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Parth Dhameliya
Gradient-weighted Class Activation Mapping (Grad-CAM), uses the class-specific gradient information flowing into the final convolutional layer of a CNN to produce a coarse localization map of the important regions in the image. In this 2-hour long project-based course, you will implement GradCAM on simple classification dataset. You will write a custom dataset class for Image-Classification dataset. Thereafter, you will create custom CNN architecture. Moreover, you are going to create train function and evaluator function which will be helpful to write the training loop. After, saving the best model, you will write GradCAM function which return the heatmap of localization map of a given class. Lastly, you plot the heatmap which the given input image.
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Syllabus
Project Overview
Gradient-weighted Class Activation Mapping (Grad-CAM), uses the class-specific gradient information flowing into the final convolutional layer of a CNN to produce a coarse localization map of the important regions in the image. In this 2-hour long project-based course, you will implement GradCAM on simple classification dataset. You will write a custom dataset class for Image-Classification dataset. Thereafter, you will create custom CNN architecture. Moreover, you are going to create train function and evaluator function which will be helpful to write the training loop. After, saving the best model, you will write GradCAM function which return the heatmap of localization map of a given class. Lastly, you plot the heatmap which the given input image.
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Strengthens an existing foundation for intermediate learners
Develops professional skills or deep expertise in a particular topic or set of topics
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This course teaches skills, knowledge, and/or tools that are highly relevant to industry
Taught by instructors who are recognized for their work in this topic
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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 with PyTorch : GradCAM with these
activities:
Review the basics of convolutional neural networks
Show steps
Reviewing the basics of convolutional neural networks will help you better understand how GradCAM works.
Browse courses on
Convolutional Neural Networks
Show steps
-
Read articles or watch videos about convolutional neural networks
-
Review your notes or textbooks from previous courses or tutorials
-
Complete practice problems or quizzes on convolutional neural networks
Follow a tutorial on how to use GradCAM with a pre-trained model
Show steps
Following a tutorial on GradCAM will help you get started with using the algorithm quickly and easily.
Browse courses on
Gradient-weighted Class Activation Mapping
Show steps
-
Find a tutorial on how to use GradCAM with a pre-trained model
-
Follow the steps in the tutorial
-
Experiment with different input images and models
Implement GradCAM from scratch on MNIST dataset
Show steps
Implementing GradCAM from scratch will help you understand the inner workings of the algorithm and solidify your understanding of the concepts discussed in the course.
Browse courses on
Gradient-weighted Class Activation Mapping
Show steps
-
Understand the theoretical concepts behind GradCAM
-
Implement the forward and backward pass of a CNN
-
Calculate the gradients of the loss with respect to the input image
-
Generate the GradCAM heatmap
-
Visualize the heatmap on the input image
Four other activities
Expand to see all activities and additional details
Show all seven activities
Discuss GradCAM with classmates or colleagues
Show steps
Discussing GradCAM with others will help you solidify your understanding of the algorithm and gain different perspectives.
Browse courses on
Gradient-weighted Class Activation Mapping
Show steps
-
Find a group of classmates or colleagues to discuss GradCAM with
-
Discuss the theoretical concepts behind GradCAM
-
Share your experiences using GradCAM
-
Brainstorm ideas for how to use GradCAM for different applications
Write a blog post explaining GradCAM
Show steps
Writing a blog post about GradCAM will help you solidify your understanding of the algorithm and share your knowledge with others.
Browse courses on
Gradient-weighted Class Activation Mapping
Show steps
-
Gather information and resources about GradCAM
-
Write a draft of your blog post
-
Proofread and revise your blog post
-
Publish your blog post on a platform like Medium or LinkedIn
-
Promote your blog post on social media or other channels
Contribute to an open-source project related to GradCAM
Show steps
Contributing to an open-source project related to GradCAM will help you gain hands-on experience and contribute to the community.
Browse courses on
Gradient-weighted Class Activation Mapping
Show steps
-
Find an open-source project related to GradCAM
-
Identify an issue or feature that you can contribute to
-
Fork the project and make changes to the code
-
Submit a pull request with your changes
-
Collaborate with other developers on the project
Participate in a Kaggle competition using GradCAM
Show steps
Participating in a Kaggle competition using GradCAM will help you apply your skills to a real-world problem and benchmark your performance against others.
Browse courses on
Gradient-weighted Class Activation Mapping
Show steps
-
Find a Kaggle competition that uses GradCAM
-
Download the data and familiarize yourself with the problem
-
Train a model using GradCAM
-
Submit your results to the competition
-
Analyze your results and improve your model
Review the basics of convolutional neural networks
Show steps
Reviewing the basics of convolutional neural networks will help you better understand how GradCAM works.
Browse courses on
Convolutional Neural Networks
Show steps
Show steps
Show steps
- Read articles or watch videos about convolutional neural networks
- Review your notes or textbooks from previous courses or tutorials
- Complete practice problems or quizzes on convolutional neural networks
Follow a tutorial on how to use GradCAM with a pre-trained model
Show steps
Following a tutorial on GradCAM will help you get started with using the algorithm quickly and easily.
Browse courses on
Gradient-weighted Class Activation Mapping
Show steps
Show steps
Show steps
- Find a tutorial on how to use GradCAM with a pre-trained model
- Follow the steps in the tutorial
- Experiment with different input images and models
Implement GradCAM from scratch on MNIST dataset
Show steps
Implementing GradCAM from scratch will help you understand the inner workings of the algorithm and solidify your understanding of the concepts discussed in the course.
Browse courses on
Gradient-weighted Class Activation Mapping
Show steps
Show steps
Show steps
- Understand the theoretical concepts behind GradCAM
- Implement the forward and backward pass of a CNN
- Calculate the gradients of the loss with respect to the input image
- Generate the GradCAM heatmap
- Visualize the heatmap on the input image
Four other activities
Expand to see all activities and additional details
Show all seven activities
Discuss GradCAM with classmates or colleagues
Show steps
Discussing GradCAM with others will help you solidify your understanding of the algorithm and gain different perspectives.
Browse courses on
Gradient-weighted Class Activation Mapping
Show steps
Show steps
Show steps
- Find a group of classmates or colleagues to discuss GradCAM with
- Discuss the theoretical concepts behind GradCAM
- Share your experiences using GradCAM
- Brainstorm ideas for how to use GradCAM for different applications
Write a blog post explaining GradCAM
Show steps
Writing a blog post about GradCAM will help you solidify your understanding of the algorithm and share your knowledge with others.
Browse courses on
Gradient-weighted Class Activation Mapping
Show steps
Show steps
Show steps
- Gather information and resources about GradCAM
- Write a draft of your blog post
- Proofread and revise your blog post
- Publish your blog post on a platform like Medium or LinkedIn
- Promote your blog post on social media or other channels
Contribute to an open-source project related to GradCAM
Show steps
Contributing to an open-source project related to GradCAM will help you gain hands-on experience and contribute to the community.
Browse courses on
Gradient-weighted Class Activation Mapping
Show steps
Show steps
Show steps
- Find an open-source project related to GradCAM
- Identify an issue or feature that you can contribute to
- Fork the project and make changes to the code
- Submit a pull request with your changes
- Collaborate with other developers on the project
Participate in a Kaggle competition using GradCAM
Show steps
Participating in a Kaggle competition using GradCAM will help you apply your skills to a real-world problem and benchmark your performance against others.
Browse courses on
Gradient-weighted Class Activation Mapping
Show steps
Show steps
Show steps
- Find a Kaggle competition that uses GradCAM
- Download the data and familiarize yourself with the problem
- Train a model using GradCAM
- Submit your results to the competition
- Analyze your results and improve your model
Career center
Learners who complete Deep Learning with PyTorch : GradCAM will develop knowledge and skills
that may be useful to these careers:
Computer Vision Engineer
Computer Vision Engineer
Computer Vision Engineers work in the field of computer science that develops techniques to give computers the ability to see. They create algorithms that train computers to interpret images and videos, just like humans do. With this course, you will learn how to implement a technique called GradCAM on simple classification datasets. This will give you a solid foundation in computer vision and prepare you for a successful career as a Computer Vision Engineer.
Data Scientist
Data Scientist
Data Scientists analyze data to extract meaningful insights and solve complex problems. They use techniques such as machine learning and artificial intelligence to build models that can predict future outcomes. This course will teach you how to implement GradCAM, a technique used in computer vision to localize important regions in images. This skill will be highly valuable in your career as a Data Scientist, as it will enable you to develop more accurate and effective models.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design, develop, and deploy machine learning models. They use their knowledge of algorithms, data structures, and programming languages to build systems that can learn from data and make predictions. This course will teach you how to implement GradCAM, a technique used in computer vision to visualize the important regions in images. This skill will be helpful in your career as a Machine Learning Engineer, as it will allow you to develop models that are more interpretable and reliable.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems. They use their knowledge of programming languages, data structures, and algorithms to create software that meets the needs of users. This course will teach you how to implement GradCAM, a technique used in computer vision to localize important regions in images. This skill will be valuable in your career as a Software Engineer, as it will enable you to develop software that is more efficient and user-friendly.
Data Analyst
Data Analyst
Data Analysts collect, clean, and analyze data to extract meaningful insights. They use their knowledge of statistics, programming, and data visualization to identify trends and patterns in data. This course will teach you how to implement GradCAM, a technique used in computer vision to visualize the important regions in images. This skill will be helpful in your career as a Data Analyst, as it will allow you to develop data visualizations that are more informative and persuasive.
Product Manager
Product Manager
Product Managers are responsible for the development and launch of new products. They work with engineers, designers, and marketers to create products that meet the needs of users. This course will teach you how to implement GradCAM, a technique used in computer vision to localize important regions in images. This skill will be valuable in your career as a Product Manager, as it will enable you to develop products that are more user-centric and visually appealing.
Business Analyst
Business Analyst
Business Analysts use data and analysis to solve business problems. They work with stakeholders to identify needs, develop solutions, and measure results. This course will teach you how to implement GradCAM, a technique used in computer vision to visualize the important regions in images. This skill will be helpful in your career as a Business Analyst, as it will enable you to develop solutions that are more data-driven and effective.
Market Researcher
Market Researcher
Market Researchers collect and analyze data to understand consumer behavior. They use their findings to develop marketing campaigns and strategies. This course will teach you how to implement GradCAM, a technique used in computer vision to visualize the important regions in images. This skill will be valuable in your career as a Market Researcher, as it will enable you to develop marketing campaigns that are more targeted and effective.
User Experience Researcher
User Experience Researcher
User Experience Researchers study how users interact with products and services. They use this information to design products that are more user-friendly and efficient. This course will teach you how to implement GradCAM, a technique used in computer vision to visualize the important regions in images. This skill will be helpful in your career as a User Experience Researcher, as it will enable you to design products that are more visually appealing and intuitive to use.
Information Architect
Information Architect
Information Architects design and organize the structure of websites and other digital products. They use their knowledge of user behavior and information science to create products that are easy to use and navigate. This course will teach you how to implement GradCAM, a technique used in computer vision to visualize the important regions in images. This skill will be helpful in your career as an Information Architect, as it will enable you to design products that are more visually appealing and well-organized.
Interaction Designer
Interaction Designer
Interaction Designers design the interactions between users and products. They use their knowledge of human-computer interaction to create products that are easy to use and enjoyable. This course will teach you how to implement GradCAM, a technique used in computer vision to visualize the important regions in images. This skill will be helpful in your career as an Interaction Designer, as it will enable you to design products that are more visually appealing and engaging.
Visual Designer
Visual Designer
Visual Designers use their knowledge of art and design to create visually appealing products. They work with other designers and engineers to create products that are both functional and beautiful. This course will teach you how to implement GradCAM, a technique used in computer vision to visualize the important regions in images. This skill will be helpful in your career as a Visual Designer, as it will enable you to create products that are more visually appealing and effective.
Art Director
Art Director
Art Directors oversee the visual aspects of products and services. They work with designers, photographers, and other artists to create visually appealing content. This course will teach you how to implement GradCAM, a technique used in computer vision to visualize the important regions in images. This skill will be helpful in your career as an Art Director, as it will enable you to create visually appealing content that is more effective at communicating your message.
Graphic designer
Graphic designer
Graphic Designers create visual concepts and content for a variety of media. They use their knowledge of art, design, and typography to create visually appealing designs that communicate a message. This course will teach you how to implement GradCAM, a technique used in computer vision to visualize the important regions in images. This skill will be helpful in your career as a Graphic Designer, as it will enable you to create designs that are more effective at communicating your message.
Photographer
Photographer
Photographers use their knowledge of photography and lighting to capture images that convey a message or tell a story. This course will teach you how to implement GradCAM, a technique used in computer vision to visualize the important regions in images. This skill will be helpful in your career as a Photographer, as it will enable you to take more visually appealing and effective photographs.
For more career information including salaries, visit:
OpenCourser.com/course/je527e/deep
Reading list
We've selected six 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 with PyTorch : GradCAM.
Provides a comprehensive overview of deep learning with PyTorch, covering the essential concepts, techniques, and applications. It valuable resource for both beginners and experienced deep learning practitioners.
Save
Provides a comprehensive overview of convolutional neural networks, covering the basic concepts, architectures, and applications. It valuable resource for anyone interested in learning more about CNNs.
Provides a practical guide to deep learning for computer vision, covering the essential concepts, techniques, and applications. It valuable resource for anyone interested in using deep learning for computer vision tasks.
Provides a hands-on guide to image classification with Keras, covering the essential concepts, techniques, and applications. It valuable resource for anyone interested in using Keras for image classification tasks.
Provides a comprehensive overview of deep learning with Python, covering the essential concepts, techniques, and applications. It valuable resource for anyone interested in learning more about deep learning with Python.
Provides a comprehensive overview of deep learning for natural language processing, covering the essential concepts, techniques, and applications. It valuable resource for anyone interested in learning more about deep learning for natural language processing.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/je527e/deep
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Effort
2 hours
Level
Intermediate
Via
Coursera
Institution
Coursera Project Network
Instructor
Parth Dhameliya
Language
English
Good to know
Strengthens an existing foundation for intermediate learners
Develops professional skills or deep expertise in a particular topic or set of topics
If this course teaches skills, knowledge, and/or tools that are useful for personal growth and development
This course teaches skills, knowledge, and/or tools that are highly relevant to industry
Taught by instructors who are recognized for their work in this topic
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