May 2020
Intermediate to advanced
404 pages
10h 52m
English
Content preview from Hands-On Python Deep Learning for the Web
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Supervised learning
In this form of ML, the algorithm is presented with a huge number of training samples, which contain information about all of the parameters, or features, that would be used to determine an output feature. This output feature could be a continuous range of values or a discrete collection of labels. Based on this, supervised ML algorithms are divided into two parts:
- Classification: Algorithms that produce discrete labels in the output feature, such as normal and not normal or a set of news categories
- Regression: When the output feature has real values, for example, the number of votes a political party might receive in an election, or the temperature of a material at which it is predicted to reach its melting point
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