There is no shying away from the fact that the high street has seen better days. UK shops are continuing to close – more than 1,200 last year, meaning more retailers are turning their focus to online.Read Full Story
We‘ve all seen presentations to large groups where the presenter takes the stage nervous, sweating, with a dry mouth and a carefully chosen statement intended to kick off his or her PowerPoint. A strong start to a presentation can secure the attention of your audience as it engages their attention and willingness to step into the flow of your story.Read Full Story
Lee Se-dol is seen in 2016 during his matches with the AI program AlphaGo. | Photo: Google / Getty Images
The South Korean Go champion Lee Se-dol has retired from professional play, telling Yonhap news agency that his decision was motivated by the ascendancy of AI.
In my last post I described the DRAW model of recurrent auto-encoders. As far as I’ve seen, the only implementations of DRAW floating around Github deal with the MNIST dataset. While they are helpful for reference, I wanted to have a model that could successfully generate photographs, not just black-and-white digits.Read Full Story
Graph are meant to be seen
The third layer of graph technology that we discuss in this article is the front-end layer, the graph visualization one. The visualization of information has been the support of many types of analysis, including Social Network Analysis. For decades, visual representations have helped researchers, analysts and enterprises derive insights from their data.
Over last 12 years, I have seen Master Data Management help companies automate and improve data. It has helped companies take a strategic approach to managing data by removing processes that were mainly left manual and time-consuming for years.
We have seen an exponential increase in volume and variety of data in last 5-6 years.
Research in machine learning has seen some of the biggest and brightest minds of our time – and copious amounts of funding – funneled into the pursuit of better, safer, and more generalizable algorithms. As the field grows, there is vigorous debate around the direction that growth should take (for a less biased take, see here).Read Full Story
Over the last couple of years, I’ve seen a large number of people attempt to diagnose the quality of their randomized experiments by looking for imbalances in covariates, because they expect covariate imbalances to be ruled out by successful randomization.Read Full Story
As seen on KDNuggets, you may now download Chapter 19, Derived Variables: Making the Data Mean More for free, thanks to our friends at JMP. This chapter is one of my personal favorites because it is about the part of data mining I find most enjoyable–thinking of ways to expose more of the information hidden in a data set so predictive algorithms are able to make use of it.Read Full Story
This post was inspired by HN user eck’s top comment seen here.
Earlier this week the New York City Taxi & Limousine Commission officially released yellow and green taxi trip record data for all of 2014 and up to June of 2015.
You’ve probably seen the composite map of lights at night from NASA. It looks a lot like population density. Tim Wallace adjusted the map for population, so that you can see (roughly) the areas that produce more light per person.
Adjusting NOAA nighttime lights for population reveals areas that create an outsized amount of light per person living there. pic.twitter.
Upper bounds as never seen on TV.
Niloy Biswas (PhD student at Harvard) and I have recently arXived a manuscript on the assessment of MCMC convergence (using couplings!).