Synthetic Data for Edge Cases
This is a free preview video from the Synthetic Data: Advanced Concepts and Applications Course: / unlock-the-power-of-synthetic-data
We use data to make decisions, understand trends, and optimize processes. And data is a key component of machine learning. But collecting data that has the quality, quantity, and diversity that you need for your machine learning use case can be time-consuming and difficult. In this course, discover how you can use synthetic data—artificially generated information, not data collected from real world events—for machine learning. Learn how to generate synthetic data, how to combine it with real data, and important differences between synthetic and real data. Get tips and tricks to optimize your training performance, and find out how to recognize synthetic data problems. Check out this course to learn how to recognize when synthetic data is needed, how to select data generation methods, and leverage various model-training strategies.
00:00: What is a robust dataset?
00:27: Real data lacks enough rare classes and edge cases (healthcare and PII)
00:47: Identify edge cases and rare classes in real data
01:02: Check that real data is representative of real-world scenarios with minimal domain gaps
01:15: Make sure your model validation process has enough real data edge cases in the test set
01:28: Don't let your machine learning model that all synthetic data is an edge case
1:40: Synthetic data can be iteratively improved over time to account for more edge cases
If you want to learn about image augmentation for deep learning (synthetic data for deep learning), check out A Data Odyssey's video: • Image Augmentation for Deep Learning ...
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