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5 Startups Playing Big, and Betting on the Future, with Deep Learning

Real Life Analytics: Accurate, Automatic Ads

To power targeted in-store ads, the U.K.’s Real Life Analytics offers retailers a webcam and a dongle to attach to a digital display. Seems simple. But the deep learning software running inside that dongle does astonishing things.

Approach the display’s webcam, and a deep learning neural network figures out your age and gender. In milliseconds, it flips on an ad targeting your demographic. Meanwhile, the deep learning network — designed with DIGITS deep learning training software using the cuDNN-accelerated Caffe framework — analyzes your real-time engagement. Running on our Tegra chip, of course.


ZZ Photo
ZZ Photo’s “DeepPet” algorithm is up to five times more accurate than traditional object recognition.

ZZ Photo: Putting Pets on the Pedestal

ZZ Photo, a startup based in Ukraine, can help you sort out the thousands of images you’ve stashed in your PCs. Using CUDA-enabled GPUs to speed up computations in their neural networks, ZZ Photo can detect images on PCs. It then sorts and arranges the photos, tagging them by face, scene or pet.

That’s right. ZZ Photo’s “DeepPet” algorithm can tell the difference between your labradoodle and chiweeni. It’s up to five times more accurate than traditional object recognition algorithms in identifying cats and dogs.

MicroBlink: Math-Solving App Heads to No. 1

MicroBlink PhotoMath app
MicroBlink’s PhotoMatch app reads and solves mathematical problems in real time.

With students recently returning to school, MicroBlink’s PhotoMath app headed to the top of the class as the No. 1 iPhone free U.S. download in early September. The app reads and solves mathematical problems in real time. Just take a picture of the problem with your smart phone or tablet.

MicroBlink, founded in Zagreb, Croatia, uses NVIDIA GPUs to train PhotoMath’s deep learning algorithms. The app can now handle fractions, inequalities, quadratic equations and more. It makes math simple by showing users how to solve math problems step by step. And parents rave about how the tool checks their kids’ homework.

HyperVerge: Innovative Image Identification

Forget scrolling past a series of selfies to find a photo of your driver’s license. HyperVerge, a startup out of India, has developed Silver. The mobile image recognition app uses GPUs for data processing and training their application engines.

The app sorts photos on mobile devices. It categorizes photos as faces, screenshots, and memes. It even identifies documents — a category that includes handwritten notes, ID scans and checks. HyperVerge has also developed tools to delete poor quality and duplicate photos.

ViSenze: Search Without Keywords

ViSenze’s image recognition technology powers visual search with uncanny accuracy.

If a picture is worth a thousand words, why are we doing so much typing into search engines? ViSenze, a Singapore-based startup, lets you search e-commerce platforms visually. Drop an image into its deep learning-powered platform and it quickly pulls up scores of similar images, without relying on keywords or manual image tagging.

In fact, its image recognition technology automatically does the tagging by attributes such as shape, color and pattern. So, for example, if you’ve found a dress but want to see similar sleeveless versions from your favorite e-tailers, or if you like a handbag but want to see variations in leather or with a tapered shape, ViSenze zeroes in with amazing accuracy and speed.

Bringing Massive Computing Power to the Masses

These are just a few of the startups using our GPUs to embrace the deep learning revolution. It’s no surprise. GPU acceleration is ideal for the demands of deep learning algorithms. These algorithms power applications in fields ranging from medical imaging analysis to self-driving cars.

Training computers on these algorithms requires they teach themselves. To do that, they process enormous amounts of data. Our DIGITS deep learning software and cuDNN programming library speed things along. For off-the-shelf capability, there’s the DIGITS DevBox. Combining four NVIDIA GeForce GTX TITAN X GPUs, DIGITS software and deep learning tools, it’s the world’s fastest deskside deep learning machine.

With tools like these, a startup can be as equipped to tackle deep learning problems as tech leaders with huge server rooms.

There’s no better place for GPU-using startups to highlight their groundbreaking work than the annual Emerging Companies Summit, where we’ll award $100,000 to the most promising venture. The daylong event, part of our annual GPU Technology Conference, will take place on April 6, 2016.

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My name is Sayed Ahmadreza Razian and I am a graduate of the master degree in Artificial intelligence .
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Related topics such as image processing, machine vision, virtual reality, machine learning, data mining, and monitoring systems are my research interests, and I intend to pursue a PhD in one of these fields.

جهت نمایش صفحه معرفی و رزومه کلیک کنید

My Scientific expertise
  • Image processing
  • Machine vision
  • Machine learning
  • Pattern recognition
  • Data mining - Big Data
  • CUDA Programming
  • Game and Virtual reality

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