At: UnitedHealth GroupLocation: Minnetonka, MN Web: www.unitedhealthgroup.comPosition: UHC Digital Director of Project Management

By Prerak Mody.

“Tomorrow’s market winners will win with the smartest products.  It’s not enough to just build insanely great products; winners must have the smartest products!” – Bill Schmarzo

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Digital Native (DN) The term Digital Native was coined and popularized by education consultant, Marc Prensky in his 2001 article entitled Digital Natives, Digital Immigrants, in which he relates the contemporaneous decline in American education to educators’ failure to understand the needs of modern students. His article posited that ‘the arrival and rapid dissemination of digital technology in the last decade of the 20th century’ had fundamentally changed the way students think and process information, making it impossible for them to excel academically using the outdated teaching methods of the day. In other words, children raised in the post-digital, media saturated world, require a media-rich learning environment to hold their attention. Contextually, his ideas were introduced after a decade of worry over increased diagnosis of children with ADD and ADHD, which itself turned out to be largely overblown. Prensky did not strictly define the Digital Native in his 2001 article, but it was later, somewhat arbitrarily, applied to children born after 1980, due to the fact that computer bulletin board systems, and Usenet were already in use at the time. The idea became popular among educators and parents, whose children fell within Prensky’s definition of a Digital Native, and has since been embraced as an effective marketing tool. …

There’s an internet joke — Godwin’s Law — that says if an internet discussion goes long enough, the probability that someone mentions Hitler approaches a probability of 1.

In this blog post, we introduce three new features of the Amazon SageMaker Neural Topic Model (NTM) that are designed to help improve user productivity, enhance topic evaluation capability, and speed up model training. In addition to these new features, by optimizing sparse operations and the parameter server, we have improved the speed of the algorithm by 2x for training and 4x for evaluation on a single GPU. The speedup is even more significant for multi-GPU training.

A Direct Proof of the Reflection Principle for Brownian Motion

Getting into data science typically requires that you have access to a decent computer or server. You also usually need to install software. Chromebook Data Science, a set of online sources from the Johns Hopkins Data Science Lab, lets people learn with just a Chromebook and an internet connection:

In Part 1 of this blog post, we demonstrated how to train and deploy neural networks to automatically segment brain tissue from an MRI scan in a simple, streamlined way using Amazon SageMaker. We used Apache MXNet to train a convolutional neural network (CNN) on Amazon SageMaker using the Bring Your Own Script paradigm. We trained two networks: U-Net and the efficient, low-latency ENet. Now we show how to use AWS Greengrass ML Inference to deploy ENet to a portable edge device for offline inference in low- or no-connectivity environments.