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Black-box testing and oracles Black-box testing of machine learning (ML) models refers to testing with no knowledge about the internal details of the model, such as the algorithm used to create it and the features in it. The main objective of black-box testing is to ensure the quality of the...
Ensuring model quality In the context of machine learning, the goal of testing is to ensure the model is performing accurately. Although testing machine learning models is different from testing conventional software, the same design techniques are applicable. The following pages describe...
Steps for using feeds from front and rear cameras for prediction Following the steps below, you can set up a multi-camera stream with model prediction on a device running a Snapdragon® mobile platform. Using live frames in the model The procedure streams live frames to the machine learning...
Using a hardware development kit and external camera for object detection and location The Qualcomm® Neural Processing Engine (NPE) is a runtime for the execution of deep neural networks. The engine is accelerated through its tight integration with a Snapdragon® mobile platform. The following...
Parameters, hyperparameters, regularization and optimization In the process of training deep learning models in frameworks compatible with the Qualcomm® Neural Processing SDK, it is a good idea to improve models through tuning and hyperparameter optimization. Why? In any machine learning (ML)...
Tips for developers working on machine learning apps on Android 1. Model training and conversion Machine learning frameworks have specific formats for storing neural network models. The Qualcomm® Neural Processing SDK includes tools for converting pre-trained models to the Deep Learning...
Mobile app development with machine learning models The Qualcomm® Neural Processing SDK for AI is a tool to optimize performance of trained neural networks on Snapdragon® mobile platforms. To begin working with the SDK, download it from the Tools...
How to build and run a sample AI application from Android Studio Setting up the AI Android project The steps below are designed to prepare you to develop an AI-based Android mobile app using Android Studio. First, follow the “Install Android Studio” guide on the Android developer site. It will...
Using MobileNet SSD model for object detection It is necessary to convert Caffe- and TensorFlow-based models supported by the Snapdragon® Mobile Platform .dlc (Deep Learning Container) format before running them on the Qualcomm® Neural Processing SDK for AI. To understand some of the issues...
Machine learning inference on edge devices The Qualcomm® Neural Processing SDK for AI is designed for converting and executing deep neural networks on the Snapdragon® mobile platform without connecting to the cloud. The SDK is built for heterogeneous computing and running trained neural networks...
Machine learning on edge devices Instead of running inference workloads in the cloud, the future belongs to apps that run them on edge devices like smartphones and drones. The Qualcomm® Neural Processing SDK for AI is deep learning software for the Snapdragon® mobile platform. It allows...
Preparing data for use in machine learning models and deep learning Whether they are new to deep learning or looking for a refresher, mobile app developers find that QDN blog posts are a good introduction to AI and machine learning (ML). Posts like Mobile AI Through Machine Learning Algorithms...
Mobile AI through machine learning algorithms Machine learning (ML) is a method of artificial intelligence (AI) in which data is used to train a machine so that it can make decisions or predictions on its own. There are four main categories of ML algorithms: Supervised machine learning...
Using an IDE for mobile apps with the Qualcomm Neural Processing SDK for AI The use of Artificial Intelligence (AI) is growing rapidly in areas like electronics, aerospace, automotive, finance and consumer services. The increasing power of smartphones is a strong factor in bringing AI to mobile...
On this page and subsequent sub-pages you will find a number of engineering sourced resources to help you with your artificial intelligence development using the Qualcomm® Neural Processing SDK. The topics included are as follows: Artificial Intelligence, Machine Learning, Android and the...
The Qualcomm® Neural Processing SDK allows developers to convert neural network models trained in Caffe/Caffe2, ONNX, or TensorFlow and run them optimally on Snapdragon® mobile platforms. Below, we show you how to set up the Qualcomm Neural Processing SDK and use it to build your first working...

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