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Explainable AI

Scene Classification and GradCam Visualization

Ryan Ahmed

In this 2 hour long hands-on project, we will train a deep learning model to predict the type of scenery in images. In addition, we are going to use a technique known as Grad-Cam to help explain how AI models think. This project could be practically used for detecting the type of scenery from the satellite images.

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What's inside

Syllabus

Scene Classification and Explainable AI

In this hands-on project, we will train deep learning model based on Convolutional Neural Networks (CNNs) and Residual Blocks to detect the type of scenery in images. This project could be practically used for detecting the type of scenery from the satellite images. In addition, this project will cover the use of a technique known as Grad-Cam to help us explain how AI models think.

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Helps learners understand and build models using techniques like deep learning and Grad-Cam, which are commonly used in industry

Fits learners who have a background or interest in deep learning and computer vision

May be somewhat difficult for those completely new to machine learning or deep learning

Requires learners to have access to a computer with the required GPU and software, which may be costly for some

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Reviews summary

Gradcam convolutional neural network

Learners say this course is step-by-step and insightful, featuring engaging assignments. The course teaches how to build a Resnet Image Classification Convolutional Neural Network, using Grad-Cam to visualize how parts of an image impact classification. Despite a few inaccuracies in the code, students say this course is a good introductory to CNN and XAI.

Course is a step-by-step guide.

"A step by step explanation of how to build a Resnet Image Classification Convolutional Neural Network."

Course is insightful for beginners.

"Very insightful introductory project course to CNN and XAI."

Assignments are engaging.

"Providing such images was really helpful."

Prerequisites are needed.

"A prerequisite for this course could also be the mathematical background"

Code has inaccuracies.

"There were several mistakes in the code."

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 Explainable AI: Scene Classification and GradCam Visualization with these activities:

Read and summarize: Convolutional Neural Networks (CNNs)

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Deepens understanding of Convolutional Neural Networks which form the backbone of the deep learning model that will be built during the course.

View Deep Learning with TensorFlow and Keras - Third... on Amazon

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Read chapters 6 and 7 of the book
Write a summary of the key concepts covered in the chapters

Practice building CNN models in Python

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Provides hands-on experience in building and training CNN models, solidifying the understanding gained from the course lectures.

Browse courses on Convolutional Neural Networks

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Follow the tutorials from the course materials on building CNN models in Python
Experiment with different CNN architectures and parameters
Evaluate the performance of the models on a dataset

Participate in a Kaggle competition on scene classification

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Provides an opportunity to apply the skills learned in the course in a competitive environment, encouraging problem-solving and innovation.

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Join the Kaggle competition
Explore the competition data and familiarize yourself with the task
Build and train a scene classification model
Submit your predictions to the competition

Three other activities

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Build a scene classification web application

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Provides a practical application of the concepts learned in the course, promoting hands-on experience and project-based learning.

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Design the architecture of the web application
Implement the scene classification model
Create a user interface for the application
Deploy the application on a web server

Create a blog post or video tutorial on scene classification using CNNs

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Enhances the understanding of scene classification and strengthens communication skills by explaining the concepts to others.

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Choose a specific aspect of scene classification to focus on
Research and gather information on the topic
Write a blog post or create a video tutorial explaining the concepts
Share the content with others and gather feedback

Write a research paper on the use of Grad-CAM for explaining AI models

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Encourages critical thinking, research skills, and in-depth understanding of the course material.

Browse courses on Grad-CAM

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Review literature on Grad-CAM and explainable AI
Design and conduct experiments to evaluate the effectiveness of Grad-CAM
Analyze the results and draw conclusions
Write a research paper summarizing the findings

Career center

Learners who complete Explainable AI: Scene Classification and GradCam Visualization will develop knowledge and skills that may be useful to these careers:

Data Scientist

Data Scientists analyze data to help organizations make informed decisions. They use statistical techniques and machine learning algorithms to identify trends and patterns in data. This course can help you build a foundation in machine learning and deep learning, which are essential skills for Data Scientists. You will also learn how to use Grad-Cam to explain the predictions of AI models, which can be helpful in communicating your findings to stakeholders.

See salaries and explore the career path for Data Scientist

Machine Learning Engineer

Machine Learning Engineers design and implement machine learning models. They work closely with Data Scientists to develop and deploy models that can solve real-world problems. This course can help you build a strong foundation in machine learning and deep learning, which are essential skills for Machine Learning Engineers. You will also learn how to use Grad-Cam to explain the predictions of AI models, which can be helpful in debugging and improving your models.

See salaries and explore the career path for Machine Learning Engineer

Computer Vision Engineer

Computer Vision Engineers develop and implement computer vision algorithms. They work on a variety of tasks, such as object detection, image segmentation, and facial recognition. This course can help you build a foundation in computer vision and deep learning, which are essential skills for Computer Vision Engineers. You will also learn how to use Grad-Cam to explain the predictions of AI models, which can be helpful in understanding how your models work and in debugging them.

See salaries and explore the career path for Computer Vision Engineer

Data Analyst

Data Analysts collect, clean, and analyze data to help organizations make informed decisions. They use statistical techniques to identify trends and patterns in data. This course can help you build a strong foundation in machine learning and deep learning, which are becoming increasingly important in data analysis. You will also learn how to use Grad-Cam to explain the predictions of AI models, which can be helpful in communicating your findings to stakeholders.

See salaries and explore the career path for Data Analyst

Software Engineer

Software Engineers design, develop, and maintain software systems. They work on a variety of projects, from small mobile apps to large enterprise systems. This course can help you build a foundation in machine learning and deep learning, which are becoming increasingly important in software development. You will also learn how to use Grad-Cam to explain the predictions of AI models, which can be helpful in debugging and improving your software.

See salaries and explore the career path for Software Engineer

Business Analyst

Business Analysts help organizations to improve their business processes. They use data to identify areas for improvement and develop solutions to problems. This course can help you build a foundation in machine learning and deep learning, which can be helpful in identifying trends and patterns in data. You will also learn how to use Grad-Cam to explain the predictions of AI models, which can be helpful in communicating your findings to stakeholders.

See salaries and explore the career path for Business Analyst

Product Manager

Product Managers are responsible for the development and launch of new products. They work closely with engineers, designers, and marketers to ensure that products meet the needs of customers. This course can help you build a foundation in machine learning and deep learning, which are becoming increasingly important in product development. You will also learn how to use Grad-Cam to explain the predictions of AI models, which can be helpful in understanding how users interact with your products.

See salaries and explore the career path for Product Manager

Project Manager

Project Managers plan and execute projects to achieve specific goals. They work with a variety of stakeholders to ensure that projects are completed on time and within budget. This course can help you build a foundation in machine learning and deep learning, which can be helpful in managing projects that involve AI or data science. You will also learn how to use Grad-Cam to explain the predictions of AI models, which can be helpful in communicating your findings to stakeholders.

See salaries and explore the career path for Project Manager

Quantitative Analyst

Quantitative Analysts use mathematical and statistical techniques to analyze financial data. They develop models to predict the performance of stocks, bonds, and other financial instruments. This course can help you build a foundation in machine learning and deep learning, which are becoming increasingly important in quantitative analysis. You will also learn how to use Grad-Cam to explain the predictions of AI models, which can be helpful in understanding how your models work and in debugging them.

See salaries and explore the career path for Quantitative Analyst

Technical Writer

Technical Writers create documentation for technical products and services. They work with engineers and other technical experts to translate complex information into clear and concise language. This course can help you build a foundation in machine learning and deep learning, which can be helpful in understanding the technical concepts that you will be writing about. You will also learn how to use Grad-Cam to explain the predictions of AI models, which can be helpful in creating accurate and informative documentation.

See salaries and explore the career path for Technical Writer

Research Scientist

Research Scientists conduct research to advance knowledge in a variety of fields, including science, engineering, and medicine. They use a variety of techniques to collect and analyze data, and they develop new theories and models to explain their findings. This course can help you build a foundation in machine learning and deep learning, which are becoming increasingly important in research. You will also learn how to use Grad-Cam to explain the predictions of AI models, which can be helpful in communicating your findings to other researchers and to the public.

See salaries and explore the career path for Research Scientist

Academic

Academics teach and conduct research at universities and other educational institutions. They specialize in a particular field of study, and they publish their findings in academic journals. This course can help you build a foundation in machine learning and deep learning, which are becoming increasingly important in academia. You will also learn how to use Grad-Cam to explain the predictions of AI models, which can be helpful in teaching your students about AI and in communicating your research findings to other researchers.

See salaries and explore the career path for Academic

Policy Analyst

Policy Analysts research and analyze public policy issues. They develop recommendations for policies that will address these issues. This course can help you build a foundation in machine learning and deep learning, which are becoming increasingly important in policy analysis. You will also learn how to use Grad-Cam to explain the predictions of AI models, which can be helpful in communicating your findings to policymakers and to the public.

See salaries and explore the career path for Policy Analyst

Consultant

Consultants provide advice and expertise to organizations on a variety of topics, including strategy, operations, and technology. This course can help you build a foundation in machine learning and deep learning, which are becoming increasingly important in consulting. You will also learn how to use Grad-Cam to explain the predictions of AI models, which can be helpful in communicating your findings to clients.

See salaries and explore the career path for Consultant

Entrepreneur

Entrepreneurs start and run their own businesses. They identify opportunities, develop products or services, and build teams to bring their visions to life. This course can help you build a foundation in machine learning and deep learning, which are becoming increasingly important in entrepreneurship. You will also learn how to use Grad-Cam to explain the predictions of AI models, which can be helpful in understanding how your products or services can be improved.

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Reading list

We've selected 13 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 Explainable AI: Scene Classification and GradCam Visualization .

Deep Learning Illustrated

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Provides a comprehensive overview of deep learning, including the theoretical foundations and practical applications. It valuable resource for anyone who wants to learn more about deep learning, regardless of their level of expertise.

Explainable AI

Scene Classification and GradCam Visualization

What's inside

Syllabus

Good to know

Save this course

Reviews summary

Gradcam convolutional neural network

Activities

Career center

Reading list

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