Analytics, statistics, operations research, data science and machine learning - with which term do you prefer associate? Are you from the House of Capulet or Montague, or do you even care? That which we call a rose By any other name would smell as sweet." Romeo was from the house of Montague, Juliet, the house of Capulet, and this distinction that meant that their families were sworn enemies. The play is a tragedy, because by the end the two lovers end up dead as a result of this long-running feud. Statistics, data science and machine learning are but a few of the "houses" that feud today over names, and while to my knowledge no deaths have resulted from this debate the competing camps have nearly come to blows. How has the emerging field of Analytics impacted the Operations Research Profession? Is Analytics part of OR or the other way around? Is it good, bad, relevant, a nuisance or an opportunity for the OR profession? Is OR just Prescriptive or is it something more? In this panel discussion, we will explore these topics in a session with some of the leading thinkers in both OR and Analytics. Be sure to attend to have your questions answered on these highly complementary and valuable fields."

You've studied machine learning, you're a dataframe master for massaging data, and you can easily pipe that data through a bunch of machine learning libraries. You go for a job interview at a SAAS company, you're given some raw data and labels and asked to predict churn, and come on - are these guys even trying? You generate the shit out of some features, you overfit the hell out of that multidimensional manifold just so you can back off and show off your knowledge of regularization, and then you put the icing on the cake by cross validating towards a better metric for the business problem than simple accuracy. You roll up on an ecommerce company, and they trick you by basically giving you no features. Ha! Nice try, but you know that's a classic recommender system.

Patterns are what machine-learning algorithms exist to sniff out. But detecting those patterns is almost never the endgame. Typically, we use machine learning (a category in which I also include deep learning) to drill down to the patterns most relevant to some decision-support scenario, such as identifying fine-grained nuances of customer sentiment for use in target marketing or pinpointing the signs of imminent equipment failure through continuous sifting of scattered event-log databases. Once discovered, the statistical patterns can take on a programmatic life of their own that goes far beyond decision support in potential applications. As crystallized in machine-learning models, the patterns can become key assets in the development of other algorithmic applications that have little or no relevance to decision support.

What's more, when you're reading one-handed on your phone, you can use the volume buttons to navigate back and forth. Bubble Zoom is part of the most recent version of Google Play Books, and yes, only Android users are privy to the feature right now. If you're using Google's software, you can employ a technical preview of the reading tool while browsing DC and Marvel volumes that support it. Google will be collecting feedback on the update and plans to make it available on all digital comics and manga. The company says it has to teach its machine learning algorithms how to read more styles before expanding its supported library.

Abdul is a young Entrepreneur, Technical Writer, Security Blogger and IT Analyst. He has a keen eye on the Cyberspace and other tech related developments.

The command module was the home of all three astronauts during most of the mission, and the only part to return intact to Earth. It sits atop the service module, which is accessible by a dock shown in the 3D model. The astronauts can then traverse the service module and access the lunar module via a docking tunnel. "The command module had many, many hidden nooks and crannies that are really hard to see," says Vincent Rossi, the senior 3D program officer. It's also composed of reflective surfaces that make scanning tough.

Microsoft has a brand-new conference, exclusively for data scientists, big data engineers, and machine learning practitioners. The Microsoft Data Science Summit, to be held in Atlanta GA, September 26-27, will feature talks and lab sessions from Microsoft engineers and thought leaders on using data science techniques and Microsoft technology, applied to real-world problems. Other topics of interest include building with bot frameworks, deep learning, Internet of Things applications, and in-depth Data Science topics. To register for the conference, follow the link below. Discounted day passes to Microsoft Ignite on September 28-29 are also available to Microsoft Data Summit registrants.

When you first heard of the term convolutional neural networks, you may have thought of something related to neuroscience or biology, and you would be right.

Expert publishing blog opinions are solely those of the blogger and not necessarily endorsed by DBW. Last month, Mike Shatzkin wrote a blog post titled "Full text examination by computer is very unlikely to predict bestsellers," in which he described how the claims of the creation of an algorithm that predicts bestsellers, as outlined in a new book The Bestseller Code: Anatomy of the Blockbuster Novel, are impossible. While I agree in theory with Shatzkin that an algorithm alone cannot predict whether a book will be a bestseller or not, that isn't precisely what The Bestseller Code claims, nor what our experience working with machine learning at Intellogo defines. What we aim to do is identify similar tones, moods, topics and writing styles to those books that are topping bestseller lists--as we can only do through algorithms--and, in this way, better understand the reading audiences' desires. Machine learning allows us to do just that.

Get a quick introduction to data science from Data Science for Beginners in five short videos. This video series is helpful if you're interested in doing data science - or work with people who do data science - and you want to start with some basic concepts. This first video is about the kinds of questions that data science can answer. Data science predicts answers to questions using a number or category. To get the most out of the series, watch them in order.