|about 3 years ago||over 6 years ago|
|Apache License 2.0||MIT License|
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Are people using Swift for machine learning / differentiable programming outside of Apple?
4 projects | /r/swift | 25 Jun 2021
Before CoreML was released (and also before BNNS), I wrote SwiftAI. The project made heavy use of Accelerate and the algorithms were written from scratch. It was a great project, and I planned to make a lot of additions, but CoreML came out pretty soon afterward and made the project mostly obsolete. Still a great learning experience and there was a fair bit of interest at the time.
What are some alternatives?
CoreML-Models - Largest list of models for Core ML (for iOS 11+)
Alamofire - Elegant HTTP Networking in Swift
Moya - Network abstraction layer written in Swift.
AIToolbox - A toolbox of AI modules written in Swift: Graphs/Trees, Support Vector Machines, Neural Networks, PCA, K-Means, Genetic Algorithms
BrainCore - The iOS and OS X neural network framework
swift-protobuf - Plugin and runtime library for using protobuf with Swift
PDFGenerator - A simple generator of PDF written in Swift.
SpriteKit+Spring - SpriteKit API reproducing UIView's spring animations with SKAction
CocoaAsyncSocket - Asynchronous socket networking library for Mac and iOS
AFNetworking - A delightful networking framework for iOS, macOS, watchOS, and tvOS.
Benchmark - The Benchmark⏲ module provides methods to measure and report the time used to execute Swift code.