Machine learning applications generally rely on supervised learning, learning from training samples that have been labeled by a human ‘teacher’. Unsupervised learning learns what it can from unlabeled training samples. What can be learned this way are basic structural characteristics of the training data, and this information can be a useful aid to supervised learning.
In my latest iMerit blog I describe how the long-used technique of clustering has been incorporated into deep learning systems, to provide a useful starting point for supervised learning and to extrapolate what is learned from labeled training data.
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