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Laurence Moroney
and
Eddy Shyu
In this course, you will:
a) Explore image classification, image segmentation, object localization, and object detection. Apply transfer learning to object localization and detection.
b) Apply object detection models such as regional-CNN and ResNet-50, customize existing models, and build your own models to detect, localize, and label your own rubber duck images.
c) Implement image segmentation using variations of the fully convolutional network (FCN) including U-Net and d) Mask-RCNN to identify and detect numbers, pets, zombies, and more.
Read more
In this course, you will:
a) Explore image classification, image segmentation, object localization, and object detection. Apply transfer learning to object localization and detection.
b) Apply object detection models such as regional-CNN and ResNet-50, customize existing models, and build your own models to detect, localize, and label your own rubber duck images.
c) Implement image segmentation using variations of the fully convolutional network (FCN) including U-Net and d) Mask-RCNN to identify and detect numbers, pets, zombies, and more.
Read more
In this course, you will:
a) Explore image classification, image segmentation, object localization, and object detection. Apply transfer learning to object localization and detection.
b) Apply object detection models such as regional-CNN and ResNet-50, customize existing models, and build your own models to detect, localize, and label your own rubber duck images.
c) Implement image segmentation using variations of the fully convolutional network (FCN) including U-Net and d) Mask-RCNN to identify and detect numbers, pets, zombies, and more.
d) Identify which parts of an image are being used by your model to make its predictions using class activation maps and saliency maps and apply these ML interpretation methods to inspect and improve the design of a famous network, AlexNet.
The DeepLearning.AI TensorFlow: Advanced Techniques Specialization introduces the features of TensorFlow that provide learners with more control over their model architecture and tools that help them create and train advanced ML models.
This Specialization is for early and mid-career software and machine learning engineers with a foundational understanding of TensorFlow who are looking to expand their knowledge and skill set by learning advanced TensorFlow features to build powerful models.
Register for this course and see more details by visiting:
OpenCourser.com/course/y1hpb1/advanced
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What's inside
Syllabus
Introduction to Computer Vision
Get a conceptual overview of image classification, object localization, object detection, and image segmentation. Also be able to describe multi-label classification, and distinguish between semantic segmentation and instance segmentation. In the rest of this course, you will apply TensorFlow to build object detection and image segmentation models.
Read more
Syllabus
Introduction to Computer Vision
Get a conceptual overview of image classification, object localization, object detection, and image segmentation. Also be able to describe multi-label classification, and distinguish between semantic segmentation and instance segmentation. In the rest of this course, you will apply TensorFlow to build object detection and image segmentation models.
Read more
Traffic lights
Traffic lights
Read about what's good
what should give you pause and
possible dealbreakers
Focuses on model architecture and tools for advanced ML models
Suitable for learners with foundational TensorFlow understanding who seek advanced knowledge and skills
Taught by industry experts Laurence Moroney and Eddy Shyu, who have established reputations in the field
Emphasizes hands-on practice with building and training object detection and image segmentation models using TensorFlow
Covers topics highly relevant to industry and academic research
Requires learners to bring foundational TensorFlow knowledge and mathematical proficiency
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Reviews summary
Practical advanced computer vision with tensorflow
According to students, this course offers a strong hands-on approach to advanced computer vision using TensorFlow. Learners highly value the practical projects, including the distinctive custom rubber ducky detection, and generally find the explanations clear and spot on for complex topics like U-Net and Mask R-CNN. Many find it excellent for bridging the gap from foundational TensorFlow to advanced CV applications, enhancing their ML engineering skills. However, a significant point of caution is that the prerequisites might be underestimated by some, suggesting a need for more than just basic TensorFlow knowledge. A few also noted that some materials felt slightly outdated or that the theoretical depth was sometimes lacking, requiring external resources. Overall, it's seen as a valuable and rewarding investment.
Overall up-to-date, but some wish for more current practices.
"The content is good, but the course materials felt a bit outdated in some parts, or at least they didn't always reflect the latest best practices in TensorFlow."
"As a professional ML engineer, this course was exactly what I needed to bridge the gap... The emphasis on real-world problems and practical implementation using the latest TF features was perfect."
"I appreciated the deep dive into FCNs and Mask R-CNN. This course immediately improved my ML engineering skills with relevant, modern topics."
The instructor's explanations are generally clear and helpful.
"The instructor's explanations were spot on, and the assignments truly test your understanding."
"The explanations for U-Net and Mask R-CNN were clear and easy to follow."
"The lectures were clear, and the coding exercises reinforced the concepts well for me."
Hands-on coding and projects are a major strength.
"This course provides a strong hands-on approach to advanced CV topics. The labs are challenging but highly rewarding, especially the custom rubber ducky detection project."
"Absolutely brilliant! The practical applications with real datasets, even the fun rubber ducky project, made complex topics digestible."
"The hands-on coding and projects are the strongest part of the course for me, solidifying my understanding of TensorFlow 2.x for object detection and segmentation."
Some feel the course lacks depth or is too fast-paced.
"Expected more depth. While the topics are advanced, the coverage often felt superficial, like a rapid tour rather than a deep dive."
"I had to consult external resources frequently to grasp the 'why' behind certain architectures, beyond just implementation."
"Some sections felt a bit rushed, particularly when diving into the code for certain models. I sometimes wished for more theoretical depth."
Course demands more than just foundational TF knowledge.
"I struggled a lot with this course. While it's 'advanced', it didn't feel like the prerequisites were clearly defined. I found myself lost in complex model architectures."
"Some sections felt a bit rushed, particularly when diving into the code for certain models. I also felt that the prerequisites weren't stressed enough."
"Make sure you have solid TF basics. I believe I needed more foundational math or specific deep learning theory background to truly excel."
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 Computer Vision with TensorFlow with these
activities:
Review coding fundamentals
Show steps
Prepare a solid technical foundation for the course by brushing up on basic coding concepts.
Browse courses on
TensorFlow
Show steps
-
Review core data structures and algorithms
-
Practice coding in your preferred programming language
-
Explore online coding challenges
Seek guidance from experienced practitioners
Show steps
Connect with experts in the field to gain valuable insights.
Show steps
-
Identify potential mentors through professional networks or online platforms
-
Reach out and request guidance
-
Attend industry events or workshops to connect with practitioners
Review concepts from linear algebra
Show steps
Strengthen your mathematical foundation to support your understanding of TensorFlow.
Browse courses on
Linear Algebra
Show steps
-
Review textbooks or online resources on linear algebra
-
Solve practice problems to reinforce concepts
-
Identify how linear algebra applies to TensorFlow
Five other activities
Expand to see all activities and additional details
Show all eight activities
Follow tutorials on image classification
Show steps
Gain a practical understanding of image classification, a fundamental concept in computer vision.
Browse courses on
Image Classification
Show steps
-
Identify reliable online tutorials
-
Follow the tutorials step-by-step
-
Experiment with the code snippets provided
Deep Learning with Python
Show steps
Enhance your understanding of the theoretical foundations of deep learning.
View
Deep Learning with Python, Second Edition
on Amazon
Show steps
-
Read the book thoroughly
-
Work through the exercises and examples provided
-
Relate the concepts to your coursework
Practice exercises on object localization
Show steps
Sharpen your skills in object localization by working through targeted exercises.
Browse courses on
Object Localization
Show steps
-
Find online resources or textbooks with exercises
-
Solve the exercises independently
-
Compare your solutions to provided answers or discuss with peers
Participate in study sessions with peers
Show steps
Collaborate with fellow students to reinforce concepts and challenge your understanding.
Show steps
-
Form or join a study group
-
Meet regularly to discuss course material
-
Work together on assignments or projects
Build a simple image segmentation model
Show steps
Apply your knowledge of image segmentation by creating a functional model.
Browse courses on
Image Segmentation
Show steps
-
Choose a dataset and define the problem statement
-
Select and implement an appropriate model architecture
-
Train and evaluate the model
-
Deploy the model and test its performance
Review coding fundamentals
Show steps
Prepare a solid technical foundation for the course by brushing up on basic coding concepts.
Browse courses on
TensorFlow
Show steps
Show steps
Show steps
- Review core data structures and algorithms
- Practice coding in your preferred programming language
- Explore online coding challenges
Seek guidance from experienced practitioners
Show steps
Connect with experts in the field to gain valuable insights.
Show steps
Show steps
Show steps
- Identify potential mentors through professional networks or online platforms
- Reach out and request guidance
- Attend industry events or workshops to connect with practitioners
Review concepts from linear algebra
Show steps
Strengthen your mathematical foundation to support your understanding of TensorFlow.
Browse courses on
Linear Algebra
Show steps
Show steps
Show steps
- Review textbooks or online resources on linear algebra
- Solve practice problems to reinforce concepts
- Identify how linear algebra applies to TensorFlow
Five other activities
Expand to see all activities and additional details
Show all eight activities
Follow tutorials on image classification
Show steps
Gain a practical understanding of image classification, a fundamental concept in computer vision.
Browse courses on
Image Classification
Show steps
Show steps
Show steps
- Identify reliable online tutorials
- Follow the tutorials step-by-step
- Experiment with the code snippets provided
Deep Learning with Python
Show steps
Enhance your understanding of the theoretical foundations of deep learning.
View
Deep Learning with Python, Second Edition
on Amazon
Show steps
Show steps
Show steps
- Read the book thoroughly
- Work through the exercises and examples provided
- Relate the concepts to your coursework
Practice exercises on object localization
Show steps
Sharpen your skills in object localization by working through targeted exercises.
Browse courses on
Object Localization
Show steps
Show steps
Show steps
- Find online resources or textbooks with exercises
- Solve the exercises independently
- Compare your solutions to provided answers or discuss with peers
Participate in study sessions with peers
Show steps
Collaborate with fellow students to reinforce concepts and challenge your understanding.
Show steps
Show steps
Show steps
- Form or join a study group
- Meet regularly to discuss course material
- Work together on assignments or projects
Build a simple image segmentation model
Show steps
Apply your knowledge of image segmentation by creating a functional model.
Browse courses on
Image Segmentation
Show steps
Show steps
Show steps
- Choose a dataset and define the problem statement
- Select and implement an appropriate model architecture
- Train and evaluate the model
- Deploy the model and test its performance
Career center
Learners who complete Advanced Computer Vision with TensorFlow will develop knowledge and skills
that may be useful to these careers:
Computer Vision Scientist
Computer Vision Scientist
Computer Vision Scientists research new computer vision algorithms and techniques. They may work on a variety of topics, from image classification to object detection to image segmentation. This course "Advanced Computer Vision with TensorFlow" may be useful to someone working in this role because it provides an overview of image classification, object localization, object detection, and image segmentation, which are all important tasks in computer vision.
Product Manager
Product Manager
Product Managers plan and develop software products. They may work on a variety of topics, from market research to product design to product launch. This course "Advanced Computer Vision with TensorFlow" may be useful to someone working in this role because it provides an overview of image classification, object localization, object detection, and image segmentation, which are all important tasks in computer vision.
Medical Image Analyst
Medical Image Analyst
Medical Image Analysts use computer vision techniques to analyze medical images. They may work on a variety of applications, from disease diagnosis to treatment planning. This course "Advanced Computer Vision with TensorFlow" may be useful to someone working in this role because it provides an overview of image classification, object localization, object detection, and image segmentation, which are all important tasks in computer vision.
Remote Sensing Scientist
Remote Sensing Scientist
Remote Sensing Scientists use computer vision techniques to analyze images taken from satellites or airplanes. They may work on a variety of applications, from land use mapping to environmental monitoring. This course "Advanced Computer Vision with TensorFlow" may be useful to someone working in this role because it provides an overview of image classification, object localization, object detection, and image segmentation, which are all important tasks in computer vision.
Autonomous Vehicle Engineer
Autonomous Vehicle Engineer
Autonomous Vehicle Engineers design, build, and maintain autonomous vehicles. They may work on a variety of technologies, from computer vision to sensor fusion to control systems. This course "Advanced Computer Vision with TensorFlow" may be useful to someone working in this role because it provides an overview of image classification, object localization, object detection, and image segmentation, which are all important tasks in computer vision.
Computer Graphics Engineer
Computer Graphics Engineer
Computer Graphics Engineers design, develop, and maintain computer graphics systems. They may work on a variety of applications, from video games to movies to virtual reality. This course "Advanced Computer Vision with TensorFlow" may be useful to someone working in this role because it provides an overview of image classification, object localization, object detection, and image segmentation, which are all important tasks in computer vision.
Human-Computer Interaction Researcher
Human-Computer Interaction Researcher
Human-Computer Interaction Researchers study how people interact with computers. They may work on a variety of topics, from user interface design to user experience. This course "Advanced Computer Vision with TensorFlow" may be useful to someone working in this role because it provides an overview of image classification, object localization, object detection, and image segmentation, which are all important tasks in computer vision.
User Experience Designer
User Experience Designer
User Experience Designers design and develop user interfaces for websites, mobile applications, and other software products. They may work on a variety of topics, from information architecture to visual design. This course "Advanced Computer Vision with TensorFlow" may be useful to someone working in this role because it provides an overview of image classification, object localization, object detection, and image segmentation, which are all important tasks in computer vision.
Computational Photographer
Computational Photographer
Computational Photographers use computer vision techniques to enhance and manipulate photographs. They may work on a variety of applications, from photo editing to image forensics. This course "Advanced Computer Vision with TensorFlow" may be useful to someone working in this role because it provides an overview of image classification, object localization, object detection, and image segmentation, which are all important tasks in computer vision.
Data Scientist
Data Scientist
Data Scientists use data to solve business problems. They may work on data analysis, machine learning, or other areas of data science. This course "Advanced Computer Vision with TensorFlow" may be useful to someone working in this role because it provides an overview of image classification, object localization, object detection, and image segmentation, which are all important tasks in computer vision.
Robotics Engineer
Robotics Engineer
Robotics Engineers design, build, and maintain robots. They may work on a variety of robots, from industrial robots to medical robots to autonomous vehicles. This course "Advanced Computer Vision with TensorFlow" may be useful to someone working in this role because it provides an overview of image classification, object localization, object detection, and image segmentation, which are all important tasks in computer vision.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems. They may work on a variety of software applications, from web applications to mobile applications to desktop applications. This course "Advanced Computer Vision with TensorFlow" may be useful to someone working in this role because it provides an overview of image classification, object localization, object detection, and image segmentation, which are all important tasks in computer vision.
Artificial Intelligence Researcher
Artificial Intelligence Researcher
Artificial Intelligence Researchers develop new artificial intelligence algorithms and techniques. They may work on a variety of topics, from machine learning to natural language processing to computer vision. This course "Advanced Computer Vision with TensorFlow" may be useful to someone working in this role because it provides an overview of image classification, object localization, object detection, and image segmentation, which are all important tasks in computer vision.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers research, design, and build artificial intelligence systems. They may work on natural language processing, speech recognition, computer vision, or other areas of machine learning. This course "Advanced Computer Vision with TensorFlow" may be useful to someone working in this role because it provides an overview of image classification, object localization, object detection, and image segmentation, which are all important tasks in computer vision.
Computer Vision Engineer
Computer Vision Engineer
Computer Vision Engineers develop, test, and maintain computer vision systems. These systems use artificial intelligence to interpret and understand the visual world. This course "Advanced Computer Vision with TensorFlow" may be useful to someone working in this role because it provides an overview of image classification, object localization, object detection, and image segmentation, which are all important tasks in computer vision.
For more career information including salaries, visit:
OpenCourser.com/course/y1hpb1/advanced
Reading list
We've selected eight 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 Computer Vision with TensorFlow.
Provides a comprehensive and up-to-date overview of computer vision, covering topics such as image formation, feature extraction, object recognition, and image segmentation. It valuable resource for both beginners and experienced practitioners in the field.
Provides a comprehensive overview of digital image processing, covering topics such as image enhancement, image restoration, image compression, and image analysis. It valuable resource for those who want to learn the fundamentals of digital image processing.
Save
Provides a comprehensive overview of computer vision algorithms and techniques, covering topics such as image formation, feature extraction, object recognition, and image segmentation. It valuable resource for both beginners and experienced practitioners in the field.
Provides a practical guide to deep learning for computer vision tasks, covering topics such as image classification, object detection, and semantic segmentation. It good resource for those who want to learn how to apply deep learning to real-world computer vision problems.
Provides a practical guide to OpenCV 4 computer vision with Python 3, covering topics such as image processing, feature extraction, object recognition, and image segmentation. It good resource for those who want to learn how to use OpenCV 4 for computer vision tasks.
Provides a comprehensive overview of pattern recognition and machine learning, covering topics such as supervised and unsupervised learning, dimensionality reduction, and Bayesian methods. It valuable resource for those who want to learn the foundations of machine learning.
Provides a practical guide to deep learning with Python, covering topics such as building and training neural networks, using pre-trained models, and deploying models to production. It good resource for those who want to learn how to use Python for deep learning tasks.
Provides a practical guide to computer vision with Python, covering topics such as image processing, feature extraction, object recognition, and image segmentation. It good resource for those who want to learn how to use Python for computer vision tasks.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/y1hpb1/advanced
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Rating
4.8
Effort
Approx. 19 hours
Level
Intermediate
Via
Coursera
Institution
DeepLearning.AI
Instructors
Laurence Moroney
Eddy Shyu
Language
English
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This course belongs to a
collection
called
TensorFlow: Advanced Techniques. It's comprised of four courses:
- Custom Models, Layers, and Loss Functions with TensorFlow
- Advanced Computer Vision with TensorFlow (viewing)
- Generative Deep Learning with TensorFlow
- Custom and Distributed Training with TensorFlow
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