completion
Showcase your accomplishment by sharing your certificate of completion.
Course Author:
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