How to benefit neural network? As long as I know, Deep Learning uses an abstract neural network, but how to benefit this neural network? Is it directly interogated by a software to help it to answer the user's question, or is it used to only generate a model that can be used by the software? In other words, does the neural network still works during software processing after the model is generated (regardless model evolution)?

They took NYC Data Science Academy 12 week full time Data Science Bootcamp program between Sept 23 to Dec 18, 2015. The post was based on their fourth class project(due at 8th week of the program). As part of a Kaggle competition, we were challenged by Rossmann, the second largest chain of German drug stores, to predict the daily sales for 6 weeks into the future for more than 1,000 stores. Exploratory data analysis revealed several novel features, including spikes in sales prior to, and preceding store refurbishment. We also engineered several novel features by the inclusion of external data including Google Trends, macroeconomic data, as well as weather data.

Save the "Star Wars" franchise? Everyone's favorite "Star Wars" actress, Daisy Ridley, told The Hollywood Reporter at this weekend's Empire Awards that there "have been conversations" about her wielding the storied video game heroine's dual pistols in an upcoming reboot. Conversations, however, don't mean that Ridley's casting is a done deal, as the 23-year-old actress has yet to receive an official offer. "I'm waiting for someone to say'I want you, let's do it,'" she said, explaining that the project doesn't have a script yet. But Ridley is committed to the role, even with her busy "Star Wars" schedule -- she is currently filming the eighth installment in the sci-fi epic. If the opportunity presented itself, Ridley would be happy to make time for "Tomb Raider," telling THR that "I'm trying to fill up my calendar."

Domino's latest "deliveryman" stands three feet tall and doesn't need to be tipped. It has ferried pizzas in Brisbane at a top speed of 12 mph, and the company's Australian master franchise said it's excited for what could come next. "We have a relentless passion to push the boundaries of what's possible with pizza delivery," said Michael Gillespie, chief digital officer for Domino's in Australia. "As we get further, it's not hard to believe that we might have a store with a couple of [robots] that are doing deliveries." Domino's has started using a robotic cart named DRU, which stands for Domino's Robotic Unit, to deliver its offerings.

China has been the power engine of global economic growth since the Chinese Communist Party embraced free market economics some 30 years ago. What leveraged China's momentous success has been its people, or - to be more precise - the enormous number of cheap labour that migrated from the rural areas to the cities, some of which had to be built from scratch in order to accommodate millions of new residents. Low wages, foreign investment and open markets made China the manufacturing powerhouse of the world. But things have changed since those halcyon days of easy growth. Labour supply in China has become increasingly scarcer, a fact reflected in the almost exponential rise of wages since the beginning of the 2000s.

Before turning to the expected round of product announcements at today's Apple event, held at the company's campus in Cupertino, Calif., Apple introduced a technical development that won't be a product anytime soon: Liam, the recycling robot. Apple's vice president of environment, policy, and social initiatives, Lisa Jackson, said that though Apple's track record of reusing iPhones that are exchanged for upgrades is good, the company recognized that eventually, these things can't be reused. Therefore, she indicated, Apple decided to up its recycling game. The company's engineers in Silicon Valley developed a recycling robot, named Liam, that recognizes all the key parts on an iPhone, takes the phone apart, and pulls out the most valuable materials, including cobalt, lithium, gold, copper, silver, platinum, and tungsten. With a team of Liams available to mine phones for precious metals, Jackson announced a free recycling program for iPhones; customers can drop the phones at Apple stores, or print a prepaid mailing label at home. She urged customers to recycle devices in a way that is "safe for data and safe for the planet," and will keep a little Liam and his friends busy.

The fundamental assumption in Machine Learning is that analytical solutions can be built by studying past data models. Machine Learning supports that kind of data analysis that learns from previous data models, trends, patterns, and builds automated, algorithmic systems based on that study. This article takes a realistic look at where that data technology is headed into the future. As Machine Learning relies solely on pre-built algorithms for making data-driven analysis and predictions, it claims to replace data analytics and prediction tasks carried out by humans. In Machine Learning, the algorithms have the capability to study and learn from past data, and then simulate the human decision-making process by using predictive analysis and decision trees.

Drone images accumulate much faster than they can be analyzed. Researchers have developed a new approach that combines crowdsourcing and machine learning to speed up the process. Who would win in a real-life game of "Where's Waldo," humans or computers? A recent study suggests that when speed and accuracy are critical, an approach combing both human and machine intelligence would take the prize. With drones being used to monitor everything natural disaster sites, pollution, or wildlife populations, analyzing drone images in real-time has become a critically important big data challenge. Publishing in the journal Big Data, researchers, including Stéphane Joost from EPFL, present a new approach to rapidly interpret aerial images taken by camera drones that combines human crowdsourcing and machine learning.

Computers are getting pretty good at predicting the future. In many cases they do it better than people. That's why Amazon uses them to figure out what you're likely to buy, how Netflix knows what you might want to watch, the way meteorologists come up with accurate 10-day forecasts. Now a team of scientists has demonstrated that a computer can outperform human judges in predicting who will commit a violent crime. In a paper published last month, they described how they built a system that started with people already arrested for domestic violence, then figured out which of them would be most likely to commit the same crime again.

Moving on, let's look at artificial intelligence (AI). For AI to work well, it needs information. Up until recently, AI systems could acquire information and learning through machine-based learning. Show something like a picture of a car to an AI system, provide commentary and off it goes to find more cars. The problem with that system is it doesn't scale well.