iOS-App-Design-Guidelines
Swift-AI
| iOS-App-Design-Guidelines | Swift-AI | |
|---|---|---|
| - | 1 | |
| 0 | 6,012 | |
| - | 0.3% | |
| 3.6 | 0.0 | |
| 7 months ago | about 7 years ago | |
| Swift | ||
| - | MIT License |
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iOS-App-Design-Guidelines
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Tracking mentions began in Dec 2020.
Swift-AI
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Are people using Swift for machine learning / differentiable programming outside of Apple?
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+)
helloworld - Explore the world of iOS app development with our 'Hello World' project in Swift. This introductory project is perfect for beginners who want to take their first steps in building mobile applications for Apple devices.
AIToolbox - A toolbox of AI modules written in Swift: Graphs/Trees, Support Vector Machines, Neural Networks, PCA, K-Means, Genetic Algorithms
OpenAI - Swift community driven package for OpenAI public API
BrainCore - The iOS and OS X neural network framework
MLKit - A simple machine learning framework written in Swift 🤖
PDFGenerator - A simple generator of PDF 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
Tensorflow-iOS
SpriteKit+Spring - SpriteKit API reproducing UIView's spring animations with SKAction