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.
We haven't tracked posts mentioning Tensorflow-iOS yet.
Tracking mentions began in Dec 2020.
Are people using Swift for machine learning / differentiable programming outside of Apple?
4 projects | reddit.com/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+)
AIToolbox - A toolbox of AI modules written in Swift: Graphs/Trees, Support Vector Machines, Neural Networks, PCA, K-Means, Genetic Algorithms
SpriteKit+Spring - SpriteKit API reproducing UIView's spring animations with SKAction
PDFGenerator - A simple generator of PDF written in Swift.
BrainCore - The iOS and OS X neural network framework
EVURLCache - a NSURLCache subclass for handling all web requests that use NSURLRequest
MLKit - A simple machine learning framework written in Swift 🤖
DL4S - Accelerated tensor operations and dynamic neural networks based on reverse mode automatic differentiation for every device that can run Swift - from watchOS to Linux
Awesome-Mobile-Machine-Learning - A curated list of awesome mobile machine learning resources for iOS, Android, and edge devices.
protobuf-swift - Google ProtocolBuffers for Apple Swift