Apple Inc.

Nathan C Wertman

Alessandro Francesco Sabatelli

Alexander Barraclough Brown

Michael R Siracusa

Donald Matthew Firlik

Pierre-Olivier Latour

Dheeraj Goswami

Jacob Ty Sawyer

Embodiments of the present disclosure present devices, methods, and computer readable medium for techniques for creating machine learning models. Application developers can select a machine learning template from a plurality of templates appropriate for the type of data used in their application. Templates can include multiple templates for classification of images, text, sound, motion, and tabular data. A graphical user interface allows for intuitive selection of training data, validation data, and integration of the trained model into the application. The techniques further display a numerical score for both the training accuracy and validation accuracy using the test data. The application provides a live mode that allows for execution of the machine learning model on a mobile device to allow for testing the model from data from one or more of the sensors (i.e., camera or microphone) on the mobile device.

Techniques for machine language model creation

One embodiment of the present invention provides a system that refactors a port of a node in a graph, wherein the node has one or more input ports and one or more output ports. During operation, the system identifies an input port of the node to be refactored. The system then creates a pass-through node, wherein the value on an output port of the pass-through node equals the value on an input port of the pass-through node. Next, the system connects the output port of the pass-through node to the input port to be refactored, so that the refactored input port receives a value that is set on the input port of the pass-through node.

Method and apparatus for refactoring a graph in a graphical programming language

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Grand Junction, CO, United States of America

Nathan C Wertman