Apple release 'Turi Create' Machine Learning Framework on Github

Apple says Turi Create is easy to use, has a visual focus, is fast and scalable, and is flexible. Turi Create is designed to export models to Core ML for use in iOS, macOS, watchOS, and tvOS apps. From the Turi Create Github repository: 

• Easy-to-use: Focus on tasks instead of algorithms
• Visual: Built-in, streaming visualizations to explore your data
• Flexible: Supports text, images, audio, video and sensor data
• Fast and Scalable: Work with large datasets on a single machine
• Ready To Deploy: Export models to Core ML for use in iOS, macOS, watchOS, and tvOS apps

With Turi Create, for example, developers can quickly build a feature that allows their app to recognize specific objects in images. Doing so takes just a few lines of code.
With Turi Create, you can tackle a number of common scenarios:

• Recommender systems
• Image classification
• Image similarity
• Object detection
• Activity classifier
• Text classifier

You can also work with essential machine learning models, organized into algorithm-based toolkits:

• Classifiers
• Regression
• Graph analytics
• Clustering
• Nearest Neighbors
• Topic models

Supported Platforms

Turi Create supports:

• macOS 10.12+
• Linux (with glibc 2.12+)
• Windows 10 (via WSL)

System Requirements

• Python 2.7 (Python 3.5+ support coming soon)
• x86_64 architecture

Cuando se busca una camiseta de club, es útil comparar medidas, corte y detalles de confección antes de decidir. Para ordenar la búsqueda, equipacion de futbol del FC Barcelona puede revisarse junto con las medidas y el tipo de ajuste preferido. La decisión final resulta más clara cuando se comparan datos concretos y no solo la apariencia general.