Lilian Weng • 2/20/2022

Learning with not Enough Data Part 2: Active Learning

Read Original

This technical article delves into active learning, a method for efficiently improving machine learning models with limited labeled data. It defines key concepts, notations, and acquisition functions like uncertainty sampling, margin score, and entropy to intelligently select which unlabeled samples to annotate within a fixed budget, with a focus on deep neural models.

0 comments
#Machine Learning #Deep Learning #Supervised Learning
Learning with not Enough Data Part 2: Active Learning

Comments

No comments yet

Be the first to share your thoughts!

Browser Extension

Get instant access to AllDevBlogs from your browser

Top of the Week

1
React, useEffect, and Stale Closures: How to Avoid Them with Dependencies and useRef
Jivbcoop • 4 votes
2
Better react-hook-form Smart Form Components
Maarten Hus • 2 votes
3
AGI, ASI, A*I – Do we have all we need to get there?
John D. Cook • 1 votes
4
Quoting Thariq Shihipar
Simon Willison • 1 votes
5
Dew Drop – January 15, 2026 (#4583)
Alvin Ashcraft • 1 votes
6
Using Browser Apis In React Practical Guide
Jivbcoop • 1 votes