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Deep Learning with PyTorch

GradCAM

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

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Reviews summary

Practical pytorch gradcam implementation

According to students, this is a highly practical, project-based course that offers a step-by-step implementation of Grad-CAM in PyTorch. Learners found the code well-structured and easy to follow, making it an excellent resource for those seeking a quick deep dive into visualizing CNN activations. However, it's frequently noted as not suitable for absolute beginners, strongly requiring prior knowledge of PyTorch and CNNs. Some found the pacing fast, suggesting it's best for intermediate learners looking to expand their deep learning toolkit.
Fast-paced for some, but efficient for experienced learners.
"I found it a bit fast-paced, and some explanations could be more detailed for someone still getting comfortable with deep learning concepts."
"As a beginner, I found it too advanced. The instructor flies through the code, and there's not enough foundational explanation."
"The 2-hour format is great for a quick deep dive if you're already familiar with the basics."
Instructor explains concepts and code clearly.
"The instructor explains complex concepts clearly, and the guided project makes it easy to follow along."
"Excellent quick project to understand Grad-CAM visually. The code works perfectly, and the instructor's delivery is good."
"I found the step-by-step code writing very helpful and easy to understand."
A very practical, project-based approach to Grad-CAM.
"This course is amazing! It provides a very practical and hands-on approach to Grad-CAM implementation."
"Perfect for a quick practical application of Grad-CAM. The code is easy to follow if you already know PyTorch."
"Good practical implementation. The focus on writing custom dataset and model was very useful."
Strongly advises prior knowledge of PyTorch and CNNs.
"A solid project... but you need to have a good grasp of PyTorch and CNNs beforehand. It's definitely not for absolute beginners."
"I struggled with this course. As a beginner, I found it too advanced. It definitely requires prior knowledge."
"I wish I had a stronger grasp of PyTorch fundamentals before taking this; it moves quickly."

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
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
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
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
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
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
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
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

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 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.
See salaries and explore the career path for Computer Vision Engineer
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.
See salaries and explore the career path for Data Scientist
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.
See salaries and explore the career path for Machine Learning 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.
See salaries and explore the career path for Software Engineer
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.
See salaries and explore the career path for Data Analyst
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.
See salaries and explore the career path for Product Manager
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.
See salaries and explore the career path for Business Analyst
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.
See salaries and explore the career path for Market 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.
See salaries and explore the career path for User Experience Researcher
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.
See salaries and explore the career path for Information Architect
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.
See salaries and explore the career path for Interaction 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.
See salaries and explore the career path for Visual Designer
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.
See salaries and explore the career path for Art Director
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.
See salaries and explore the career path for Graphic Designer
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.
See salaries and explore the career path for Photographer

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.
Cover image
Deep Learning with PyTorch
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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.
Deep Learning with PyTorch: Build, train, and tune...
Paperback
$$
Deep Learning with PyTorch: Build, train, and tune...
Kindle Edition
$$
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Deep Learning
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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.
Deep Learning
Paperback
$$$
Deep Learning (Adaptive Computation and Machine...
Hardcover
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Deep Learning (Adaptive Computation and Machine...
Kindle Edition
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Concurrent NumPy in Python
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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.
Concurrent NumPy in Python: Faster NumPy With BLAS,...
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Concurrent NumPy in Python: Faster NumPy With BLAS,...
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Hands-On Machine Learning with Scikit-Learn, Keras,...
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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.
Hands-On Machine Learning with Scikit-Learn, Keras,...
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Hands-On Machine Learning with Scikit-Learn, Keras,...
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Hands-On Machine Learning with Scikit-Learn, Keras,...
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Deep Learning with Python, Second Edition
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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.
Deep Learning with Python, Second Edition
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Deep Learning with Python, Second Edition
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Cross-Lingual Word Embeddings
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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.
Cross-Lingual Word Embeddings (Synthesis Lectures...
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Effort
2 hours
Level
Intermediate
Via
Coursera
Institution
Coursera Project Network
Instructor
Parth Dhameliya
Language
English
Traffic lights
Read about what's good
what should give you pause
and possible dealbreakers
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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