Download and Learn Intel Edge AI for IoT Developers Nanodegree for free with Google Drive Download Link. Lead the development of cutting-edge Edge AI applications for the future of the Internet of Things. Leverage the Intel® Distribution of OpenVINO™ Toolkit to fast-track development of high-performance computer vision & deep learning inference applications.
At 10 hours / week
November 25, 2020
Get access to classroom immediately on enrollment
Intermediate Python, and Experience with Deep Learning, Command Line, and OpenCV
See prerequisites in detail below.
IN COLLABORATION WITH
Download Udacity – Intel® Edge AI for IoT Developers Nanodegree for free
What Edge AI?
In Edge AI, the AI algorithms are processed locally on a hardware device, without requiring any connection. It uses data that is generated from the device and processes it to give real-time insights in less than few milliseconds.
APPLICATIONS For Edge AI:
Some Use cases for AI on the Edge
- Image analytics.
- Audio Analytics. AI and Deep Learning can analyze a visual scene in all its elements, much as an audio scene can be split into its basic parts to enable the following functions by deep learning.
- Inertial Sensor/Environmental Sensor Analytics.
Introduction Video For Intel® Edge AI for IoT Developers Nanodegree
What You Will Learn in Intel® Edge AI for IoT Developers Nanodegree
Lead the development of cutting-edge Edge AI applications that are the future of the Internet of Things. Leverage the Intel® Distribution of OpenVINO™ Toolkit to fast-track development of high-performance computer vision and deep learning inference applications.
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PREREQUISITE KNOWLEDGE for Intel® Edge AI for IoT Developers Nanodegree
- Edge AI Fundamentals with OpenVINO™Leverage a pre-trained model for computer vision inferencing. You will convert pre-trained models into the framework agnostic intermediate representation with the Model Optimizer, and perform efficient inference on deep learning models through the hardware-agnostic Inference Engine. Finally, you will deploy an app on the edge, including sending information through MQTT, and analyze model performance and use casesDEPLOY A PEOPLE COUNTER AT THE EDGE
- Hardware for Computer Vision & Deep Learning Application Deployment: Grow your expertise in choosing the right hardware. Identify key hardware specifications of various hardware types (CPU, VPU, FPGA, and Integrated GPU). Utilize the Intel® DevCloud for the Edge to test model performance and deploy power-efficient deep neural network inference on on the various hardware types. Finally, you will distribute workload on available compute devices in order to improve model performance.DESIGN A SMART QUEUING SYSTEM
- Optimization Techniques and Tools for Computer Vision & Deep Learning Applications: Learn how to optimize your model and application code to reduce inference time when running your model at the edge. Use different software optimization techniques to improve the inference time of your model. Calculate how computationally expensive your model is. Use the DL Workbench to optimize your model and benchmark the performance of your model. Use a VTune amplifier to find and fix hotspots in your application code. Finally, package your application code and data so that it can be easily deployed to multiple devices.BUILD A COMPUTER POINTER CONTROLLER
Intel® Edge AI for IoT Developers Nanodegree includes:
Real-world projects from industry experts
With real world projects and immersive content built in partnership with top tier companies, you’ll master the tech skills companies want.
Get a knowledgeable mentor who guides your learning and is focused on answering your questions, motivating you and keeping you on track.
Personal career coach and career services
You’ll have access to career coaching sessions, interview prep advice, and resume and online professional profile reviews to help you grow in your career.
Flexible learning program
Get a custom learning plan tailored to fit your busy life. Along with easy monthly payments you can learn at your own pace and reach your personal goals.
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