JuliaML / LossFunctions.jl
Julia package of loss functions for machine learning.
Why this job: works in the areas we cover, but does not say which job it does
What it does
This package provides a considerable amount of carefully implemented loss functions, as well as an API to query their properties (e.g. convexity). Furthermore, we expose methods to compute their values, derivatives, and second derivatives for single observations as well as arbitrarily sized arrays of observations. In the case of arrays a user additionally has the ability to define if and how element-wise results are averaged or summed over.
Get started
No single install command. Read the steps in the README (opens in new tab)
- Maintenance
- Active
- Last commit 46 days ago
- Stars
- 153
- On GitHub
- Licence
- Custom
- Read before business use
- Downloads for
- None listed
- No release files
- Latest version
- v1.2.2
- Released 4 months ago
- Built with
- Julia
- 100% of the code
Is it looked after?
- Contributors
- Not stated
- Last 90 days
- Issues
- 99% closed
- 1 still open
- Pull requests
- 99 merged
- 0 waiting
- Releases
- 30
- 3 in the last year
- No contributing guide
- No security policy
- No code of conduct
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More: languages, topics and project record
Languages
- Julia 100%
Topics
- classification
- julia
- loss-functions
- machine-learning
- regression
Project record
- JobOther AI tools
- StatusListed
- Commits533
- Forks36
- Watching7
- Stars in 28 days0
- CreatedOct 11, 2015
- First seen by usNov 7, 2026
- CheckedNov 8, 2026