What are the key differences between supervised and unsupervised Machine Learning?
The main difference between supervised and unsupervised Machine Learning is the presence of labeled data in supervised learning, where the algorithm learns from a pre-existing dataset with predefined labels. On the other hand, unsupervised learning deals with unlabeled data, where the algorithm tries to identify patterns and relationships without any predefined labels.
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Supervised learning involves training a model with labeled data to make predictions or classifications based on that knowledge. Unsupervised learning, however, focuses on finding hidden patterns or structures in the data without any predefined labels or targets.
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Supervised learning relies on a known output variable for every input, while unsupervised learning explores the data to find meaningful patterns or groups without any previously known outputs to guide the process.
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Machine Learning
2024-08-08 19:43:48
What are some common challenges in training deep neural networks?
