We solve this problem using a semi-supervised form of logistic regression. A large portion of the model consists of “bag of words” type features from user submitted reviews on the properties. Since it is a semi-supervised technique, not only do we use the reviews on locations that we have tag votes on during training, we also use a large chunk of unlabeled data. Also, when applying the model to get the end results, we need to read and process all our reviews. On top of that, we have hundreds of different tags.
There are interesting similarities between the design of Apache Kafka and Unix pipes. In this post we explore how this design allows us to build large, scalable applications by composing small stream processing tools.
In this post, we outline Spark Streaming’s architecture and explain how it provides the above benefits. We also discuss some of the interesting ongoing work in the project that leverages the execution model.
New Class of Memory Unleashes the Performance of PCs, Data Centers and More NEWS HIGHLIGHTS Intel and Micron begin production on new class of non-volatile memory, creating the first new memory category in more than 25 years.New 3D XPoint™ technology brings non-volatile memory speeds up to 1,000
We’ve seen that there is one processor that needs to be added to the picture — the commodity multi-core CPU. This is already a part of many server configurations, and for some applications, e.g., Monte-Carlo pricing of American options, it can give better or comparable performance than an accelerator processor when optimized correctly. Between NVIDIA’s Kepler GPUs and Xeon Phi, the GPU wins for both of our test applications.
Written by Nicole White What’s New in RNeo4j? RNeo4j is Neo4j’s R driver – it allows you to quickly and easily interact with a Neo4j database from your R environment. Some recent updates to RNeo4j include: My contributions Functionality for… Learn More »
By migrating AdRoll's real-time data pipeline to Kinesis we were able to reduce our end to end latency more than one hundredfold while simultaneously cutting costs and improving system stability. Here we'll follow architectural decisions, implementation details, and overall learnings from this process.
A big part of any ML workflow is massaging the data into the right features for use in downstream processing. To simply feature extraction, Spark provides many feature transformers out-of-the-box. The table below outlines most of the feature transformers available in Spark 1.4 along with descriptions of each one. Much of the API is inspired by scikit-learn; for reference, we provide names of similar scikit-learn transformers where available.
With the recent release of Apache Spark 1.4.1 on July 15th, 2015, I wanted to write a step-by-step guide to help new users get up and running with SparkR locally on a Windows machine using command shell and RStudio. SparkR provides an R frontend to Apache Spark and using Spark’s distributed computation engine allows R-Users to run large scale data analysis from the R shell. The steps listed here are also documented in my online book title “Getting Started with SparkR for Big Data Analysis” which can be accessed at: http://www.danielemaasit.com/getting-started-with-sparkr/. These steps will get you up and running in less than 5 mins.
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