Leading stock photo company Shutterstock unveiled a new deep learning-based tool that lets users search photos by their composition. "Built on our next generation visual similarity model, this tool helps you find the exact image you need by placing keywords on a canvas and moving them around where you want subject matter to appear in the image," mentioned Kevin Lester, VP of Engineering at Shutterstock in a related blog. "The patent-pending spatially aware technology will find strong matches based not only on your search terms, but also on the placement of your search terms." Using TITAN X GPUs and the cuDNN-accelerated Torch deep learning framework, the researchers trained their visual model on their own internal image dataset and the language model to match a textual query to the embedding of a corresponding image. Once trained, they leverage Tesla GPUs on the Amazon cloud to give users total control over the image composition on any project – such as being able to use search terms like "wine" and "cheese" and being able to drag it around so photos of "wine" are on the left and "cheese" on the right.

As our Intel CEO Brian Krzanich discussed earlier today at Wall Street Journal's D.Live event, Intel will soon be shipping the world's first family of processors designed from the ground up for artificial intelligence (AI): the Intel Nervana Neural Network Processor family (formerly known as "Lake Crest"). This family of processors is over 3 years in the making, and on behalf of the team building it, I'd like to share a bit more insight on the motivation and design behind the world's first neural network processor. Machine Learning and Deep Learning are quickly emerging as the most important computational workloads of our time. These methods allow us extract meaningful insights from data. We've been listening to our customers and applying changes to Intel's silicon portfolio to deliver superior Machine Learning performance.

When the AI boom came a-knocking, Intel wasn't around to answer the call. Now, the company is attempting to reassert its authority in the silicon business by unveiling a new family of chips designed especially for artificial intelligence: the Intel Nervana Neural Network Processor family, or NNP for short. The NNP family is meant as a response to the needs of machine learning, and is destined for the data center, not your PC. Intel's CPUs may still be a stalwart of server stacks (by some estimates, it has a 96 percent market share in data centers), but the workloads of contemporary AI are much better served by the graphical processors or GPUs coming from firms like Nvidia and ARM. Consequently, demand for these companies' chips has skyrocketed.

Intel enlisted one of the most enthusiastic users of deep learning and artificial intelligence to help out with the chip design. "We are thrilled to have Facebook in close collaboration sharing their technical insights as we bring this new generation of AI hardware to market," said Intel CEO Brian Krzanich in a statement. On top of social media, Intel is targeting healthcare, automotive and weather, among other applications. Unlike its PC chips, the Nervana NNP is an application-specific integrated circuit (ASIC) that's specially made for both training and executing deep learning algorithms. "The speed and computational efficiency of deep learning can be greatly advanced by ASICs that are customized for ... this workload," writes Intel's VP of AI, Naveen Rao.

This morning at the WSJ's D.Live event, Intel formally unveiled its Nervana Neural Network Processor (NNP) family of chips designed for machine learning use cases. Intel has previously alluded to these chips using the pre-launch name Lake Crest. The technology underlying the chips is heavily tied to Nervana Systems, a deep learning hardware startup Intel purchased last August for $350 million. Intel's NNP chips nix standard cache hierarchy and use software to manage on-chip memory to achieve faster training times for deep learning models. Intel has been scrambling in recent months to avoid being completely leveled by Nvidia.

I don't know about you, but I was not the most athletic kid growing up. It took me forever to make a jump shot well. When I started playing golf after college my short game was an absolute disaster. I always had a hard time visualising what I needed to do differently. Having a coach tell me what to do never seemed to do the trick.

TensorFlow* is a leading deep learning and machine learning framework, which makes it important for Intel and Google to ensure that it is able to extract maximum performance from Intel's hardware offering. This paper introduces the Artificial Intelligence (AI) community to TensorFlow optimizations on Intel Xeon and Intel Xeon Phi processor-based platforms. These optimizations are the fruit of a close collaboration between Intel and Google engineers announced last year by Intel's Diane Bryant and Google's Diane Green at the first Intel AI Day. We describe the various performance challenges that we encountered during this optimization exercise and the solutions adopted. We also report out performance improvements on a sample of common neural networks models.

What is an artificial neural network? What types of artificial neural networks exist? How are different types of artificial neural networks used in natural language processing? We will discuss all these questions in the following article. An artificial neural network (ANN) is a computational nonlinear model based on the neural structure of the brain that is able to learn to perform tasks like classification, prediction, decision-making, visualization, and others just by considering examples.

One of the biggest differences between machine learning and deep learning is the effort that goes into making the algorithms work. With machine learning, data scientists have to perform a task called feature engineering. "People get the incoming data, and they prepare it, and they clean it, and they maybe manipulate it in a way that's going to give them the relevant information," said Edd Wilder-James, former vice president of technology strategy at Silicon Valley Data Science and now an open source strategist at Google's TensorFlow, during a presentation at the Strata Data Conference. Looking to establish accountability across disparate project teams? Trying to automate processes or allow for lean methodology support?

This is interesting, but I wish they also included precision somehow. AUC ROC can look good when precision is in fact not so good. And there are a lot of scenarios when precision might be important, so measuring impact on that would have been nice.