Download and Learn Intel® Edge AI for IoT Developers Udacity Nanodegree Course 2023 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.
In collaboration with
What You’ll Learn in Intel® Edge AI for IoT Developers Nanodegree
Intel® Edge AI for IoT Developers
Leverage the Intel® Distribution of OpenVINO™ Toolkit to fast-track development of high-performance computer vision and deep learning inference applications, and run pre-trained deep learning models for computer vision on-premise. You will identify key hardware specifications of various hardware types (CPU, VPU, FPGA, and Integrated GPU), and utilize the Intel® DevCloud for the Edge to test model performance on the various hardware types. Finally, you will use software tools to optimize deep learning models to improve performance of Edge AI systems.
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.
3 months to complete
Intel® Edge AI for IoT Developers Nanodegree Intro Video:
Prerequisite Knowledge
This program requires intermediate knowledge of Python, and experience with Deep Learning, Command Line, and OpenCV.
To succeed in this program, students should have the following:
- Intermediate knowledge of programming in Python
- Experience with training and deploying deep learning models
- Familiarity with different DL layers and architectures (CNN based)
- Familiarity with the command line (bash terminal)
- Experience using OpenCV
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 cases
Project – Deploy a People Counter at the Edge
Investigate different pre-trained models for person detection, and detect the number of people in the frame, and the time spent there.
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.
Project – Design a Smart Queuing System
Build custom queuing systems for the retail, manufacturing and transportation sectors and use the Intel® DevCloud for the Edge to test your solutions performance.
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.
Project – Build a Computer Pointer Controller
Use models available in the OpenVINO™ toolkit to control your computer pointer using your eye gaze.
Intel® Edge AI for IoT Developers Nanodegree Free Download Link:
