binary cross entropy
Type: Concept
As of November 2026, binary cross entropy has been mentioned in one podcast episode on Carnegie Mellon University Deep Learning (Jan 2026). Binary cross entropy is presented as a specialized form of cross entropy loss for binary classification problems.
In information theory, the cross-entropy between two probability distributions and , over the same underlying set of events, measures the average number of bits needed to identify an event drawn from the set when the coding scheme used for the set is optimized for an estimated probability distribution , rather than the true distribution .
| Mentioned in | 1 episode on 1 podcast (as of November 2026) |
|---|---|
| First heard | Jan 2026 on Carnegie Mellon University Deep Learning |
| Field | Machine Learning |
Who talks about binary cross entropy on podcasts?
binary cross entropy came up; the most recent mention was Jan 2026 on Carnegie Mellon University Deep Learning.
- 0.20 Losses part1 on Carnegie Mellon University Deep Learning (Jan 2026)