Download JAR files tagged by cognitive with all dependencies

This package contains Microsoft Azure Cognitive Search Management SDK. For documentation on how to use this package, please see https://aka.ms/azure-sdk-java-mgmt

This SDK allows you to use the Microsoft Cognitive Speech Service for remote meeting. See https://aka.ms/csspeech for more information.

This package contains Microsoft Azure SDK for CognitiveServices Management SDK. For documentation on how to use this package, please see https://aka.ms/azsdk/java/mgmt. Cognitive Services Management Client. Package tag package-2023-05.

This client library allows the use of Microsoft's state-of-the-art cloud-based face algorithms to detect and recognize human faces in images. See https://github.com/Microsoft/Cognitive-Face-Android for more information.

This client library allows the use of Microsoft's state-of-the-art cloud-based algorithms to process spoken language. See https://github.com/Microsoft/Cognitive-Speech-STT-Android/tree/master/Speech for more information.

This client library allows the use of Microsoft's state-of-the-art cloud-based speaker recognition algorithms to identify and verify a speaker's voice. See https://www.microsoft.com/cognitive-services/en-us/speaker-recognition-api for more information.

This client library allows the use of Microsoft's state-of-the-art cloud-based computer vision algorithms to return information based on the visual content, and to generate your ideal thumbnail. See https://github.com/Microsoft/Cognitive-Vision-Android for more information.

TD-FALCON is a fusion architecture that incorporates temporal difference methods and self-organizing neural networks for reinforcement learning with delayed rewards. It learns by creating cognitive codes across sensory input, actions, and rewards.

Azure Video Analyzer provides a platform to build intelligent video applications that span the edge and the cloud. The platform offers the capability to capture, record, and analyze live videos along with publishing the results, video, and video analytics, to Azure services in the cloud or the edge. It is designed to be an extensible platform, enabling you to connect different video analysis edge modules such as Cognitive services containers, custom edge modules built by you with open-source machine learning models or custom models trained with your own data. You can then use them to analyze live video without worrying about the complexity of building and running a live video pipeline. Use the client library for Video Analyzer Edge to simplify interactions with the Microsoft Azure IoT SDKs (https://github.com/azure/azure-iot-sdks) and programmatically construct pipeline topologies and live pipelines.