Deep Learning with PyTorch : GradCAM
This course provides an introduction to Gradient-weighted Class Activation Mapping (Grad-CAM) and its implementation with PyTorch. Students will learn to create custom datasets, CNN architectures, training and evaluation functions, and a GradCAM function to generate a heatmap of the localization map of a given class. Finally, they will plot the heatmap on the given input image. ▼
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Course Feature
Cost:
Paid
Provider:
Coursera
Certificate:
Paid Certification
Language:
English
Start Date:
22nd May, 2023
Course Overview
❗The content presented here is sourced directly from Coursera platform. For comprehensive course details, including enrollment information, simply click on the 'Go to class' link on our website.
Updated in [March 20th, 2023]
What skills and knowledge will you acquire during this course? By taking this course, learners will acquire skills and knowledge in deep learning with PyTorch and GradCAM, including creating custom datasets, CNN architectures, train functions, evaluator functions, and saving the best model. They will also learn how to write a GradCAM function that returns a heatmap of the localization map of a given class, as well as how to plot the heatmap on the given input image. Additionally, learners will gain an understanding of the fundamentals of deep learning with PyTorch and GradCAM, which can be applied to their own projects. This course will also provide learners with the knowledge and skills to pursue career paths such as machine learning engineer, data scientist, artificial intelligence engineer, and computer vision engineer, as well as education paths such as a Bachelor of Science in Computer Science, Master of Science in Artificial Intelligence, Doctor of Philosophy in Machine Learning, and Master of Science in Data Science. How does this course contribute to professional growth? Deep Learning with PyTorch : GradCAM contributes to professional growth by providing learners with the skills and knowledge to apply deep learning concepts to their own projects. Learners can use the custom dataset class and custom CNN architecture to create their own models, as well as the train and evaluator functions to write their own training loop. Additionally, they can use the GradCAM function to generate heatmaps of localization maps of a given class and plot the heatmap on the given input image. Furthermore, learners can gain an understanding of the fundamentals of deep learning with PyTorch and GradCAM, as well as the ability to interpret and communicate their findings. This course provides learners with the skills and knowledge to pursue a career in machine learning, data science, artificial intelligence, or computer vision engineering. Is this course suitable for preparing further education? Deep Learning with PyTorch : GradCAM is a project-based course that provides learners with the skills and knowledge to apply deep learning concepts to their own projects. It covers the fundamentals of deep learning with PyTorch and GradCAM, giving learners the opportunity to create custom datasets, CNN architectures, train functions, evaluator functions, and save the best model. Additionally, learners will learn how to write a GradCAM function that returns a heatmap of the localization map of a given class. This course is suitable for preparing further education, as it provides learners with the skills and knowledge to pursue a career in machine learning, data science, artificial intelligence, or computer vision engineering.
Pros & Cons
Easy to follow
Builds intuition
Great material
Limited scope
Not comprehensive
Not suitable for beginners
Course Provider
Provider Coursera's Stats at AZClass
Discussion and Reviews
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1. What is GradCAM?
2. What is the purpose of GradCAM?
3. What is the output of GradCAM?
4. What is the last step of the course?
5. What is the name of the course?
Correct Answer: Deep Learning with PyTorch : GradCAM
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