AI-powered learning
Save this course
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.
4.3
59 Lessons
8h
Join 2.9 million developers at
Join 2.9 million developers at
LEARNING OBJECTIVES
- 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
Learning Roadmap
1.
Getting Started
Getting Started
Get familiar with applying deep learning for predicting rare, impactful events with tailored evaluation metrics.
2.
Rare Event Prediction
Rare Event Prediction
Unpack the core of statistical foundations, prediction strategies, and spatio-temporal challenges in rare event prediction.
3.
Multi-Layer Perceptrons (MLPs)
Multi-Layer Perceptrons (MLPs)
12 Lessons
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
Long Short-Term Memory (LSTM) Networks
11 Lessons
11 Lessons
Enhance your skills in LSTM networks for advanced sequential data modeling and prediction.
5.
Convolutional Neural Networks (CNNs)
Convolutional Neural Networks (CNNs)
17 Lessons
17 Lessons
Take a look at CNNs for efficient high-dimensional data handling, spatial feature extraction, and time series modeling.
6.
Autoencoders
Autoencoders
8 Lessons
8 Lessons
See how it works to build and optimize diverse autoencoders for anomaly detection and feature extraction.
Certificate of Completion
Showcase your accomplishment by sharing your certificate of completion.
Complete more lessons to unlock your certificate
Developed by MAANG Engineers
ABOUT THIS COURSE
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.
ABOUT THE AUTHOR
Chitta Ranjan
Chitta is pioneer and a world-renowned AI/ML author and leader. In the past 15+ years, he has built multiple successful AI/ML companies and, currently, leading an advanced ML team at Amazon. He is also an AI educator and a top-read writer.
Trusted by 2.9 million developers working at companies
A
Anthony Walker
@_webarchitect_
E
Evan Dunbar
ML Engineer
S
Software Developer
Carlos Matias La Borde
S
Souvik Kundu
Front-end Developer
V
Vinay Krishnaiah
Software Developer
Built for 10x Developers
No Passive Learning
Learn by building with project-based lessons and in-browser code editor
Personalized Roadmaps
The platform adapts to your strengths & skills gaps as you go
Future-proof Your Career
Get hands-on with in-demand skills
AI Code Mentor
Write better code with AI feedback, smart debugging, and "Ask AI"
MAANG+ Interview Prep
AI Mock Interviews simulate every technical loop at top companies
Free Resources
