Super Data Science: ML & AI Podcast with Jon Krohn 635: The Perils of Manually Labeling Data for Machine Learning Models
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Dec 13, 2022 Shayan Mohanty, CEO of Watchful, discusses the perils of manually labeling data for ML models with Jon Krohn. Topics include information bias, Chomsky hierarchy, simulation engines, and misconceptions about hand-labeled data. Learn about solutions to bias, the importance of automation in labeling, and the characteristics sought in new hires.
Chapters
Transcript
Episode notes
1 2 3 4 5 6 7
Intro
00:00 • 2min
Challenges and Solutions in Manual Data Labeling for Machine Learning Models
02:15 • 26min
Exploring Mathematical Foundations and Data Labeling Challenges in Machine Learning
28:03 • 3min
Efficient Data Labeling for Machine Learning Models
30:49 • 8min
Weekly Supervised Learning and Weak Supervision in Machine Learning
38:23 • 29min
High-speed implementations for data scientists
01:07:20 • 7min
The Perils of Manually Labeling Data for Machine Learning Models
01:14:11 • 4min