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About Semi-: Supervised Learning
Semi-supervised learning uses both labeled and unlabeled data for training machine learning models. It aims to produce better models by leveraging a large amount of unlabeled data along with a typically smaller amount of labeled data. Active learning is a type of semi-supervised learning where the model selects the most informative unlabeled examples to be labeled by an oracle like a human to iteratively improve the model with less labeled data. While promising, active learning has not been widely adopted in industry due to practical challenges around infrastructure, software support, and long training times of modern deep learning models.
Detailed Information
| Author: | ['Rajachandra Voodiga'] |
|---|---|
| Publication Year: | 2022 |
| Pages: | 40 |
| Language: | English |
| Format: | |
| Price: | FREE |
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