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|over 5 years ago||almost 6 years ago|
|MIT License||MIT License|
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We haven't tracked posts mentioning CoreML-samples 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
Awesome-Mobile-Machine-Learning - A curated list of awesome mobile machine learning resources for iOS, Android, and edge devices.
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