Programmatically building and managing training datasets with Snorkel
Paroma Varma teaches you how to build and manage training datasets programmatically with Snorkel, an open source framework developed at the Stanford AI Lab, and demonstrates how this can lead to more efficiently building and managing ML models in a range of practical settings.
You’ll learn how to use Snorkel to programmatically label, transform (or augment), and slice training datasets in order to train a downstream ML model, using an extended version of the open source tutorial on using Snorkel to train a spam classifier over YouTube comments. Paroma offers a high-level overview of other Snorkel features, including applications to other data modalities and ML problems, an outlook on next step developments for Snorkel, and brainstorms use cases of Snorkel.
- A basic knowledge of supervised ML
Materials or downloads needed in advance
- A WiFi-enabled laptop
What you'll learn
- Learn how to use Snorkel to programmatically build and manage training datasets for ML
- Understand various modern ML topics supported by and/or used in Snorkel including weak supervision, data augmentation, and data slicing
Paroma Varma is a cofounder at Snorkel and completed a PhD at Stanford, advised by Professor Christopher Ré and affiliated with the DAWN, SAIL, and StatML groups, where she was supported by the Stanford Graduate Fellowship and the National Science Foundation Graduate Research Fellowship. Her research interests revolve around weak supervision or using high-level knowledge in the form of noisy labeling sources to efficiently label massive datasets required to train machine learning models.
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