CoreML - Machine Learning Framework | Apple

    CoreML - Machine Learning Framework | Apple

    Core ML is a very popular machine learning framework released by Apple that runs on all Apple products like Camera, Siri, and QuickType. With Core ML developers can effectively integrate machine learning into your apps. Core ML does not require a thorough knowledge of machine learning or any neutral apps and pre-trained data models can be simply converted into Core ML model. The best feature is that it can deliver quick results by allowing you to build apps with extensive features with a few simple lines of code.

    What is Core ML?

    Core ML is basically a machine learning framework that was announced at Apple’s World Wide Developers Conference along with iOS 11. It enables to integrate several machine learning models into your apps. Core ML uses CPU & GPU for best performance as it is built on ‘Metal’ and ‘Accelerate’ two low-level technologies. As machine learning models are run on devices, so data can be analyzed on the device itself.

    Machine learning is an app that allows computers to learn without explicit programming. When the machine learning algorithm is combined with training data the result is a trained model. Apple has made it very easy for app developers to integrate different machine learning models in apps opening up new possibilities. Firstly a computer vision machine learning feature can be built into your app like face detection, face tracking, object detection, image recognition, text detection, landmarks, rectangle detection and barcode detection. Moreover, machine learning is used for natural language processing APIs to accurately understand text that uses features like language identification and entity recognition. Core ML is such an easy and simple app that takes only 10 lines of code for integration into our apps. How can you reduce the Size of your Core ML App?

    The easiest way to get started with Core ML is to bundle the machine learning model into your app. Once the models are advanced, they take up considerable storage space. Here you can reduce the footprint for a neural network model by lowering the weight parameters of precision representation. In case of a non-neutral network, you can reduce the size of your app, you can either go for half precision or add a certain functionality to compile and download your model into the device of the user as an alternative to bundling the model with the app. This conversion to half precision can tremendously reduce the size of the network, which most often comes from connecting weights in the network. Half precision tends to reduce the accuracy of floating point values along with reducing the possible values range.