Like the course I just released on Hidden Markov Models, Recurrent Neural Networks are all about learning sequences - but whereas Markov Models are limited by the Markov assumption, Recurrent Neural Networks are not - and as a result, they are more expressive, and more powerful than anything we've seen on tasks that we haven't made progress on in decades. So what's going to be in this course and how will it build on the previous neural network courses and Hidden Markov Models? In the first section of the course we are going to add the concept of time to our neural networks. I'll introduce you to the Simple Recurrent Unit, also known as the Elman unit. We are going to revisit the XOR problem, but we're going to extend it so that it becomes the parity problem - you'll see that regular feedforward neural networks will have trouble solving this problem but recurrent networks will work because the key is to treat the input as a sequence.

It's been six months since German automaker BMW, U.S. technology giant Intel and Israeli computer vision systems company Mobileye came together to collaborate on self-driving cars, and the partnership seems to finally be bearing fruit. During a joint press conference at the ongoing Consumer Electronics Show in Las Vegas, the companies announced they will start testing autonomous vehicles later this year. "A fleet of approximately 40 autonomous BMW vehicles will be on the roads by the second half of 2017, demonstrating the significant advancements made by the three companies towards fully autonomous driving," the companies said in a joint statement. The vehicles will be BMW 7 series cars, and the trials will take place in the U.S. as well as in Europe. "This year our fleet of vehicles will already test this joint technology globally under real traffic conditions. This is a significant step towards the introduction of the BMW iNEXT in 2021, which will be the BMW Group's first fully autonomous vehicle," Klaus Fröhlich of BMW said in the statement.

R-squared score is computed for regression problems. Logistic regression, as the name suggests, is not regression but binary classification problem. Therefore, R-squared statistics cannot be computed for logistic regression. Other performance metrics, such as, accuracy, precision, recall etc are more relevant in this context. To answer your question - No R-squared score is not a valid metric for logistic regression, be it using Weka or any other ML library or even your own algorithm.

LONDON, UK, 3 January 2017 – Digital Catapult is proud to announce that it is the advocate UK partner involved in the IBM Watson AI XPRIZE, a $5m competition challenging teams to demonstrate how humans can collaborate with AI to tackle the world's grand challenges, such as poverty, health and sustainable energy. As part of its wider strategy to support market-led technology and innovation, Digital Catapult will support IBM Watson AI XPRIZE via its technical experts as resources and mentors for competing teams. Digital Catapult will also form a hub for UK teams by hosting events, meet-ups and take a lead in helping to showcase the best competitors to come out of the UK. XPRIZE offers competitors a rare chance to work on applications using AI, machine learning, predictive technologies, robotics and other advanced technologies to develop powerful cognitive applications. Teams have the freedom to define their own goals and have the unique opportunity to potentially attract investment and compete for the main prize in the final at TED 2020.

This article discusses a far more general version of the technique described in our article The best kept secret about regression. Here we adapt our methodology so that it applies to data sets with a more complex structure, in particular with highly correlated independent variables. Our goal is to produce a regression tool that can be used as a black box, be very robust and parameter-free, and usable and easy-to-interpret by non-statisticians. It is part of a bigger project: automating many fundamental data science tasks, to make it easy, scalable and cheap for data consumers, not just for data experts. Readers are invited to further formalize the technology outlined here, and challenge my proposed methodology.

Today, natural language interfaces (NLIs) on computers or phones are often trained once and deployed, and users must just live with their limitations. Allowing users to demonstrate or teach the computer appears to be a central component to enable more natural and usable NLIs. Examining language acquisition research, there is considerable evidence suggesting that human children require interactions to learn language, as opposed to passively absorbing language, such as when watching TV (Kuhl et al., 2003, Sachs et al., 1981). Research suggests that when learning a language, rather than consciously analyzing increasingly complex linguistic structures (e.g. In contrast, the standard machine learning dataset setting has no interaction.

Nvidia had a huge 2016 with one of best performing stocks of the year. In the past 12 months, the graphics processing chipmaker's stock value has boomed 230%. This is mostly due to its impressive growth in artificial intelligence applications using its graphics processors in data centers and cars. Meanwhile, Nvidia maintains a fast-growing business in its core gaming market. Partially as a reflection of the growing importance of AI in the tech industry, Nvidia stole the opening Consumer Electronics Show keynote this year from Intel.

Among emerging technologies, IT professionals expect Internet of Things (IoT) devices and artificial intelligence (AI) technology to have the biggest impact in the workplace, according to a new report from online IT professionals community Spiceworks. The study, "Future of IT: Hype vs. Reality," examines organizations' adoption plans for technologies such as virtual reality (VR), 3D printing, IoT and AI. While survey respondents don't expect mass adoption to take off for VR and 3D printers, some industries have significantly higher adoption rates than the industry average, the report notes. Of the 566 IT professionals surveyed worldwide in October 2016, 80 percent said IoT devices will be useful to their business practices in three to five years, and nearly 60 percent said the same for AI. Over the next five years 60 percent of companies plan to adopt machine learning and 72 percent plan to deploy business analytics with AI.

Bose dabbles in a lot of different things, but the most left-field product could could be its ride-smoothing trucker seats. The company is about to demo similar tech for autonomous cars at its "Beyond Sound" CES experience in Las Vegas. Specifically, it'll show off a (simulated) autonomous vehicle equipped with Bose Ride suspension seating that "isolates passengers from road vibrations, shaking and unwanted motion," the company said in a press release. The idea is that if you're going to be driven around by a robot anyway, why not feel like you're in your living room or office, rather than a lurching car? "Our personal suspension technology is already proven, and it can dramatically enhance the passenger experience," Bose Automotive VP Marc Mansell said in a statement, referring to the Bose Ride truck seats that debuted in 2010. Bose actually started dabbling with the tech way back in the 1980s.

So, you want to learn how to build machine learning algorithms? But where do you start? Becoming a data scientist is a really smart career move – it's possibly one of the most valuable jobs out there. That's just one of the reasons it was hailed by the Harvard Business Review as the'sexiest job of the twentieth century' back in 2012. But learning the skills you need to become a truly great data scientist, capable of building powerful machine learning systems with languages like Python and R, isn't easy.