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Matt Rich,
Megan Thompson,
Amanda Wang,
Brandon Armstrong,
and
Mehdi Alemi
Detecting and locating objects is one of the most common uses of deep learning for computer vision. Applications include helping autonomous systems navigate complex environments, locating medical conditions like tumors, and identifying ready-to-harvest crops in agriculture. In the course projects, you will apply detection models to real-world scenarios and train a model to detect various parking signs. Completing this course will give you the skills to train detection models for your application.
Read more
Detecting and locating objects is one of the most common uses of deep learning for computer vision. Applications include helping autonomous systems navigate complex environments, locating medical conditions like tumors, and identifying ready-to-harvest crops in agriculture. In the course projects, you will apply detection models to real-world scenarios and train a model to detect various parking signs. Completing this course will give you the skills to train detection models for your application.
Read more
Detecting and locating objects is one of the most common uses of deep learning for computer vision. Applications include helping autonomous systems navigate complex environments, locating medical conditions like tumors, and identifying ready-to-harvest crops in agriculture. In the course projects, you will apply detection models to real-world scenarios and train a model to detect various parking signs. Completing this course will give you the skills to train detection models for your application.
By the end of this course, you will be able to:
• Explain how deep learning networks locate and classify objects in images
• Retrain popular YOLO deep learning models for your application
• Use a variety of metrics to evaluate prediction results
• Visualize results to gain insights into model performance
• Improve model performance by adjusting important model parameters
• Analyze labeled images to identify and fix potential shortcomings in your data
For the duration of the course, you will have free access to MATLAB, software used by top employers worldwide. The courses draw on the applications using MATLAB, so you spend less time coding and more time applying deep learning concepts.
Register for this course and see more details by visiting:
OpenCourser.com/course/arw4hx/deep
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What's inside
Syllabus
Detecting Objects with Pre-trained Models
Get started with object detection by using pre-trained models
Training Object Detection Models
Use transfer learning to retrain YOLO models for new applications
Read more
Syllabus
Detecting Objects with Pre-trained Models
Get started with object detection by using pre-trained models
Training Object Detection Models
Use transfer learning to retrain YOLO models for new applications
Read more
Evaluating Object Detection Models
Use metrics like recall, precision, and mean average precision to evaluate your models
Final Project: Train and Evaluate a Detection Model
Apply the full object detection workflow on a final project
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Examines detecting and locating objects using deep learning, which is standard in computer vision
Taught by instructors who are recognized for their work in object detection
Develops skills in training detection models for various applications
Offers hands-on labs and interactive materials for practical learning
Suitable for learners interested in computer vision and deep learning applications
Requires access to MATLAB software, which may pose a financial barrier for some learners
Save this course
Save Deep Learning for Object Detection to your list so you can find it easily later:
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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 Deep Learning for Object Detection with these
activities:
Organize your course notes and assignments
Show steps
Improve your ability to find and use course materials.
Show steps
-
Create a system for organizing your notes, assignments, and other course materials.
-
Review your notes and assignments regularly to reinforce your learning.
Review basic programming concepts
Show steps
Ensure you have a strong foundation in programming before taking this course.
Browse courses on
Programming
Show steps
-
Review the basics of programming, such as variables, data types, and control flow.
-
Practice writing simple programs in Python or MATLAB.
Review basic linear algebra concepts
Show steps
Ensure you have a strong foundation in linear algebra before taking this course.
Browse courses on
Linear Algebra
Show steps
-
Review the basics of linear algebra, such as vectors, matrices, and eigenvalues.
-
Practice solving linear algebra problems.
Seven other activities
Expand to see all activities and additional details
Show all ten activities
Read Introduction to Deep Learning
Show steps
Build a solid foundation in deep learning concepts and techniques before taking this course.
View
Deep Learning (Adaptive Computation and Machine...
on Amazon
Show steps
-
Read Chapters 1-3 to gain an overview of deep learning.
-
Complete the exercises in Chapters 1-3 to practice implementing deep learning algorithms.
Join a study group to discuss object detection concepts
Show steps
Engage with other students to deepen your understanding of the course material.
Browse courses on
Object Detection
Show steps
-
Find a study group or create your own.
-
Meet regularly to discuss the course material.
-
Work together on practice problems and projects.
Practice using the YOLO object detection model
Show steps
Develop proficiency in using the YOLO object detection model.
Browse courses on
Yolo
Show steps
-
Follow the YOLO documentation to install the model and its dependencies.
-
Load a dataset of images.
-
Use the YOLO model to detect objects in the images.
-
Evaluate the performance of the model on the dataset.
Practice Object Detection Models for Classification
Show steps
Improve your understanding of how deep learning neural networks can classify objects in images.
Browse courses on
Deep Learning
Show steps
-
Load the dataset into a machine learning framework.
-
Select a dataset of labeled images.
-
Train and evaluate different object detection models.
-
Make predictions on new images.
Build a simple object detection model
Show steps
Apply the concepts learned in the course to a practical project.
Browse courses on
Object Detection
Show steps
-
Choose a dataset of images with objects you want to detect.
-
Train a simple object detection model using a pre-trained model as a starting point.
-
Evaluate the performance of your model on a test set of images.
Write a blog post about object detection
Show steps
Reinforce your understanding of object detection by explaining it to others.
Browse courses on
Object Detection
Show steps
-
Choose a specific aspect of object detection to write about.
-
Research the topic thoroughly.
-
Write a clear and concise blog post that explains the topic in a way that is accessible to a non-expert audience.
Contribute to an open-source object detection project
Show steps
Gain practical experience by contributing to an open-source object detection project.
Browse courses on
Object Detection
Show steps
-
Find an open-source object detection project that you are interested in.
-
Read the project documentation to understand its goals and how to contribute.
-
Identify a small contribution that you can make to the project.
-
Submit a pull request to the project with your contribution.
Organize your course notes and assignments
Show steps
Improve your ability to find and use course materials.
Show steps
Show steps
Show steps
- Create a system for organizing your notes, assignments, and other course materials.
- Review your notes and assignments regularly to reinforce your learning.
Review basic programming concepts
Show steps
Ensure you have a strong foundation in programming before taking this course.
Browse courses on
Programming
Show steps
Show steps
Show steps
- Review the basics of programming, such as variables, data types, and control flow.
- Practice writing simple programs in Python or MATLAB.
Review basic linear algebra concepts
Show steps
Ensure you have a strong foundation in linear algebra before taking this course.
Browse courses on
Linear Algebra
Show steps
Show steps
Show steps
- Review the basics of linear algebra, such as vectors, matrices, and eigenvalues.
- Practice solving linear algebra problems.
Seven other activities
Expand to see all activities and additional details
Show all ten activities
Read Introduction to Deep Learning
Show steps
Build a solid foundation in deep learning concepts and techniques before taking this course.
View
Deep Learning (Adaptive Computation and Machine...
on Amazon
Show steps
Show steps
Show steps
- Read Chapters 1-3 to gain an overview of deep learning.
- Complete the exercises in Chapters 1-3 to practice implementing deep learning algorithms.
Join a study group to discuss object detection concepts
Show steps
Engage with other students to deepen your understanding of the course material.
Browse courses on
Object Detection
Show steps
Show steps
Show steps
- Find a study group or create your own.
- Meet regularly to discuss the course material.
- Work together on practice problems and projects.
Practice using the YOLO object detection model
Show steps
Develop proficiency in using the YOLO object detection model.
Browse courses on
Yolo
Show steps
Show steps
Show steps
- Follow the YOLO documentation to install the model and its dependencies.
- Load a dataset of images.
- Use the YOLO model to detect objects in the images.
- Evaluate the performance of the model on the dataset.
Practice Object Detection Models for Classification
Show steps
Improve your understanding of how deep learning neural networks can classify objects in images.
Browse courses on
Deep Learning
Show steps
Show steps
Show steps
- Load the dataset into a machine learning framework.
- Select a dataset of labeled images.
- Train and evaluate different object detection models.
- Make predictions on new images.
Build a simple object detection model
Show steps
Apply the concepts learned in the course to a practical project.
Browse courses on
Object Detection
Show steps
Show steps
Show steps
- Choose a dataset of images with objects you want to detect.
- Train a simple object detection model using a pre-trained model as a starting point.
- Evaluate the performance of your model on a test set of images.
Write a blog post about object detection
Show steps
Reinforce your understanding of object detection by explaining it to others.
Browse courses on
Object Detection
Show steps
Show steps
Show steps
- Choose a specific aspect of object detection to write about.
- Research the topic thoroughly.
- Write a clear and concise blog post that explains the topic in a way that is accessible to a non-expert audience.
Contribute to an open-source object detection project
Show steps
Gain practical experience by contributing to an open-source object detection project.
Browse courses on
Object Detection
Show steps
Show steps
Show steps
- Find an open-source object detection project that you are interested in.
- Read the project documentation to understand its goals and how to contribute.
- Identify a small contribution that you can make to the project.
- Submit a pull request to the project with your contribution.
Career center
Learners who complete Deep Learning for Object Detection will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
Data scientists develop, deploy, and interpret machine learning models to build predictive analytics products. This course will be particularly useful for data scientists looking to specialize in computer vision because it provides hands-on experience with deep learning models for object detection. Students will learn how to retrain popular YOLO models for their own applications, use a variety of metrics to evaluate prediction results, and visualize results to gain insights into model performance.
Machine Learning Engineer
Machine Learning Engineer
Machine learning engineers design, build, and maintain machine learning models. This course will provide machine learning engineers with the skills to develop deep learning models for object detection. Students will learn how to retrain popular YOLO models for their own applications, use a variety of metrics to evaluate prediction results, and visualize results to gain insights into model performance.
Computer Vision Engineer
Computer Vision Engineer
Computer vision engineers develop and implement computer vision systems. This course will be useful for computer vision engineers who want to learn how to use deep learning models for object detection. Students will learn how to retrain popular YOLO models for their own applications, use a variety of metrics to evaluate prediction results, and visualize results to gain insights into model performance.
Software Engineer
Software Engineer
Software engineers design, develop, and maintain software systems. This course may be useful for software engineers who want to learn how to use deep learning models for object detection. Students will learn how to retrain popular YOLO models for their own applications, use a variety of metrics to evaluate prediction results, and visualize results to gain insights into model performance.
Data Analyst
Data Analyst
Data analysts collect, analyze, and interpret data to identify trends and patterns. This course may be useful for data analysts who want to learn how to use deep learning models for object detection. Students will learn how to retrain popular YOLO models for their own applications, use a variety of metrics to evaluate prediction results, and visualize results to gain insights into model performance.
Product Manager
Product Manager
Product managers oversee the development and launch of new products. This course may be useful for product managers who want to learn how to use deep learning models for object detection. Students will learn how to retrain popular YOLO models for their own applications, use a variety of metrics to evaluate prediction results, and visualize results to gain insights into model performance.
Business Analyst
Business Analyst
Business analysts identify and solve business problems. This course may be useful for business analysts who want to learn how to use deep learning models for object detection. Students will learn how to retrain popular YOLO models for their own applications, use a variety of metrics to evaluate prediction results, and visualize results to gain insights into model performance.
Technical Writer
Technical Writer
Technical writers create documentation for software and hardware products. This course may be useful for technical writers who want to learn how to write about deep learning models for object detection. Students will learn how to retrain popular YOLO models for their own applications, use a variety of metrics to evaluate prediction results, and visualize results to gain insights into model performance.
Marketing Manager
Marketing Manager
Marketing managers develop and execute marketing campaigns. This course may be useful for marketing managers who want to learn how to use deep learning models for object detection. Students will learn how to retrain popular YOLO models for their own applications, use a variety of metrics to evaluate prediction results, and visualize results to gain insights into model performance.
Sales Manager
Sales Manager
Sales managers oversee the sales team and develop sales strategies. This course may be useful for sales managers who want to learn how to use deep learning models for object detection. Students will learn how to retrain popular YOLO models for their own applications, use a variety of metrics to evaluate prediction results, and visualize results to gain insights into model performance.
Customer Success Manager
Customer Success Manager
Customer success managers help customers get the most value from their products and services. This course may be useful for customer success managers who want to learn how to use deep learning models for object detection. Students will learn how to retrain popular YOLO models for their own applications, use a variety of metrics to evaluate prediction results, and visualize results to gain insights into model performance.
Project Manager
Project Manager
Project managers plan and execute projects. This course may be useful for project managers who want to learn how to use deep learning models for object detection. Students will learn how to retrain popular YOLO models for their own applications, use a variety of metrics to evaluate prediction results, and visualize results to gain insights into model performance.
Account Manager
Account Manager
Account managers manage customer relationships and sales. This course may be useful for account managers who want to learn how to use deep learning models for object detection. Students will learn how to retrain popular YOLO models for their own applications, use a variety of metrics to evaluate prediction results, and visualize results to gain insights into model performance.
Recruiter
Recruiter
Recruiters find and hire new employees. This course may be useful for recruiters who want to learn how to use deep learning models for object detection. Students will learn how to retrain popular YOLO models for their own applications, use a variety of metrics to evaluate prediction results, and visualize results to gain insights into model performance.
Office Manager
Office Manager
Office managers oversee the day-to-day operations of an office. This course may be useful for office managers who want to learn how to use deep learning models for object detection. Students will learn how to retrain popular YOLO models for their own applications, use a variety of metrics to evaluate prediction results, and visualize results to gain insights into model performance.
For more career information including salaries, visit:
OpenCourser.com/course/arw4hx/deep
Reading list
We've selected 13 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
Deep Learning for Object Detection.
Save
Provides a comprehensive overview of deep learning, including object detection. It covers the latest advances in deep learning models and techniques and provides hands-on examples of how to train and evaluate deep learning models.
Provides a comprehensive overview of deep learning for computer vision, covering topics such as image classification, object detection, and image segmentation. It valuable resource for anyone looking to learn more about this field.
Provides a comprehensive overview of deep learning with Python, including object detection. It covers the latest advances in deep learning with Python models and techniques and provides hands-on examples of how to train and evaluate deep learning with Python models.
Provides a comprehensive overview of deep learning for object detection and recognition. It covers the latest advances in deep learning models and techniques and provides hands-on examples of how to train and evaluate object detection and recognition models.
Save
Provides a comprehensive overview of machine learning, including object detection. It covers the latest advances in machine learning models and techniques and provides hands-on examples of how to train and evaluate machine learning models.
Save
Provides a comprehensive overview of computer vision algorithms and applications. It covers a wide range of topics, including image processing, feature extraction, and object recognition. It valuable resource for anyone looking to learn more about this field.
Provides a comprehensive overview of pattern recognition and machine learning, including object detection. It covers the latest advances in pattern recognition and machine learning models and techniques and provides hands-on examples of how to train and evaluate pattern recognition and machine learning models.
Provides a comprehensive overview of computer science, including object detection. It covers the latest advances in computer science models and techniques and provides hands-on examples of how to train and evaluate computer science models.
Provides a comprehensive overview of statistical learning with sparsity, including object detection. It covers the latest advances in statistical learning with sparsity models and techniques and provides hands-on examples of how to train and evaluate statistical learning with sparsity models.
Provides a practical guide to object detection with deep learning. It covers topics such as data preparation, model training, and evaluation. It valuable resource for anyone looking to learn more about this field.
Provides a comprehensive overview of convex optimization, including object detection. It covers the latest advances in convex optimization models and techniques and provides hands-on examples of how to train and evaluate convex optimization models.
Provides a comprehensive overview of computer vision algorithms and applications. It covers a wide range of topics, including image processing, feature extraction, and object recognition. It valuable resource for anyone looking to learn more about this field.
Provides a tutorial on deep learning for computer vision. It covers topics such as image classification, object detection, and image segmentation. It valuable resource for anyone looking to learn more about this field.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/arw4hx/deep
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Level
Intermediate
Via
Coursera
Institution
MathWorks
Instructors
Matt Rich
Megan Thompson
Amanda Wang
Brandon Armstrong
Mehdi Alemi
Language
English
This course belongs to a
collection
called
Deep Learning for Computer Vision. It's comprised of three courses:
- Introduction to Deep Learning for Computer Vision
- Deep Learning for Object Detection (viewing)
- Advanced Deep Learning Techniques for Computer Vision
Good to know
Examines detecting and locating objects using deep learning, which is standard in computer vision
Taught by instructors who are recognized for their work in object detection
Develops skills in training detection models for various applications
Offers hands-on labs and interactive materials for practical learning
Suitable for learners interested in computer vision and deep learning applications
Requires access to MATLAB software, which may pose a financial barrier for some learners
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