There are many deep learning resources freely available online, but it can be confusing knowing where to begin. Go from vague understanding of deep neural networks to knowledgeable practitioner in 7 steps!

We at Sift Science provide fraud detection for hundreds of customers spanning many industries and use cases. To do this, we have devised a specialized modeling stack that is able to adapt to individual customers while simultaneously delivering a great out-of-box experience for new customers, achieved by mixing the output from a "global" model – trained on our entire network of data – with the output from a customer's individualized model. Prior to decision forests, we used a custom-built logistic regression classifier combined with highly specialized feature engineering for our global model. While logistic regression has many great attributes, it is fundamentally limited by its inability to model non-linear interactions between features. At Sift, we tend to think of our modeling stack primarily as an enabler of our feature engineering; more powerful modeling allows us to extract the most insight from our features and can even lead to new classes of features.

A former science teacher who believed in the power and possibility of online learning over two decades ago, he taught himself how to build courses in HTML on class intranets. Kevin taught one of the first hybrid, educational technology courses for teachers, for the University of Washington. And, after building countless web pages and classes on the early world wide web, he now helps develop e-learning programs, consults on virtual training'best practices' and has many interests in other internet and educational technology-related areas. Kevin finds he's now enjoying learning more from his children who are all deep into their own technology-related careers and entrepreneurial endeavors. With two new grandchildren, he's investigating more seriously the advancing new technologies in an effort to understand the knowledge and skills necessary to achieve happiness and success in a technological future.

A trip to Iceland wouldn't be complete without a dip in the Blue Lagoon, a man-made geothermal pool on Reykjanes peninsula. A trip to Iceland wouldn't be complete without a dip in the Blue Lagoon, a man-made geothermal pool on Reykjanes peninsula. The premise is simple: Correspondents, editors and producers from our newsroom share the pieces that have kept them reading, using the #NPRreads hashtag. Each weekend, we highlight some of the best stories. You have storms, you have darkness, but the pool is a place to find yourself again.

Peter Singer is a professor of bioethics at Princeton University and Laureate Professor at the University of Melbourne His books include Animal Liberation, The Life You Can Save, The Most Good You Can Do, and, most recently, Famine, Affluence and Morality. Last month, AlphaGo, a computer program specially designed to play the game Go, caused shock waves among aficionados when it defeated Lee Sedol, one of the world's top-ranked professional players, winning a five-game tournament by a score of 4-1. Why, you may ask, is that news? Twenty years have passed since the IBM computer Deep Blue defeated world chess champion Garry Kasparov and we all know computers have improved since then. But Deep Blue won through sheer computing power, using its ability to calculate the outcomes of more moves to a deeper level than even a world champion can.

Futurists envision a doomsday scenario where robots rise up against us. But Artificial Intelligence and robots are not the same thing, and Artificial Intelligence software has quietly crept into many facets of our lives. Artificial Intelligence is used in computer games and in the software that helps us parallel park. Artificial Intelligence is about designing intelligent software that can analyze its environment and make intelligent choices for online learning. But what exactly could be the future of Artificial Intelligence in eLearning?

Sure, robots and intelligent machines are likely to replace jobs in the not so distant future. The situation, though, isn't as dire as some would have you believe, according to Tom Davenport, co-author of Only Humans Need Apply: Winners and Losers in the Age of Smart Machines. The book is due out in May. Instead of stealing humans' jobs, artificial intelligent systems and robotics will help many people do their jobs better. "We have a new generation of technologies and we need to work with them if we're going to be productive and effective," Davenport said in an interview.

In the Army's Emergent Leader Immersive Training Environment, or ELITE, Soldiers hone their basic counseling skills through practice with virtual humans like virtual Staff Sergeant Jessica Chen. New research aims to get robots and humans to speak the same language to improve communication in fast-moving and unpredictable situations. Scientists from the U.S. Army Research Laboratory and the University of Southern California Institute for Creative Technologies are exploring the potential of developing a flexible multimodal human-robot dialogue that includes natural language, along with text, images and video processing. "Research and technology are essential for providing the best capabilities to our Warfighters," said Dr. Laurel Allender, director of the U.S. Army Research Laboratory Human Research and Engineering Directorate. "This is especially so for the immersive and live-training environments we are developing to achieve squad overmatch and to optimize Soldier performance, both mentally and physically."

Below is my personal list of statistical and machine learning methods that every data scientist should know in 2016. From my experience in the data science industry for 4 years, I think that currently these 12 methods are the most popular, useful and suitable for various problems requiring data science. As far as I've known, there have been not a few lists of "representative methods in data science" ever. However, I feel some of them are already out-of-date because they appear to neglect the latest advance of data science in the industry. Thus I made this list as the one by business person, who knows practical matters and solutions with data science, including statistics and machine learning in the industry.

And the battle to control and exploit that data is just getting started. On Monday, the Japanese carmaker Toyota announced a new subsidiary, called Toyota Connected, that will manage and mine the data collected from its vehicles, and the company said it would collaborate with Microsoft on the venture. The data collected and delivered might include mapping data, engine statistics, and records of driver behavior. Most immediately, this could mean updating vehicle features or patching bugs remotely. But the goal is also to develop new kinds of interfaces that predict a driver's intention.