[UDACITY] INTEL® EDGE AI FOR IOT DEVELOPERS

[UDACITY] INTEL® EDGE AI FOR IOT DEVELOPERS
Nanodegree Program–nd131

Intel® Edge AI for IoT Developers

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.
  • Estimated Time
    3 Months

    At 10 hours / week

Prerequisites
Intermediate Python, and Experience with Deep Learning, Command Line, and OpenCV

In collaboration with

Intel

What You Will Learn

SYLLABUS

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

Prerequisite Knowledge

This program requires intermediate knowledge of Python, and experience with Deep Learning, Command Line, and 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

    Deploy 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

Learn with the best

Stewart Christie

Stewart Christie

Community Manager – IoT Developer Program at Intel®

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Stewart is a Technical Evangelist for Intel®, responsible for running workshops, creating content, and supporting the developer community in IoT. He is skilled in developing applications that interface hardware with software for computer vision, robotics, and language processing.

Michael Virgo

Michael Virgo

Senior Curriculum Manager at Udacity

After beginning his career in business, Michael utilized Udacity Nanodegree programs to build his technical skills, eventually becoming a Self-Driving Car Engineer at Udacity before switching roles to work on curriculum development for a variety of AI and Autonomous Systems programs.

Soham Chatterjee

Soham Chatterjee

Graduate Student at the Nanyang Technological University

Soham is an Intel® Software Innovator and a former Deep Learning Researcher at Saama Technologies. He is currently a Masters by Research student at NTU, Singapore. His research is on Edge Computing, IoT and Neuromorphic Hardware.

Vaidheeswaran Archana

Vaidheeswaran Archana

Graduate Student at the National University of Singapore

Archana is a graduate student at NUS. She is currently pursuing her research in Deep Learning and Smart Grids, under Professor Dipti Srinivasan. Archana is an Intel® Software Innovator and a former Deep Learning Engineer at Saama Technologies.

Get started with

Intel® Edge AI for IoT Developers

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Learn
Lead the development of cutting-edge Edge AI applications that are the future of the Internet of Things.

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Average Time
On average, successful students take null months to complete this program.

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Benefits include
  • Real-world projects from industry experts
  • Technical mentor support
  • Personal career coach & career services

blue stacked bills

STAY SHARP WHILE STAYING IN
  • Financial support available worldwide to help in this challenging time
  • Spend your time at home learning new, higher-paying job skills
  • Commit to a brighter future by learning today
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Program Details

PROGRAM OVERVIEW – WHY SHOULD I TAKE THIS PROGRAM?
Why should I enroll?
70% of data being created is at the edge, and only half of that will go to the public cloud; the rest will be stored and processed at the edge, which requires a different kind of developer. Demand for professionals with the Edge AI skills will be immense, as the Edge Artificial Intelligence (AI) software market size is forecasted to grow from $355 Million in 2018, to $1.15 billion by 2023, at an Annual Growth Rate of 27%.(MarketsandMarkets) In the Edge AI for IoT Developers Nanodegree program, you’ll leverage the potential of edge computing and use the Intel® Distribution of OpenVINO™ Toolkit to fast-track development of high-performance computer vision and deep learning inference applications.
Computer Vision is a fast-growing technology being deployed in nearly every industry from factory floors to amusement parks to shopping malls, smart buildings, and smart homes. It is also driving the evolution of machine learning and human interactions with intelligent systems. Additional applications include drones, security cameras, robots, facial recognition on cell phones, self-driving vehicles, and more, which means these industries and more all need developers with computer vision and deep learning IoT experience.
Size: 1.37GB

 

Course: https://www.udacity.com/course/intel-edge-ai-for-iot-developers-nanodegree–nd131
 
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