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Embedded Vision Alliance Announces New Training Class: Deep Learning for Computer Vision with TensorFlow

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WALNUT CREEK, Calif., June 12, 2017 /PRNewswire/ — The Embedded Vision Alliance today announced its new Deep Learning for Computer Vision with TensorFlow training class on July 13th in Santa Clara, Calif. During this hands-on class, attendees will learn how to use TensorFlow, the popular, open source framework for deep learning, to create and train models for computer vision solutions, including medical, consumer, retail, public safety and automotive applications.

Deep Learning for Computer Vision with TensorFlow
Date: Thursday, July 13, 2017
Time: 9 a.m. – 5 p.m.
Location: Hyatt Regency Santa Clara, 1501 Great America Parkway, Santa Clara, Calif.
To register: click here

The computer vision space is experiencing a dramatic technology shift, with a growing number of engineers using deep learning and convolutional neural networks to develop the next-generation of computers and machines that will see and understand the world around them. The Embedded Vision Alliance developed this interactive class specifically for engineers who are creating algorithms and developing software for visual machine solutions. The class is being held in eight cities in the U.S. and Europe, over the next 12 months. To learn more, visit www.embedded-vision.com/tensorflow.

About The Embedded Vision Alliance

The Embedded Vision Alliance is a worldwide industry partnership bringing together technology providers and end product companies who are enabling innovative and practical applications for computer vision. Its members include providers of services and technologies for computer vision, including processors, sensors, and software, for a range of market segments and applications, including automotive, consumer electronics, gaming, imaging, and more. Membership is open to any company that supplies or uses technology for computer vision systems and applications. For more information on the Alliance, visit https://www.embedded-vision.com.

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Julie Seymour
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Email: [email protected]

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