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Parth Dhameliya
In this 2-hour project-based course, you will be able to :
- Understand the Facial Keypoint Dataset and you will write a custom dataset class for Image-Keypoint dataset. Additionally, you will apply keypoint augmentation to augment images as well as its keypoints. For keypoint augmentation you will use albumentation library. You will plot the image keypoint pair.
- Load a pretrained state of the art convolutional neural network using timm library.
Read more
In this 2-hour project-based course, you will be able to :
- Understand the Facial Keypoint Dataset and you will write a custom dataset class for Image-Keypoint dataset. Additionally, you will apply keypoint augmentation to augment images as well as its keypoints. For keypoint augmentation you will use albumentation library. You will plot the image keypoint pair.
- Load a pretrained state of the art convolutional neural network using timm library.
Read more
In this 2-hour project-based course, you will be able to :
- Understand the Facial Keypoint Dataset and you will write a custom dataset class for Image-Keypoint dataset. Additionally, you will apply keypoint augmentation to augment images as well as its keypoints. For keypoint augmentation you will use albumentation library. You will plot the image keypoint pair.
- Load a pretrained state of the art convolutional neural network using timm library.
- Create train function and evaluator function which will helpful to write training loop. Moreover, you will use training loop to train the model.
- Lastly, you will use trained model to find keypoints given any image.
Register for this course and see more details by visiting:
OpenCourser.com/course/6bkztl/facial
What's inside
Syllabus
Project Overview
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Good to know
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what to watch for , and
possible dealbreakers
Teaches advanced techniques for computer vision, including image augmentation and training a CNN for keypoint detection
Develops expertise in applying deep learning and computer vision for practical applications
Provides hands-on experience through a project-based approach
Requires familiarity with Python, deep learning, and computer vision concepts
Assumes prior knowledge in computer vision and deep learning
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Reviews summary
Computer vision skills builder
Students say they find facial keypoint detection to be a relevant field, and students appreciated the practical applications of this course. One reviewer even said that this course is useful for those in the field of computer vision. Students have also said the course explains concepts clearly.
The course likely provides useful applications for students.
"The project was extremely useful to apply the knowledge in real world applications in the field of advanced computer vision."
The course is likely relevant to the field of computer vision.
"The project was extremely useful to apply the knowledge in real world applications in the field of advanced computer vision."
The course's concepts are likely explained clearly.
"The instructor did a nice job and explained most of the concepts very clear."
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 Facial Keypoint Detection with PyTorch with these
activities:
Brush up on Linear Algebra basics
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Refreshes the core concepts of linear algebra essential for deep learning
Browse courses on
Linear Algebra
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-
Review vector operations (addition, subtraction, dot product, cross product)
-
Practice solving systems of linear equations
-
Understand matrix operations (addition, subtraction, multiplication)
Organize and review notes, assignments, and course resources
Show steps
Enhances retention and makes study materials easily accessible
Show steps
-
Create a system for organizing notes and assignments
-
Review and summarize key concepts regularly
Read *Deep Learning* by Ian Goodfellow
Show steps
Provides a comprehensive overview of the fundamental concepts and algorithms in deep learning
View
Deep Learning
on Amazon
Show steps
-
Read Chapters 1-3 to understand the basics of deep learning
-
Focus on Chapters 4-6 to learn about different neural network architectures
-
Review Chapters 7-9 to grasp advanced topics such as regularization and optimization
Four other activities
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Participate in a study group with other students
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Encourages collaboration, discussion, and knowledge sharing
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-
Find a group of classmates with similar interests
-
Establish regular meeting times
-
Discuss course material, work on assignments together, and share resources
Implement a simple image classification model
Show steps
Applies the concepts learned to build a practical deep learning model
Show steps
-
Choose a dataset (e.g., MNIST, CIFAR-10)
-
Select a neural network architecture (e.g., CNN)
-
Train and evaluate the model
-
Deploy the model for inference
Develop a presentation on a specific deep learning application
Show steps
Enhances communication and understanding of practical deep learning uses
Show steps
-
Choose an application domain (e.g., computer vision, natural language processing)
-
Research a specific deep learning model used in the domain
-
Create a presentation outlining the model's architecture, training, and results
Develop a research proposal outlining a deep learning project
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Fosters critical thinking, research skills, and project planning abilities
Show steps
-
Identify a research problem in deep learning
-
Propose a novel approach or solution
-
Outline the methodology, experiments, and expected outcomes
-
Secure feedback from an instructor or mentor
Brush up on Linear Algebra basics
Show steps
Refreshes the core concepts of linear algebra essential for deep learning
Browse courses on
Linear Algebra
Show steps
Show steps
Show steps
- Review vector operations (addition, subtraction, dot product, cross product)
- Practice solving systems of linear equations
- Understand matrix operations (addition, subtraction, multiplication)
Organize and review notes, assignments, and course resources
Show steps
Enhances retention and makes study materials easily accessible
Show steps
Show steps
Show steps
- Create a system for organizing notes and assignments
- Review and summarize key concepts regularly
Read *Deep Learning* by Ian Goodfellow
Show steps
Provides a comprehensive overview of the fundamental concepts and algorithms in deep learning
View
Deep Learning
on Amazon
Show steps
Show steps
Show steps
- Read Chapters 1-3 to understand the basics of deep learning
- Focus on Chapters 4-6 to learn about different neural network architectures
- Review Chapters 7-9 to grasp advanced topics such as regularization and optimization
Four other activities
Expand to see all activities and additional details
Show all seven activities
Participate in a study group with other students
Show steps
Encourages collaboration, discussion, and knowledge sharing
Show steps
Show steps
Show steps
- Find a group of classmates with similar interests
- Establish regular meeting times
- Discuss course material, work on assignments together, and share resources
Implement a simple image classification model
Show steps
Applies the concepts learned to build a practical deep learning model
Show steps
Show steps
Show steps
- Choose a dataset (e.g., MNIST, CIFAR-10)
- Select a neural network architecture (e.g., CNN)
- Train and evaluate the model
- Deploy the model for inference
Develop a presentation on a specific deep learning application
Show steps
Enhances communication and understanding of practical deep learning uses
Show steps
Show steps
Show steps
- Choose an application domain (e.g., computer vision, natural language processing)
- Research a specific deep learning model used in the domain
- Create a presentation outlining the model's architecture, training, and results
Develop a research proposal outlining a deep learning project
Show steps
Fosters critical thinking, research skills, and project planning abilities
Show steps
Show steps
Show steps
- Identify a research problem in deep learning
- Propose a novel approach or solution
- Outline the methodology, experiments, and expected outcomes
- Secure feedback from an instructor or mentor
Career center
Learners who complete Facial Keypoint Detection with PyTorch will develop knowledge and skills
that may be useful to these careers:
Software Engineer
Software Engineer
Software Engineers design, develop, and implement software systems. They use a variety of programming languages and technologies to create software that meets the needs of users. This course provides a strong foundation in the fundamentals of software engineering, including software design, software development, and software testing. It also covers the latest advances in artificial intelligence, which are essential for developing state-of-the-art software systems. This course will be particularly useful for Software Engineers who want to develop facial recognition systems.
Data Scientist
Data Scientist
Data Scientists use data to solve business problems. They collect, clean, and analyze data to identify trends and patterns. They then use this information to develop predictive models and make recommendations. This course provides a strong foundation in the fundamentals of data science, including data analysis, machine learning, and deep learning. It also covers the latest advances in artificial intelligence, which are essential for developing state-of-the-art data science solutions. This course will be particularly useful for Data Scientists who want to develop facial recognition systems.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design, develop, and implement machine learning systems. They use machine learning algorithms to solve a wide variety of problems, such as facial recognition, object detection, and natural language processing. This course provides a strong foundation in the fundamentals of machine learning, including supervised learning, unsupervised learning, and deep learning. It also covers the latest advances in artificial intelligence, which are essential for developing state-of-the-art machine learning systems. This course will be particularly useful for Machine Learning Engineers who want to develop facial recognition systems.
Computer Vision Engineer
Computer Vision Engineer
Computer Vision Engineers develop and implement computer vision systems, which are used in a wide variety of applications, such as facial recognition, object detection, and autonomous driving. This course provides a strong foundation in the fundamentals of computer vision, including image processing, feature extraction, and machine learning. It also covers the latest advances in deep learning for computer vision, which are essential for developing state-of-the-art computer vision systems. This course will be particularly useful for Computer Vision Engineers who want to develop facial recognition systems.
Computer Graphics Artist
Computer Graphics Artist
Computer Graphics Artists create digital images and animations. They use a variety of software tools to create realistic and engaging visual content. This course provides a strong foundation in the fundamentals of computer graphics, including 3D modeling, animation, and visual effects. It also covers the latest advances in artificial intelligence, which are essential for developing state-of-the-art computer graphics. This course will be particularly useful for Computer Graphics Artists who want to develop facial recognition systems.
Web Developer
Web Developer
Web Developers design, develop, and implement websites. They use a variety of programming languages and technologies to create websites that meet the needs of users. This course provides a strong foundation in the fundamentals of web development, including HTML, CSS, and JavaScript. It also covers the latest advances in artificial intelligence, which are essential for developing state-of-the-art websites. This course will be particularly useful for Web Developers who want to develop facial recognition systems.
Game Developer
Game Developer
Game Developers design, develop, and implement video games. They use a variety of programming languages and technologies to create video games that meet the needs of users. This course provides a strong foundation in the fundamentals of game development, including game design, game development, and game testing. It also covers the latest advances in artificial intelligence, which are essential for developing state-of-the-art video games. This course will be particularly useful for Game Developers who want to develop facial recognition systems.
Mobile App Developer
Mobile App Developer
Mobile App Developers design, develop, and implement mobile apps. They use a variety of programming languages and technologies to create mobile apps that meet the needs of users. This course provides a strong foundation in the fundamentals of mobile app development, including iOS and Android development. It also covers the latest advances in artificial intelligence, which are essential for developing state-of-the-art mobile apps. This course will be particularly useful for Mobile App Developers who want to develop facial recognition systems.
Data Analyst
Data Analyst
Data Analysts collect, clean, and analyze data to identify trends and patterns. They then use this information to make recommendations and develop data-driven solutions. This course provides a strong foundation in the fundamentals of data analysis, including data visualization, statistical analysis, and data mining. It also covers the latest advances in artificial intelligence, which are essential for developing state-of-the-art data analysis solutions. This course will be particularly useful for Data Analysts who want to develop facial recognition systems.
Project Manager
Project Manager
Project Managers plan, organize, and execute projects. They use a variety of tools and techniques to manage project scope, budget, and timeline. They also work with a variety of stakeholders to ensure that project goals are met. This course provides a strong foundation in the fundamentals of project management, including project planning, project execution, and project closeout. It also covers the latest advances in artificial intelligence, which are essential for developing state-of-the-art project management solutions. This course will be particularly useful for Project Managers who want to develop facial recognition systems.
Operations Manager
Operations Manager
Operations Managers plan, organize, and execute operations. They use a variety of tools and techniques to manage operations costs, quality, and efficiency. They also work with a variety of stakeholders to ensure that operations goals are met. This course provides a strong foundation in the fundamentals of operations management, including operations planning, operations execution, and operations closeout. It also covers the latest advances in artificial intelligence, which are essential for developing state-of-the-art operations management solutions. This course will be particularly useful for Operations Managers who want to develop facial recognition systems.
Product Manager
Product Manager
Product Managers define and manage the development of new products and services. They work with a variety of stakeholders to gather and analyze data, and then develop and implement product roadmaps. This course provides a strong foundation in the fundamentals of product management, including product planning, product development, and product marketing. It also covers the latest advances in artificial intelligence, which are essential for developing state-of-the-art product management solutions. This course will be particularly useful for Product Managers who want to develop facial recognition systems.
Business Analyst
Business Analyst
Business Analysts identify and analyze business problems and develop solutions to improve business performance. They use a variety of tools and techniques to gather and analyze data, and then develop and implement recommendations. This course provides a strong foundation in the fundamentals of business analysis, including business process modeling, data analysis, and financial analysis. It also covers the latest advances in artificial intelligence, which are essential for developing state-of-the-art business analysis solutions. This course will be particularly useful for Business Analysts who want to develop facial recognition systems.
Sales Manager
Sales Manager
Sales Managers plan, organize, and execute sales campaigns. They use a variety of tools and techniques to manage sales targets, sales pipelines, and sales teams. They also work with a variety of stakeholders to ensure that sales goals are met. This course provides a strong foundation in the fundamentals of sales management, including sales planning, sales execution, and sales closeout. It also covers the latest advances in artificial intelligence, which are essential for developing state-of-the-art sales management solutions. This course will be particularly useful for Sales Managers who want to develop facial recognition systems.
Financial Analyst
Financial Analyst
Financial Analysts provide financial advice to individuals and organizations. They use a variety of tools and techniques to analyze financial data and develop financial plans. This course provides a strong foundation in the fundamentals of financial analysis, including financial reporting, financial modeling, and investment analysis. It also covers the latest advances in artificial intelligence, which are essential for developing state-of-the-art financial analysis solutions. This course will be particularly useful for Financial Analysts who want to develop facial recognition systems.
For more career information including salaries, visit:
OpenCourser.com/course/6bkztl/facial
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
Facial Keypoint Detection with PyTorch.
Save
Provides a comprehensive overview of deep learning algorithms and techniques, covering topics such as convolutional neural networks, recurrent neural networks, and generative adversarial networks. It valuable resource for anyone interested in learning more about the field of deep learning.
Provides a practical introduction to deep learning for computer vision tasks, covering topics such as convolutional neural networks, image classification, object detection, and image segmentation. It valuable resource for anyone interested in learning how to apply deep learning to computer vision problems.
Provides a comprehensive overview of machine learning algorithms and techniques from a probabilistic perspective, covering topics such as Bayesian learning, graphical models, and reinforcement learning. It valuable resource for anyone interested in learning more about the field of machine learning.
Provides a comprehensive overview of statistical learning algorithms and techniques, covering topics such as linear regression, logistic regression, and decision trees. It valuable resource for anyone interested in learning more about the field of machine learning.
Save
Provides a comprehensive overview of computer vision algorithms and techniques, covering topics such as image formation, feature detection, object recognition, and image segmentation. It valuable resource for anyone interested in learning more about the field of computer vision.
Provides an accessible introduction to machine learning algorithms and techniques, covering topics such as supervised learning, unsupervised learning, and reinforcement learning. It valuable resource for anyone interested in learning more about the field of machine learning.
Provides a comprehensive overview of pattern recognition and machine learning algorithms and techniques, covering topics such as statistical pattern recognition, Bayesian learning, and support vector machines. It valuable resource for anyone interested in learning more about the field of machine learning.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/6bkztl/facial
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Effort
2 hours
Level
Intermediate
Via
Coursera
Institution
Coursera Project Network
Instructor
Parth Dhameliya
Language
English
Good to know
Teaches advanced techniques for computer vision, including image augmentation and training a CNN for keypoint detection
Develops expertise in applying deep learning and computer vision for practical applications
Provides hands-on experience through a project-based approach
Requires familiarity with Python, deep learning, and computer vision concepts
Assumes prior knowledge in computer vision and deep learning
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