A Voice Controlled E-Commerce Web Application

1. Advanced Modeling

NearBucket Locality Sensitive Hashing (NearBucket-LSH) We present NearBucket-LSH, an effective algorithm for similarity search in large-scale distributed online social networks organized as peer-to-peer overlays. As communication is a dominant consideration in distributed systems, we focus on minimizing the network cost while guaranteeing good search quality. Our algorithm is based on Locality Sensitive Hashing (LSH), which limits the search to collections of objects, called buckets, that have a high probability to be similar to the query. More specifically, NearBucket-LSH employs an LSH extension that searches in near buckets, and improves search quality but also significantly increases the network cost. We decrease the network cost by considering the internals of both LSH and the P2P overlay, and harnessing their properties to our needs. We show that our NearBucket-LSH increases search quality for a given network cost compared to previous art. In many cases, the search quality increases by more than 50%. …

“Data science is an interdisciplinary endeavor born of the synergy between computing, statistics, data management, and visualization. This can make it challenging to get started, because you have to know so many things before you get to the good stuff. We’re going to try to ease into it by starting with computational explorations of mathematical and statistical concepts.” Bob Horton ( January 20, 2015 )

styler 1.1.0 is now available on CRAN. Thisrelease introduces new features and is fully backward-compatible. It alsoadapts to changes in the R parser committed into R devel (#419). Major changesare:

At: Humana Location: Chicago, IL, Dallas, TX and Louisville, KYWeb: www.humana.comPosition: Principal Data Scientist/Informatics Principal

MIT researchers show how to detect and address AI bias without loss in accuracy

Learn the business “why” and technical “how” for implementing machine learning in your organization.

Today we are excited to announce the availability of Dynamic Training (DT) for deep learning models, or DT for short. DT allows deep learning practitioners to reduce model training cost and time by leveraging the cloud’s elasticity and economies of scale. Our first reference implementation of DT is based on Apache MXNet, and is open sourced under Dynamic Training with Apache MXNet. This blog post introduces the concept of DT, showcases training results achieved, and demonstrates how you can get started leveraging it for your model training jobs.

A few months ago, I wasn’t sure what to expect when looking at fluorescence microscopy images in published papers. I looked at the accompanying graph to understand the data or the point the authors were trying to make. Often, the graph represents one or more measures of the so-called co-localization, but I couldn’t figure out how to interpret them. It turned out; reading the images is simple. Cells are simultaneously stained by two dyes (say, red and green) for two different proteins.