机器学习 学习的本质：抽象与模型化
You train a model to recognize patterns by showing it representative samples. For example, you can train a model to recognize dogs by showing it lots of images of different dogs. After you’ve trained the model, you test it out on data it hasn’t seen before, and evaluate how well it performed the task. When the model is performing well enough, you’re ready to integrate it into your app using Core ML.
Create ML leverages the machine learning infrastructure built in to Apple products like Photos and Siri. This means your image classification and natural language models are smaller and take much less time to train.
Overview
With Core ML, you can integrate trained machine learning models into your app.
A trained model is the result of applying a machine learning algorithm to a set of training data. The model makes predictions based on new input data. For example, a model that's been trained on a region's historical house prices may be able to predict a house's price when given the number of bedrooms and bathrooms.
Core ML is the foundation for domain-specific frameworks and functionality. Core ML supports Vision for image analysis, Natural Language for natural language processing, and GameplayKit for evaluating learned decision trees. Core ML itself builds on top of low-level primitives like Accelerate and BNNS, as well as Metal Performance Shaders.
Core ML is optimized for on-device performance, which minimizes memory footprint and power consumption. Running strictly on the device ensures the privacy of user data and guarantees that your app remains functional and responsive when a network connection is unavailable.
https://developer.apple.com/documentation/coreml