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  2. برمجيات حسان

Deep Learning: 02: Why Neural Nets Work?

Опубликовано: 24 Март 2026
на канале: برمجيات حسان
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Linear separability is a problem in machine learning that refers to the ability of a set of data points to be separated into distinct categories using a linear decision boundary. If a set of data points can be separated by a straight line, then it is said to be linearly separable. However, not all sets of data points are linearly separable, and there are a number of ways to deal with this problem.

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