Semi-supervised-learning
Semi-supervised learning falls between unsupervised learning (without any labeled training data) and supervised learning (with completely labeled training data). Some of the training examples are missing training labels, yet many machine-learning researchers have found that unlabeled data, when used in conjunction with a small amount of labeled data, can produce a considerable improvement in learning accuracy. In weakly supervised learning, the training labels are noisy, limited, or imprecise; however, these labels are often cheaper to obtain, resulting in larger effective training sets.[42]
How to download and setup Semi-supervised-learning
Open terminal and run command
git clone https://github.com/Aryia-Behroziuan/Semi-supervised-learning.git
git clone is used to create a copy or clone of Semi-supervised-learning repositories.
You pass git clone a repository URL. it supports a few different network protocols and corresponding URL formats.
Also you may download zip file with Semi-supervised-learning https://github.com/Aryia-Behroziuan/Semi-supervised-learning/archive/master.zip
Or simply clone Semi-supervised-learning with SSH
[email protected]:Aryia-Behroziuan/Semi-supervised-learning.git
If you have some problems with Semi-supervised-learning
You may open issue on Semi-supervised-learning support forum (system) here: https://github.com/Aryia-Behroziuan/Semi-supervised-learning/issuesSimilar to Semi-supervised-learning repositories
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