Learn About Computer Vision with Intel

Take a deep dive into the world of Artificial Intelligence and Computer Vision with Intel. 

Edge Computing is poised to bring the power of AI to the Internet of Things. To help you stay at the cutting-edge of AI technology, Intel offers you a series of courses to fast-track the development of high-performance computer vision and deep learning inference applications. Classes are now available in both Coursera* and Udacity*, each with a slightly different focus, but both exploring computer vision using the Intel® Distribution of OpenVINO™ toolkit.

These classes will help you enhance your current skill-set and advance your career in AI and edge computing. At the end of the course, you can opt for a certificate that can be added to your resume and social media profiles.

Computer Vision Fundamentals on Coursera*

Understand the fundamentals of deep learning, computer vision and hardware acceleration. These classes are a great place to learn about models, inference, optimization, and how to use the Intel Distribution of OpenVINO toolkit to fully realize your CV needs.

This course is intended for learners with no prior experience with computer vision, although previous knowledge is helpful. This course is ideal for anyone interested in learning more about core concepts of computer vision applications and the Intel Distribution of OpenVINO toolkit.

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Edge AI Fundamentals on Udacity*

Start with the Fundamentals Course on Udacity and learn to use the Intel Distribution of OpenVINO toolkit to deploy computer vision capabilities inside a range of edge applications.

In the subsequent Nanodegree program, you will learn to leverage the potential of edge computing using Intel Distribution of OpenVINO toolkit and pre-trained deep learning models through a high-level C++ or Python inference engine API integrated with application logic. Based on convolutional neural networks (CNN), the toolkit enables you to maximize performance while extending workloads across Intel® hardware (including accelerators).

The course is intended for students with some background in AI and computer vision, with experience in either Python or C++ and familiarity with command line basics.

Launch date: April, 2020

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