palle-k/DL4S
SwiftMITactive
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Accelerated tensor operations and dynamic neural networks based on reverse mode automatic differentiation for every device that can run Swift - from watchOS to Linux
Health Breakdown
Activity25
Community25
Maintenance14
Popularity24
#autograd#automatic-differentiation#convolutional-neural-networks#deep-learning#deep-neural-networks#derivatives#gradient-descent#machine-learning#neural-networks#optimizers#recurrent-networks#recurrent-neural-networks#swift#swift-machine-learning#tensor
Should you contribute to palle-k/DL4S?
palle-k/DL4S has a FoundDev health score of 88/100, which puts it in the active-and-maintained tier. The maintainer team is shipping recently, issues are being closed, and a PR you open this week has a realistic chance of being reviewed.
Last push was 0 days ago — that signals an actively maintained project. New issues are likely to get a maintainer response within days. The project is written primarily in Swift, so prior Swift experience will shorten ramp-up.
Licensed under MIT, a standard OSI-approved license — safe to contribute to under normal employer IP policies.
Community
SwiftMIT
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0d ago
