At: UnitedHealth GroupLocation: Minnetonka, MN Web: www.unitedhealthgroup.comPosition: Director, Omni-Channel Analytics

I’ve posted several examples here of using Azure Cognitive Services for data science applications. You can upload an an image or video to the service and extract information about faces and emotions, generate a caption describing a scene from a provided photo, or speak written text in a natural voice. (If you haven’t tried the Cognitive Services tools yet, you can try them out using the instructions in this notebook using only a browser.)

MEKA The MEKA project provides an open source implementation of methods for multi-label learning and evaluation. In multi-label classification, we want to predict multiple output variables for each input instance. This different from the ‘standard’ case (binary, or multi-class classification) which involves only a single target variable. MEKA is based on the WEKA Machine Learning Toolkit; it includes dozens of multi-label methods from the scientific literature, as well as a wrapper to the related MULAN framework. …

Data scientists and developers can now quickly and easily access, monitor, and visualize metrics that are computed while training machine learning models on Amazon SageMaker. You can now specify the metrics you want to track by using the AWS Management Console for Amazon SageMaker or by using the Amazon SageMaker Python SDK APIs. After the model training starts, Amazon SageMaker will automatically monitor and stream the specified metrics in real time to the Amazon CloudWatch console for visualizing time-series curves, such as loss curves and accuracy curves. You can also access the metrics programmatically using Amazon SageMaker Python SDK APIs.

I’ve posted several examples here of using Azure Cognitive Services for data science applications. You can upload an an image or video to the service and extract information about faces and emotions, generate a caption describing a scene from a provided photo, or speak written text in a natural voice. (If you haven’t tried the Cognitive Services tools yet, you can try them out using the instructions in this notebook using only a browser.)

This is a followup to a previous post:

The epubr package provides functions supporting the reading and parsing of internal e-book content from EPUB files. This post briefly highlights the changes from v0.4.0. See the vignette for a more comprehensive introduction.

Joint Sentiment Topic Modelling (rJST)Estimates the Joint Sentiment Topic model and its reversed variety, as described by Lin and He, 2009 <DOI:10.1145/1645953.1646003> and Lin, He, E …

You can file this one under “I may have the very specific solution if you’re having exactly the same problem.�