This decrease was mainly due to the Note 7 issue, despite the increase of sales in the memory and OLED businesses. The gross profit for the quarter was KRW18.4 trillion, about KRW1.7 trillion year-on-year decrease. But the gross profit margin as a percent of sale held steady due to higher gross profits from the sales expansion of premium products in the OLED and consumer electronics businesses. Our SG&A expenditures increased Y-on-Y, mainly due to the recall cost related to Note 7. The operating profit decreased by KRW2.2 trillion, year on year to KRW5.2 trillion, and the operating profit margin declined by 3.4 percentage points to 10.9%. The earnings of the component business decreased marginally year on year due to price correction of DRAM during the first half of this year. However, on Q-on-Q basis, this operating profit increased due to sales expansion of high-end products such as SSD, flexible OLED under the stabilized ASP environment. In the set business, earnings declined in the IM division due to the loss resulting from the Note 7 issue, but the consumer electronics business continued to grow year on year, driven by the sales growth of SUHD TVs and premium home appliance products. In this quarter's strengthening of the Korean won against the major currencies such as U.S. dollar and euro had a negative impact on the operating profit quarter on quarter. We figured it's approximately KRW700 billion effect, mostly on the component business. The non-operating profit was KRW540 billion, mainly from the sales of various investments including investments in ASML. Now I would like to address the business outlook. In the fourth quarter we expect the overall earnings to improve year on year. The mobile business is expected to recover its earnings to the similar level as 4Q last year through solid S7 sales, while earnings in the component business is projected to improve year on year. For the semiconductor business, we expect the earnings to improve due to the sales expansion of the V-NAND-based SSD. For the display business, we expect the earnings to improve also from LCD business recovery year on year.

None of deep learning models discussed here work as classification algorithms. Instead, they can be seen as Pretrainin, automated feature selection and learning, creating a hierarchy of features etc. Once trained (features are selected), the input vectors are transformed into a better representation and these are in turn passed on to a real classifier such as SVM or Logistic regression. This can be represented as below.

In less than a decade, Uber has redefined the idea of flexible labor and gutted the American taxi industry. The company launched a fleet of self-driving cars in Pittsburgh. Within a decade, according to a 99-page white paper released today, Uber will have a network--to be called "Elevate"--of on-demand, fully electric aircraft that take off and land vertically. Instead of slogging down the 101, you and a few other flyers will get from San Francisco to Silicon Valley in about 15 minutes--for the price of private ride on the ground with UberX. These aren't flying cars in the sense that they both drive on the ground and soar through the air.

Apple is about to release a whole new range of computers. They might be the most important Macs released in recent years – and could decide the near future of Apple. The new computers come at an important time for the company, just days after it reported its worst results in 15 years and said that it wouldn't be able to deliver the EarPods that had been the centre of its plan for the wireless future. And they come at an important time for the Mac, too: many models haven't been meaningfully updated for years, with fans of the computers worrying that more attention is being paid to the iPhones and iPad. The event will be Apple's big chance to turn around all of those worries with new technology.

This notebook was written by Dr. Randal S. Olson from GitHub. In this notebook, Randal is going to go over a basic Python data analysis pipeline from start to finish to show you what a typical data science workflow looks like. In addition to providing code examples, he also hopes to imbue in you a sense of good practices so you can be a more effective -- and more collaborative -- data scientist. Randal will be following along with the data analysis checklist from The Elements of Data Analytic Style, which he strongly recommends reading as a free and quick guidebook to performing outstanding data analysis. In the time it took you to read this sentence, terabytes of data have been collectively generated across the world -- more data than any of us could ever hope to process, much less make sense of, on the machines we're using to read this notebook.In response to this massive influx of data, the field of Data Science has come to the forefront in the past decade. Cobbled together by people from a diverse array of fields -- statistics, physics, computer science, design, and many more -- the field of Data Science represents our collective desire to understand and harness the abundance of data around us to build a better world.

Big Data is becoming too big to manage manually. The amount of data coming from sensors, streams and social media is astronomical--but that's only part of the problem. Out of all the data that is being collected, only a small amount of it is actually essential, making it an impossible task to find the needle (value) in the haystack (data). "Data collection is easy," said Sri Ambati, CEO of H2O.ai, a machine learning solution provider. "But it is not just about collecting data for your customer anymore; it is knowing what they want that makes a big difference." In order to sift out the value from all the data, organizations are turning to machine learning technologies to learn from their data, make sense of their data, and make better business decisions based on the data. "Machine learning is the crucial link between business use, between applications at the business level, and between ROI to the actual collection of data," said Ambati. Big Data has become the norm in today's enterprise, and machine learning is now becoming imperative to that norm, according to Steven Noels, cofounder and CTO of NGDATA, a Big Data analytics and management provider. Businesses need to continuously pull insights out of their massive amounts of data in order to improve customer experience, streamline business processes, optimize solutions, and understand the business in real time.

In Munich, Germany, the technology conglomerate Siemens AG is betting $1.1 billion on digital technologies, such as the proposition that blockchain data can be leveraged by machine learning to improve the secure transmission of data used in energy trading. Siemens is welcoming its employees and independent firms to bid for the money if they are willing to research how Siemens can develop businesses that use artificial intelligence. Siemens is just one of a number of companies embracing machine learning--the combination of artificial intelligence and deep analytics that enables enterprises to make predictions on large amounts of data and allows developers to experiment by incorporating features like speech and pattern recognition, as well as statistical techniques, into their analysis. And HPE's recent announcement of "machine learning as a service" (MLaaS) is aimed at helping the process really take off. Speaking at HPE Discover Las Vegas 2016, HPE Executive Vice President Robert Youngjohns called machine learning and deep analytics the most fundamental change we're ever going to see.

Over the course of the last 12 years or so, we've seen an evolution from large traditional VC firms investing $5-10M per company in the first round of financing to the emergence of "micro" VC firms investing in rounds $1M-$3M dubbed "seed rounds". This evolution has also spawned even smaller firms investing in rounds of several hundreds of thousands of dollars as well in a stage referred to as "pre-seed". As Mark Suster wrote in his post linked above, the emergence of open source software and cloud computing completely eviscerated the costs and barriers to starting a company, leading to deflationary economics where one or two people could start their company without the large upfront costs that were historically the hallmark of the VC industry. These lower barriers to entry has led to a "cambrian explosion" of startups but hasn't necessarily changed the rules of business. Without a defensible moat, it's just about impossible to create a large company with sustainable profits.

With a human voice and mindful mannerisms, she's the first alarm clock you'll be happy to wake up to. A.I. algorithms ease your morning routine by learning about you & what you love. The Bonjour Smart Alarm Clock is like the personal assistant you never had. She's always there early to wake you up and make the most of your day. Bonjour can adjust your wake-up time if certain conditions are fulfilled.

Slack Technologies Inc., a business messaging provider, is the latest company to partner with International Business Machines Corp. IBM 0.62 % to add artificial intelligence to its service. Slack plans to improve Slackbot, its customer-service bot, using IBM's Watson, a collection of artificial-intelligence software delivered as cloud-computing services, the two companies said on Wednesday. The messaging company will use Watson Conversation, an IBM service that processes natural language, to enhance the accuracy and efficiency of the bot, which helps Slack users troubleshoot problems. These and other enhancements will be available to users early next year, Slack said.