Machine Learning

Sequence Intent Classification Using Hierarchical Attention Networks

March 6, 2018 Mar 6, 2018 03/6/18

We analyze how Hierarchical Attention Neural Networks could be helpful with malware detection and classification scenarios, demonstrating the usefulness of this approach for generic sequence intent analysis. The novelty of our approach is in applying techniques that are used to discover structure in a narrative text to data that describes the behavior of executables.

Giving meaningful context to social media influence with Microsoft Cognitive Services

December 7, 2017 Dec 7, 2017 12/7/17

Rachel Weil

When it comes to identifying and understanding influential voices in social media, context is key. See how we used topic detection, sentiment analysis, and Azure Functions to automate context-aware social media insights.

Comparing Image-Classification Systems: Custom Vision Service vs. Inception

December 5, 2017 Dec 5, 2017 12/5/17

Clemens Wolff

This story covers how to get started with transfer-learning and build image classification models in Python with the Custom Vision Service. We compare the results with the popular Tensorflow-based models Inception and MobileNet.

Stock Market Predictions with Natural Language Deep Learning

December 4, 2017 Dec 4, 2017 12/4/17

Patty Ryan

We developed a deep learning model using a one-dimensional convolutional neural network to predict future stock market performance of companies using Azure Machine Learning Workbench and Keras.

Autoscaling Deep Learning Training with Kubernetes

November 21, 2017 Nov 21, 2017 11/21/17

Rita Zhang

We explore how we worked with a customer to add autoscaling capability to a Kubernetes cluster to meet bursty demands for deep learning training in a cost-efficient manner.

Permissively-Licensed Named Entity Recognition on the JVM

November 20, 2017 Nov 20, 2017 11/20/17

Clemens Wolff

The ability to correctly identify entities, such as places, people, and organizations, adds a powerful level of natural language understanding to applications. This post introduces a MIT-licensed one-click deployment to Azure for web services that lets developers get started with a wide range of natural language tasks in 5 minutes or less, by consuming simple HTTP services for language identification, tokenization, part-of-speech-tagging and named entity recognition.

Building a Custom Spark Connector for Near Real-Time Speech-to-Text Transcription

November 1, 2017 Nov 1, 2017 11/1/17

Clemens Wolff

This post describes in detail the Azure Cognitive Services speech-to-text WebSocket protocol and shows how to implement the protocol in Java. This enables us to transcribe audio to text in near real-time. We then show how to feed the transcribed radio into a pipeline based on Spark Streaming for further analysis, augmentation, and aggregation. The Java client is reusable across a wide range of text-to-speech scenarios that require time-efficient speech-to-text transcription in more than 10 languages including English, French, Spanish, German and Chinese.

Bird Detection with Azure ML Workbench

October 24, 2017 Oct 24, 2017 10/24/17

Olga Liakhovich

We demonstrate how to train Object Detection models using CNTK and Tensoflow DNN frameworks. Azure ML Workbench is used as the main training and model hosting infrastructure.

Developing a Custom Search Engine for an Expert Chat System

August 7, 2017 Aug 7, 2017 08/7/17

Mona Soliman Habib

We address the challenge of creating a custom search experience for a specific domain area. We also provide a guide for creating your own custom search experience by leveraging Azure Search and Cognitive Services and sharing custom code for iterative testing, measurement and indexer redeployment.

Using Object Detection for Complex Image Classification Scenarios

July 31, 2017 Jul 31, 2017 07/31/17

Ari Bornstein

An overview of different image classification approaches including Microsoft Azure Custom Vision Service and CNTK for various levels of classification complexity.

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