This repo will contain different AI applied within IOT application especially created for intel distribution.
- Overview
- Foundation Course
- List of projects you will develop
- What you will be learned?
- What it Does?
- How it Works?
- View Certificate
After completing this degree, you will have enough knowledge to start implementing edge computing projects in your work. I am always learning and I take many online courses myself! So I know how difficult it was to take time out of your schedule and dedicate the many hours of hard work it took to complete this course. I know that there were some very difficult concepts to learn, some of the quizzes and coding exercises were tough, and the projects were challenging.
Visit this to see the activities and process to get enrollement to this program.
Content to view all the projects are listed below:
This program contains three major projects with a deep concept of AI for IoT devices. This course cover topic as:
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In the first course, you learned about what edge computing is, how to use the model downloader to download models, and how to create Inference Engine pipelines.
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In the second course, you learned about different hardware types and their characteristics. You learned how to weigh the advantages and disadvantages of each hardware device and compare performance across devices in order to suggest devices for different real-world scenarios.
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In this last course, you learned about the different optimization techniques and tools you can use to improve the performance of your edge computing application. You first learned how to measure the complexities of your model and then learned different techniques to reduce both the size of your model and the number of operations in your model. You learned how to use the VTune Amplifier to check for hotspots in your application code. And, finally, you learned how to package your model so that it can be deployed to different devices.
The people counter application will demonstrate how to create a smart video IoT solution using Intel® hardware and software tools. The app will detect people in a designated area, providing the number of people in the frame, average duration of people in frame, and total count.
The counter will use the Inference Engine included in the Intel® Distribution of OpenVINO™ Toolkit. The model used should be able to identify people in a video frame. The app should count the number of people in the current frame, the duration that a person is in the frame (time elapsed between entering and exiting a frame) and the total count of people. It then sends the data to a local web server using the Paho MQTT Python package.
You will choose a model to use and convert it with the Model Optimizer.
Please download the PDF to view it: Download PDF.