Deep Learning with PyTorch : GradCAM faq

instructor Instructor: Parth Dhameliya instructor-icon
duration Duration: duration-icon

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.

ADVERTISEMENT

Course Feature Course Overview Pros & Cons Course Provider Discussion and Reviews
Go to class

Course Feature

costCost:

Paid

providerProvider:

Coursera

certificateCertificate:

Paid Certification

languageLanguage:

English

start dateStart 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

Pros Cons
  • pros

    Easy to follow

  • pros

    Builds intuition

  • pros

    Great material

  • cons

    Limited scope

  • cons

    Not comprehensive

  • cons

    Not suitable for beginners

Course Provider

Provider Coursera's Stats at AZClass

Rating Grade: B This is a trending provider perfect for gaining traction and maybe a good option for users who are looking for a reliable source of learning content.

Discussion and Reviews

0.0   (Based on 0 reviews)

Start your review of Deep Learning with PyTorch : GradCAM

Quiz

submit successSubmitted Sucessfully

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

close
part

faq FAQ for Pytorch Courses

Q1: Does the course offer certificates upon completion?

Yes, this course offers a paid certificate. AZ Class have already checked the course certification options for you. Access the class for more details.

Q2: How do I contact your customer support team for more information?

If you have questions about the course content or need help, you can contact us through "Contact Us" at the bottom of the page.

Q3: How many people have enrolled in this course?

So far, a total of 0 people have participated in this course. The duration of this course is hour(s). Please arrange it according to your own time.

Q4: How Do I Enroll in This Course?

Click the"Go to class" button, then you will arrive at the course detail page.
Watch the video preview to understand the course content.
(Please note that the following steps should be performed on Coursera's official site.)
Find the course description and syllabus for detailed information.
Explore teacher profiles and student reviews.
Add your desired course to your cart.
If you don't have an account yet, sign up while in the cart, and you can start the course immediately.
Once in the cart, select the course you want and click "Enroll."
Coursera may offer a Personal Plan subscription option as well. If the course is part of a subscription, you'll find the option to enroll in the subscription on the course landing page.
If you're looking for additional Pytorch courses and certifications, our extensive collection at azclass.net will help you.

close

To provide you with the best possible user experience, we use cookies. By clicking 'accept', you consent to the use of cookies in accordance with our Privacy Policy.