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Kimberly Karalekas
and
Vu Q Nguyen
This course is designed for application developers who wants to deploy computer vision inference workloads using the Intel® Distribution of OpenVINOTM toolkit. The course looks at computer vision neural network models from a variety of popular machine...
Read more
This course is designed for application developers who wants to deploy computer vision inference workloads using the Intel® Distribution of OpenVINOTM toolkit. The course looks at computer vision neural network models from a variety of popular machine...
Read more
This course is designed for application developers who wants to deploy computer vision inference workloads using the Intel® Distribution of OpenVINOTM toolkit. The course looks at computer vision neural network models from a variety of popular machine learning frameworks and covers writing a portable application capable of deploying inference on a range of compute devices.
The course is targeted for application developers, and places focus on examples and discussion of the development workflow. As such, the discussions include not only the details about how to use the toolkit itself, but topics like how to take benchmarks to compare compute devices or what to do when you encounter issues. The course is made so that it serves as a how-to guide for developing a computer vision inference deployment with the toolkit. By the end of the course, students will have the skillset necessary to deploy their own computer vision application using the toolkit.
Register for this course and see more details by visiting:
OpenCourser.com/course/ljquxi/intermediate
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Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Suitable for beginner to intermediate developers with working knowledge of computer vision frameworks
Focuses on practical application development with the Intel® Distribution of OpenVINOTM toolkit
Covers deploying inference on a range of compute devices, providing portability and flexibility
Includes hands-on examples, discussions, and troubleshooting tips to enhance understanding
Provides valuable knowledge for deploying computer vision applications using industry-standard tools
Instructors are recognized experts in the field of computer vision and OpenVINO toolkit
Save this course
Save Intermediate Intel® Distribution of OpenVINO™ toolkit for Deep Learning Applications to your list so you can find it easily later:
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Reviews summary
Useful but flawed deep learning toolkit course
This course on Intermediate Intel® Distribution of OpenVINO™ toolkit for Deep Learning Applications is intended to provide guidance for deploying computer vision applications with the Intel® Distribution of OpenVINOTM toolkit. However, this course has flaws that prevent it from being great. While reviewers consistently mention the course's usefulness and concise explanations, it is also plagued by frequent errors and a lack of exercise solutions. Despite this, learners may find it useful, especially with corrections for errors and the addition of exercise solutions.
Course is useful for deploying computer vision workloads.
Course is clear and easy to follow.
"A short, concise, and clear tutorial..."
Course has frequent errors with no support.
"Many errors in the course and no support from tutors in the forums."
Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Intermediate Intel® Distribution of OpenVINO™ toolkit for Deep Learning Applications with these
activities:
Organize your course materials
Show steps
Review and organize the materials provided for the course, including lecture notes, assignments, and readings, to ensure you have a clear understanding of the content and structure of the course.
Show steps
-
Create a dedicated folder or workspace for the course.
-
Save and organize lecture notes, assignments, and readings in the designated folder.
-
Create a system for naming and organizing files to ensure easy retrieval.
-
Review the course syllabus and make note of important dates and deadlines.
Practice coding in Python or C++
Show steps
Brush up on your Python or C++ coding skills to enhance your ability to develop computer vision applications using OpenVINO.
Browse courses on
Python
Show steps
-
Review basic programming concepts and syntax.
-
Complete coding exercises or practice problems.
-
Review sample code and examples related to computer vision.
-
Build a small computer vision application using Python or C++.
Review computer vision fundamentals
Show steps
Review the fundamentals of computer vision, including image processing, feature extraction, and object detection, to enhance your understanding of the course material.
Browse courses on
Computer Vision
Show steps
-
Read articles or blog posts on computer vision fundamentals.
-
Watch online tutorials or videos on computer vision.
-
Take a refresher course or workshop on computer vision.
-
Review your notes or textbooks from previous computer vision courses.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Join the OpenVINO™ community forum
Show steps
Join the OpenVINO™ community forum to connect with other developers and experts who can provide support and insights.
Browse courses on
Computer Vision
Show steps
-
Visit the OpenVINO™ community forum website.
-
Create an account.
-
Join relevant discussion threads.
-
Ask questions, share knowledge, and collaborate with other community members.
Intel OpenVINO™ toolkit documentation
Show steps
Review the official Intel OpenVINO™ toolkit documentation to better understand how OpenVINO deploys computer vision inference workflows.
Browse courses on
Computer Vision
Show steps
-
Visit the Intel OpenVINO™ toolkit website.
-
Navigate to the documentation section.
-
Explore the different sections of the documentation, including the getting started guide, tutorials, and API reference.
-
Take notes on the key concepts and steps involved in deploying computer vision inference workflows with OpenVINO.
Develop a plan for deploying a computer vision application using OpenVINO
Show steps
Create a deployment plan that outlines the steps involved in deploying a computer vision application using OpenVINO, including hardware and software requirements, performance considerations, and potential challenges.
Browse courses on
Computer Vision
Show steps
-
Identify the target application for deployment.
-
Research and select the appropriate OpenVINO™ hardware and software components.
-
Consider performance requirements and optimization strategies.
-
Identify potential challenges and develop mitigation plans.
-
Document the deployment plan.
Deploy a sample computer vision application using OpenVINO
Show steps
Deploy a sample computer vision application using OpenVINO to gain hands-on experience with the toolkit.
Browse courses on
Computer Vision
Show steps
-
Download the OpenVINO™ toolkit.
-
Install the OpenVINO™ toolkit.
-
Obtain a sample computer vision application, such as the object detection demo.
-
Follow the instructions to deploy the application using OpenVINO.
-
Test the deployed application to ensure it is working correctly.
Contribute to the OpenVINO™ toolkit
Show steps
Contribute to the OpenVINO™ toolkit to further your understanding of the technology and to give back to the community.
Browse courses on
Computer Vision
Show steps
-
Identify an area of the OpenVINO™ toolkit that you would like to contribute to.
-
Read the contribution guidelines.
-
Fork the OpenVINO™ toolkit repository.
-
Make your changes and submit a pull request.
-
Work with the OpenVINO™ community to review and merge your changes.
Organize your course materials
Show steps
Review and organize the materials provided for the course, including lecture notes, assignments, and readings, to ensure you have a clear understanding of the content and structure of the course.
Show steps
Show steps
Show steps
- Create a dedicated folder or workspace for the course.
- Save and organize lecture notes, assignments, and readings in the designated folder.
- Create a system for naming and organizing files to ensure easy retrieval.
- Review the course syllabus and make note of important dates and deadlines.
Practice coding in Python or C++
Show steps
Brush up on your Python or C++ coding skills to enhance your ability to develop computer vision applications using OpenVINO.
Browse courses on
Python
Show steps
Show steps
Show steps
- Review basic programming concepts and syntax.
- Complete coding exercises or practice problems.
- Review sample code and examples related to computer vision.
- Build a small computer vision application using Python or C++.
Review computer vision fundamentals
Show steps
Review the fundamentals of computer vision, including image processing, feature extraction, and object detection, to enhance your understanding of the course material.
Browse courses on
Computer Vision
Show steps
Show steps
Show steps
- Read articles or blog posts on computer vision fundamentals.
- Watch online tutorials or videos on computer vision.
- Take a refresher course or workshop on computer vision.
- Review your notes or textbooks from previous computer vision courses.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Join the OpenVINO™ community forum
Show steps
Join the OpenVINO™ community forum to connect with other developers and experts who can provide support and insights.
Browse courses on
Computer Vision
Show steps
Show steps
Show steps
- Visit the OpenVINO™ community forum website.
- Create an account.
- Join relevant discussion threads.
- Ask questions, share knowledge, and collaborate with other community members.
Intel OpenVINO™ toolkit documentation
Show steps
Review the official Intel OpenVINO™ toolkit documentation to better understand how OpenVINO deploys computer vision inference workflows.
Browse courses on
Computer Vision
Show steps
Show steps
Show steps
- Visit the Intel OpenVINO™ toolkit website.
- Navigate to the documentation section.
- Explore the different sections of the documentation, including the getting started guide, tutorials, and API reference.
- Take notes on the key concepts and steps involved in deploying computer vision inference workflows with OpenVINO.
Develop a plan for deploying a computer vision application using OpenVINO
Show steps
Create a deployment plan that outlines the steps involved in deploying a computer vision application using OpenVINO, including hardware and software requirements, performance considerations, and potential challenges.
Browse courses on
Computer Vision
Show steps
Show steps
Show steps
- Identify the target application for deployment.
- Research and select the appropriate OpenVINO™ hardware and software components.
- Consider performance requirements and optimization strategies.
- Identify potential challenges and develop mitigation plans.
- Document the deployment plan.
Deploy a sample computer vision application using OpenVINO
Show steps
Deploy a sample computer vision application using OpenVINO to gain hands-on experience with the toolkit.
Browse courses on
Computer Vision
Show steps
Show steps
Show steps
- Download the OpenVINO™ toolkit.
- Install the OpenVINO™ toolkit.
- Obtain a sample computer vision application, such as the object detection demo.
- Follow the instructions to deploy the application using OpenVINO.
- Test the deployed application to ensure it is working correctly.
Contribute to the OpenVINO™ toolkit
Show steps
Contribute to the OpenVINO™ toolkit to further your understanding of the technology and to give back to the community.
Browse courses on
Computer Vision
Show steps
Show steps
Show steps
- Identify an area of the OpenVINO™ toolkit that you would like to contribute to.
- Read the contribution guidelines.
- Fork the OpenVINO™ toolkit repository.
- Make your changes and submit a pull request.
- Work with the OpenVINO™ community to review and merge your changes.
Career center
Learners who complete Intermediate Intel® Distribution of OpenVINO™ toolkit for Deep Learning Applications will develop knowledge and skills
that may be useful to these careers:
Computer Vision Researcher
Computer Vision Researcher
Computer Vision Researchers research and develop new computer vision algorithms and techniques. This course may be useful because it provides a foundation in the Intel® Distribution of OpenVINO™ toolkit, which is a popular tool for developing computer vision applications. Additionally, this course covers topics such as how to take benchmarks to compare compute devices and what to do when you encounter issues, which are important skills for Computer Vision Researchers.
Data Scientist
Data Scientist
Data Scientists use data to build predictive models and insights. This course may be useful because it provides a foundation in the Intel® Distribution of OpenVINO™ toolkit, which is a popular tool for deploying machine learning models on a variety of compute devices. Additionally, this course covers topics such as how to take benchmarks to compare compute devices and what to do when you encounter issues, which are important skills for Data Scientists.
Machine Learning Researcher
Machine Learning Researcher
Machine Learning Researchers research and develop new machine learning algorithms and techniques. This course may be useful because it provides a foundation in the Intel® Distribution of OpenVINO™ toolkit, which is a popular tool for deploying machine learning models on a variety of compute devices. Additionally, this course covers topics such as how to take benchmarks to compare compute devices and what to do when you encounter issues, which are important skills for Machine Learning Researchers.
Project Manager
Project Manager
Project Managers plan, execute, and close projects. This course may be useful because it provides a foundation in the Intel® Distribution of OpenVINO™ toolkit, which is a popular tool for developing computer vision applications. Additionally, this course covers topics such as how to take benchmarks to compare compute devices and what to do when you encounter issues, which are important skills for Project Managers.
Business Analyst
Business Analyst
Business Analysts analyze business processes and systems to identify opportunities for improvement. This course may be useful because it provides a foundation in the Intel® Distribution of OpenVINO™ toolkit, which is a popular tool for developing computer vision applications. Additionally, this course covers topics such as how to take benchmarks to compare compute devices and what to do when you encounter issues, which are important skills for Business Analysts.
Software Engineer
Software Engineer
Software Engineers design, develop, and implement software applications. This course may be useful because it provides a foundation in the Intel® Distribution of OpenVINO™ toolkit, which is a popular tool for developing computer vision applications. Additionally, this course covers topics such as how to take benchmarks to compare compute devices and what to do when you encounter issues, which are important skills for Software Engineers.
Data Analyst
Data Analyst
Data Analysts collect, clean, and analyze data to identify patterns and trends. This course may be useful because it provides a foundation in the Intel® Distribution of OpenVINO™ toolkit, which is a popular tool for deploying machine learning models on a variety of compute devices. Additionally, this course covers topics such as how to take benchmarks to compare compute devices and what to do when you encounter issues, which are important skills for Data Analysts.
Product Manager
Product Manager
Product Managers plan, develop, and launch new products and services. This course may be useful because it provides a foundation in the Intel® Distribution of OpenVINO™ toolkit, which is a popular tool for developing computer vision applications. Additionally, this course covers topics such as how to take benchmarks to compare compute devices and what to do when you encounter issues, which are important skills for Product Managers.
Computer Scientist
Computer Scientist
Computer Scientists research and develop new computer technologies and applications. This course may be useful because it provides a foundation in the Intel® Distribution of OpenVINO™ toolkit, which is a popular tool for developing computer vision applications. Additionally, this course covers topics such as how to take benchmarks to compare compute devices and what to do when you encounter issues, which are important skills for Computer Scientists.
Robotics Engineer
Robotics Engineer
Robotics Engineers design, develop, and implement robots and robotic systems. This course may be useful because it provides a foundation in the Intel® Distribution of OpenVINO™ toolkit, which is a popular tool for developing computer vision applications. Additionally, this course covers topics such as how to take benchmarks to compare compute devices and what to do when you encounter issues, which are important skills for Robotics Engineers.
Artificial Intelligence Engineer
Artificial Intelligence Engineer
Artificial Intelligence Engineers design, develop, and implement artificial intelligence systems. This course may be useful because it provides a foundation in the Intel® Distribution of OpenVINO™ toolkit, which is a popular tool for deploying machine learning models on a variety of compute devices. Additionally, this course covers topics such as how to take benchmarks to compare compute devices and what to do when you encounter issues, which are important skills for Artificial Intelligence Engineers.
Deep Learning Engineer
Deep Learning Engineer
Deep Learning Engineers design, develop, and implement deep learning models. This course may be useful because it provides a foundation in the Intel® Distribution of OpenVINO™ toolkit, which is a popular tool for deploying machine learning models on a variety of compute devices. Additionally, this course covers topics such as how to take benchmarks to compare compute devices and what to do when you encounter issues, which are important skills for Deep Learning Engineers.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design, develop, and implement machine learning models and algorithms. This course may be useful because it provides a foundation in the Intel® Distribution of OpenVINO™ toolkit, which is a popular tool for deploying machine learning models on a variety of compute devices.
Computer Vision Engineer
Computer Vision Engineer
Computer Vision Engineers are responsible for developing computer vision models and designing software applications that use computer vision. This course may be useful because it provides a foundation in the Intel® Distribution of OpenVINO™ toolkit, which is a popular tool for developing computer vision applications.
For more career information including salaries, visit:
OpenCourser.com/course/ljquxi/intermediate
Reading list
We've selected seven books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Intermediate Intel® Distribution of OpenVINO™ toolkit for Deep Learning Applications.
Provides a comprehensive overview of OpenCV 4, a popular open-source library for computer vision. It covers everything from the basics of image processing to advanced topics such as object detection and tracking.
Save
Provides a comprehensive overview of computer vision. It covers everything from the basics of image processing to advanced topics such as object detection and recognition.
Provides a comprehensive overview of deep learning. It covers everything from the basics of deep learning to advanced topics such as convolutional neural networks and recurrent neural networks.
Provides a comprehensive overview of artificial intelligence. It covers everything from the basics of AI to advanced topics such as machine learning and natural language processing.
Provides a practical introduction to deep learning using Fastai and PyTorch. It covers everything from the basics of deep learning to advanced topics such as object detection and natural language processing.
Provides a practical introduction to machine learning using Scikit-Learn, Keras, and TensorFlow. It covers everything from the basics of machine learning to advanced topics such as deep learning and natural language processing.
Provides a practical introduction to deep learning using R. It covers everything from the basics of deep learning to advanced topics such as object detection and natural language processing.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/ljquxi/intermediate
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Via
Coursera
Institution
Intel
Instructors
Kimberly Karalekas
Vu Q Nguyen
Good to know
Suitable for beginner to intermediate developers with working knowledge of computer vision frameworks
Focuses on practical application development with the Intel® Distribution of OpenVINOTM toolkit
Covers deploying inference on a range of compute devices, providing portability and flexibility
Includes hands-on examples, discussions, and troubleshooting tips to enhance understanding
Provides valuable knowledge for deploying computer vision applications using industry-standard tools
Instructors are recognized experts in the field of computer vision and OpenVINO toolkit
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