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Matt Rich,
Megan Thompson,
Amanda Wang,
Brandon Armstrong,
and
Mehdi Alemi
Visual inspection and medical imaging are two applications that aim to find anything unusual in images. In this course, you’ll train and calibrate specialized models known as anomaly detectors to identify defects. You’ll also use advanced techniques to overcome common data challenges with deep learning. AI-assisted labeling is a technique to auto-label images, saving time and money when you have tens of thousands of images. If you have too few images, you’ll generate synthetic training images using data augmentation for situations where acquiring more data is expensive or impossible.
Read more
Visual inspection and medical imaging are two applications that aim to find anything unusual in images. In this course, you’ll train and calibrate specialized models known as anomaly detectors to identify defects. You’ll also use advanced techniques to overcome common data challenges with deep learning. AI-assisted labeling is a technique to auto-label images, saving time and money when you have tens of thousands of images. If you have too few images, you’ll generate synthetic training images using data augmentation for situations where acquiring more data is expensive or impossible.
Read more
Visual inspection and medical imaging are two applications that aim to find anything unusual in images. In this course, you’ll train and calibrate specialized models known as anomaly detectors to identify defects. You’ll also use advanced techniques to overcome common data challenges with deep learning. AI-assisted labeling is a technique to auto-label images, saving time and money when you have tens of thousands of images. If you have too few images, you’ll generate synthetic training images using data augmentation for situations where acquiring more data is expensive or impossible.
By the end of this course, you will be able to:
• Train anomaly detection models
• Generate synthetic training images using data augmentation
• Use AI-assisted annotation to label images and video files
• Import models from 3rd party tools like PyTorch
• Describe approaches to using your model outside of MATLAB
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/jtgb8a/advanced
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What's inside
Syllabus
Anomaly Detection
Train anomaly detection models. These models do not find specific objects or classes, but instead find unusual regions in images.
Read more
Syllabus
Anomaly Detection
Train anomaly detection models. These models do not find specific objects or classes, but instead find unusual regions in images.
Read more
Data Augmentation
Generate synthetic images to use for training models.
Model-Assisted Labeling
Save hours of manual labor by using model-assisted labeling to prepare images for object detection
Creating Your Own Models
Learn how to diagnose problems when training models for your applications. Also, learn the options available to share and use your model outside of MATLAB.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Taught by experts in the field, and this course is enhanced with MATLAB software
Demonstrates proven techniques to detect anomalies, generate training images, and implement AI-assisted annotation
Provides advanced techniques to overcome challenges faced in deep learning
Suitable for those with prior knowledge in image processing and deep learning
Save this course
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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 Advanced Deep Learning Techniques for Computer Vision with these
activities:
Read 'Deep Learning with Python'
Show steps
This book provides a comprehensive overview of deep learning concepts and techniques, which will enhance your understanding of the course material.
View
Deep Learning with Python, Second Edition
on Amazon
Show steps
-
Purchase or borrow the book.
-
Read Chapters 1-5.
-
Complete the practice exercises and quizzes in the book.
Build an Anomaly Detection App
Show steps
Build a hands-on project to apply the concepts of anomaly detection learned in the course, reinforcing your understanding.
Browse courses on
Anomaly Detection
Show steps
-
Identify a suitable dataset for anomaly detection.
-
Design and train an anomaly detection model using MATLAB.
-
Develop a user interface for the app.
-
Test and evaluate the app's performance.
Follow a tutorial on model-assisted labeling
Show steps
Engaging in a tutorial will supplement the course material by providing a step-by-step guide to using model-assisted labeling techniques, which will enhance your practical skills.
Show steps
-
Find a tutorial that covers model-assisted labeling in MATLAB.
-
Follow the tutorial and apply the techniques to a small dataset.
Six other activities
Expand to see all activities and additional details
Show all nine activities
Attend a deep learning meetup
Show steps
Networking with professionals in the field will expose you to industry trends, potential job opportunities, and diverse perspectives, enriching your learning experience.
Browse courses on
Deep Learning
Show steps
-
Find a local or virtual deep learning meetup group.
-
Attend a meetup and engage in discussions with other members.
Diagnosis and Debugging Challenges
Show steps
Engage in practice drills that simulate real-world challenges to hone your skills in diagnosing and debugging anomaly detection models.
Browse courses on
Troubleshooting
Show steps
-
Analyze error messages and exception reports.
-
Identify and fix common pitfalls in model training.
-
Optimize model parameters to improve accuracy.
Complete practice exercises on importing models from PyTorch
Show steps
Completing practice exercises will reinforce your understanding of how to import and use models from PyTorch, which will expand your toolkit for deep learning.
Browse courses on
PyTorch
Show steps
-
Find online resources or textbooks with practice exercises on importing PyTorch models.
-
Complete the exercises to gain hands-on experience.
Build a simple anomaly detection model
Show steps
By building a hands-on project that applies the anomaly detection techniques learned in the course, you will solidify your understanding and gain practical experience.
Browse courses on
Anomaly Detection
Show steps
-
Gather a dataset of images containing anomalies.
-
Preprocess the dataset and split it into training and validation sets.
-
Train an anomaly detection model using MATLAB.
-
Evaluate the model's performance on the validation set.
-
Deploy the model for real-world use.
Create a blog post about data augmentation
Show steps
Sharing your understanding of data augmentation through a blog post will reinforce your knowledge and potentially assist others in grasping the concept.
Browse courses on
Data Augmentation
Show steps
-
Choose a specific data augmentation technique to focus on.
-
Write a blog post explaining the technique, its benefits, and its limitations.
-
Include code examples and visual demonstrations to illustrate the technique.
Participate in a hackathon focused on deep learning
Show steps
Participating in a hackathon will challenge you to apply your deep learning skills in a collaborative and competitive environment, fostering innovation and problem-solving abilities.
Browse courses on
Deep Learning
Show steps
-
Find a hackathon that aligns with your interests and skill level.
-
Form a team or join an existing one.
-
Develop and present a deep learning solution to the hackathon challenge.
Read 'Deep Learning with Python'
Show steps
This book provides a comprehensive overview of deep learning concepts and techniques, which will enhance your understanding of the course material.
View
Deep Learning with Python, Second Edition
on Amazon
Show steps
Show steps
Show steps
- Purchase or borrow the book.
- Read Chapters 1-5.
- Complete the practice exercises and quizzes in the book.
Build an Anomaly Detection App
Show steps
Build a hands-on project to apply the concepts of anomaly detection learned in the course, reinforcing your understanding.
Browse courses on
Anomaly Detection
Show steps
Show steps
Show steps
- Identify a suitable dataset for anomaly detection.
- Design and train an anomaly detection model using MATLAB.
- Develop a user interface for the app.
- Test and evaluate the app's performance.
Follow a tutorial on model-assisted labeling
Show steps
Engaging in a tutorial will supplement the course material by providing a step-by-step guide to using model-assisted labeling techniques, which will enhance your practical skills.
Show steps
Show steps
Show steps
- Find a tutorial that covers model-assisted labeling in MATLAB.
- Follow the tutorial and apply the techniques to a small dataset.
Six other activities
Expand to see all activities and additional details
Show all nine activities
Attend a deep learning meetup
Show steps
Networking with professionals in the field will expose you to industry trends, potential job opportunities, and diverse perspectives, enriching your learning experience.
Browse courses on
Deep Learning
Show steps
Show steps
Show steps
- Find a local or virtual deep learning meetup group.
- Attend a meetup and engage in discussions with other members.
Diagnosis and Debugging Challenges
Show steps
Engage in practice drills that simulate real-world challenges to hone your skills in diagnosing and debugging anomaly detection models.
Browse courses on
Troubleshooting
Show steps
Show steps
Show steps
- Analyze error messages and exception reports.
- Identify and fix common pitfalls in model training.
- Optimize model parameters to improve accuracy.
Complete practice exercises on importing models from PyTorch
Show steps
Completing practice exercises will reinforce your understanding of how to import and use models from PyTorch, which will expand your toolkit for deep learning.
Browse courses on
PyTorch
Show steps
Show steps
Show steps
- Find online resources or textbooks with practice exercises on importing PyTorch models.
- Complete the exercises to gain hands-on experience.
Build a simple anomaly detection model
Show steps
By building a hands-on project that applies the anomaly detection techniques learned in the course, you will solidify your understanding and gain practical experience.
Browse courses on
Anomaly Detection
Show steps
Show steps
Show steps
- Gather a dataset of images containing anomalies.
- Preprocess the dataset and split it into training and validation sets.
- Train an anomaly detection model using MATLAB.
- Evaluate the model's performance on the validation set.
- Deploy the model for real-world use.
Create a blog post about data augmentation
Show steps
Sharing your understanding of data augmentation through a blog post will reinforce your knowledge and potentially assist others in grasping the concept.
Browse courses on
Data Augmentation
Show steps
Show steps
Show steps
- Choose a specific data augmentation technique to focus on.
- Write a blog post explaining the technique, its benefits, and its limitations.
- Include code examples and visual demonstrations to illustrate the technique.
Participate in a hackathon focused on deep learning
Show steps
Participating in a hackathon will challenge you to apply your deep learning skills in a collaborative and competitive environment, fostering innovation and problem-solving abilities.
Browse courses on
Deep Learning
Show steps
Show steps
Show steps
- Find a hackathon that aligns with your interests and skill level.
- Form a team or join an existing one.
- Develop and present a deep learning solution to the hackathon challenge.
Career center
Learners who complete Advanced Deep Learning Techniques for Computer Vision will develop knowledge and skills
that may be useful to these careers:
Computer Vision Engineer
Computer Vision Engineer
A Computer Vision Engineer develops and implements computer vision algorithms and systems. These systems are used in a variety of applications, such as self-driving cars, medical imaging, and security. This course would be highly relevant to this role, as it provides specialized training in advanced deep learning techniques for computer vision. Specifically, the course covers topics such as anomaly detection, data augmentation, and model-assisted labeling, which are essential for developing computer vision systems.
Artificial Intelligence Engineer
Artificial Intelligence Engineer
An Artificial Intelligence Engineer designs, develops, and implements AI solutions for a variety of applications. AI Engineers may work on projects such as developing self-driving cars, improving medical diagnosis, or creating new ways to interact with computers. This course would be a valuable resource for an aspiring AI Engineer as it provides a solid foundation in advanced deep learning techniques for computer vision. These techniques are essential for developing AI-powered systems that can see and understand the world around them.
Machine Learning Engineer
Machine Learning Engineer
A Machine Learning Engineer builds, deploys, and maintains machine learning models. This includes developing and implementing algorithms, collecting and cleaning data, and training and evaluating models. This course could be a valuable resource for an aspiring Machine Learning Engineer as it provides instruction on advanced deep learning techniques for computer vision, including anomaly detection and data augmentation. These techniques are important for developing effective machine learning models.
Data Scientist
Data Scientist
A Data Scientist uses scientific methods, processes, algorithms and systems to extract knowledge and insights from data in various forms, both structured and unstructured. This course could support an aspiring Data Scientist by providing a foundation in advanced deep learning techniques for computer vision. Specifically, it can provide an understanding of model-assisted labeling, an important technique for preparing large datasets for object detection.
Biomedical Engineer
Biomedical Engineer
A Biomedical Engineer designs and develops medical devices and systems. Biomedical Engineers may work on projects such as developing new medical imaging techniques, creating new ways to diagnose and treat diseases, or developing new prosthetic limbs. This course may be useful for an aspiring Biomedical Engineer as it provides a foundation in advanced deep learning techniques for computer vision. These techniques can be used to develop medical imaging systems that can detect and diagnose diseases earlier, as well as to develop new ways to interact with medical devices.
Robotics Engineer
Robotics Engineer
A Robotics Engineer designs, builds, and maintains robots. Robots are used in a variety of applications, such as manufacturing, healthcare, and space exploration. This course could be useful for an aspiring Robotics Engineer as it provides a foundation in advanced deep learning techniques for computer vision. These techniques can be used to develop robots that can see and understand their environment.
Civil Engineer
Civil Engineer
A Civil Engineer designs and builds infrastructure, such as roads, bridges, and buildings. Civil Engineers may also work on projects such as developing new construction techniques or improving the sustainability of infrastructure. This course may be useful for an aspiring Civil Engineer as it provides a foundation in advanced deep learning techniques for computer vision. These techniques can be used to develop new ways to inspect infrastructure, as well as to develop new ways to design and build infrastructure.
Aerospace Engineer
Aerospace Engineer
An Aerospace Engineer designs, builds, and tests aircraft, spacecraft, and other aerospace vehicles. Aerospace Engineers may also work on projects such as developing new propulsion systems or improving the safety of air travel. This course may be useful for an aspiring Aerospace Engineer as it provides a foundation in advanced deep learning techniques for computer vision. These techniques can be used to develop new ways to inspect aircraft and spacecraft, as well as to develop new ways to navigate and control these vehicles.
Chemical Engineer
Chemical Engineer
A Chemical Engineer designs, builds, and operates chemical plants and processes. Chemical Engineers may also work on projects such as developing new ways to produce chemicals or improve the efficiency of chemical processes. This course may be useful for an aspiring Chemical Engineer as it provides a foundation in advanced deep learning techniques for computer vision. These techniques can be used to develop new ways to inspect chemical plants and processes, as well as to develop new ways to control these processes.
Electrical Engineer
Electrical Engineer
An Electrical Engineer designs, builds, and tests electrical systems and devices. Electrical Engineers may also work on projects such as developing new power generation technologies or improving the efficiency of electrical devices. This course may be useful for an aspiring Electrical Engineer as it provides a foundation in advanced deep learning techniques for computer vision. These techniques can be used to develop new ways to inspect electrical systems and devices, as well as to develop new ways to control these systems.
Industrial Engineer
Industrial Engineer
An Industrial Engineer designs, develops, and implements systems to improve the efficiency of industrial processes. Industrial Engineers may also work on projects such as developing new ways to manage inventory or improve the layout of a factory. This course may be useful for an aspiring Industrial Engineer as it provides a foundation in advanced deep learning techniques for computer vision. These techniques can be used to develop new ways to inspect industrial processes, as well as to develop new ways to control these processes.
Materials Engineer
Materials Engineer
A Materials Engineer designs, develops, and tests new materials. Materials Engineers may also work on projects such as developing new ways to make materials stronger or more durable. This course may be useful for an aspiring Materials Engineer as it provides a foundation in advanced deep learning techniques for computer vision. These techniques can be used to develop new ways to inspect materials, as well as to develop new ways to control the properties of materials.
Mechanical Engineer
Mechanical Engineer
A Mechanical Engineer designs, builds, and tests mechanical systems and devices. Mechanical Engineers may also work on projects such as developing new ways to make engines more efficient or improving the safety of vehicles. This course may be useful for an aspiring Mechanical Engineer as it provides a foundation in advanced deep learning techniques for computer vision. These techniques can be used to develop new ways to inspect mechanical systems and devices, as well as to develop new ways to control these systems.
Mining Engineer
Mining Engineer
A Mining Engineer designs, develops, and operates mines. Mining Engineers may also work on projects such as developing new ways to extract minerals or improve the safety of mining operations. This course may be useful for an aspiring Mining Engineer as it provides a foundation in advanced deep learning techniques for computer vision. These techniques can be used to develop new ways to inspect mines and mining operations, as well as to develop new ways to control these operations.
Nuclear Engineer
Nuclear Engineer
A Nuclear Engineer designs, builds, and operates nuclear power plants. Nuclear Engineers may also work on projects such as developing new ways to generate nuclear power or improving the safety of nuclear power plants. This course may be useful for an aspiring Nuclear Engineer as it provides a foundation in advanced deep learning techniques for computer vision. These techniques can be used to develop new ways to inspect nuclear power plants, as well as to develop new ways to control these plants.
For more career information including salaries, visit:
OpenCourser.com/course/jtgb8a/advanced
Reading list
We've selected 16 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
Advanced Deep Learning Techniques for Computer Vision.
Save
Provides a comprehensive overview of computer vision algorithms and applications. It covers topics such as image formation, feature extraction, object recognition, and image understanding. It valuable resource for students and practitioners who want to learn more about this field.
Provides a practical guide to deep learning using Python. It covers topics such as neural networks, convolutional neural networks, and recurrent neural networks. It valuable resource for students and practitioners who want to learn more about this field.
Provides a comprehensive overview of pattern recognition and machine learning algorithms. It covers topics such as statistical pattern recognition, neural networks, and support vector machines. It valuable resource for students and practitioners who want to learn more about this field.
Provides a comprehensive overview of computer vision algorithms and applications. It covers topics such as image formation, feature extraction, object recognition, and image understanding. It valuable resource for students and practitioners who want to learn more about this field.
Provides a practical guide to creating your own deep learning models, covering the entire process from data collection to model deployment. It valuable resource for anyone interested in building their own deep learning models.
Provides a practical guide to deep learning using fastai and PyTorch. It covers topics such as neural networks, convolutional neural networks, and recurrent neural networks. It valuable resource for students and practitioners who want to learn more about this field.
Save
Provides a comprehensive overview of generative adversarial networks (GANs). GANs are a type of deep learning model that can generate new data from a given distribution. They are a powerful tool for a variety of applications, such as image generation, text generation, and music generation.
Provides a comprehensive overview of deep learning for computer vision, covering the latest algorithms and techniques. It valuable resource for anyone interested in learning more about this field.
Save
Provides a comprehensive overview of deep learning algorithms and applications. It covers topics such as neural networks, convolutional neural networks, and recurrent neural networks. It valuable resource for students and practitioners who want to learn more about this field.
Save
Provides a practical guide to computer vision with OpenCV, covering the latest algorithms and techniques. It valuable resource for anyone interested in learning more about this field.
Save
Provides a comprehensive overview of machine learning algorithms and applications. It covers topics such as supervised learning, unsupervised learning, and reinforcement learning. It valuable resource for students and practitioners who want to learn more about this field.
Provides a comprehensive overview of artificial intelligence from a philosophical perspective. It covers topics such as the nature of intelligence, the history of AI, and the future of AI. It valuable resource for students and practitioners who want to learn more about this field.
Provides a comprehensive overview of statistical learning algorithms and applications. It covers topics such as supervised learning, unsupervised learning, and reinforcement learning. It valuable resource for students and practitioners who want to learn more about this field.
Save
Provides a comprehensive overview of data mining algorithms and applications. It covers topics such as data preprocessing, feature selection, and model evaluation. It valuable resource for students and practitioners who want to learn more about this field.
Provides a comprehensive overview of machine learning algorithms and applications for beginners. It covers topics such as supervised learning, unsupervised learning, and reinforcement learning. It valuable resource for students and practitioners who want to learn more about this field.
Provides a comprehensive overview of machine learning algorithms and applications. It covers topics such as supervised learning, unsupervised learning, and reinforcement learning. It valuable resource for students and practitioners who want to learn more about this field.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/jtgb8a/advanced
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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
- Advanced Deep Learning Techniques for Computer Vision (viewing)
Good to know
Taught by experts in the field, and this course is enhanced with MATLAB software
Demonstrates proven techniques to detect anomalies, generate training images, and implement AI-assisted annotation
Provides advanced techniques to overcome challenges faced in deep learning
Suitable for those with prior knowledge in image processing and deep learning
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