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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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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)
Deepens understanding of Convolutional Neural Networks which form the backbone of the deep learning model that will be built during the course.
Show steps
  • 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
Provides hands-on experience in building and training CNN models, solidifying the understanding gained from the course lectures.
Show steps
  • 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
Provides an opportunity to apply the skills learned in the course in a competitive environment, encouraging problem-solving and innovation.
Show steps
  • 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
Expand to see all activities and additional details
Show all six activities
Build a scene classification web application
Provides a practical application of the concepts learned in the course, promoting hands-on experience and project-based learning.
Show steps
  • 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
Enhances the understanding of scene classification and strengthens communication skills by explaining the concepts to others.
Show steps
  • 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
Encourages critical thinking, research skills, and in-depth understanding of the course material.
Browse courses on Grad-CAM
Show steps
  • 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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.

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 .
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.
Provides a practical guide to interpretable machine learning, including the different techniques and tools that can be used to make AI models more understandable. It valuable resource for anyone who wants to learn more about interpretable machine learning, regardless of their level of expertise.
Provides a comprehensive overview of computer vision algorithms and applications. It valuable resource for anyone who wants to learn more about computer vision, regardless of their level of expertise.
Provides a comprehensive overview of machine learning from a probabilistic perspective. It valuable resource for anyone who wants to learn more about machine learning, regardless of their level of expertise.
Provides a comprehensive overview of pattern recognition and machine learning. It valuable resource for anyone who wants to learn more about pattern recognition and machine learning, regardless of their level of expertise.
Provides a practical guide to machine learning with Python. It valuable resource for anyone who wants to learn more about machine learning with Python.
Provides practical guidance on how to implement machine learning algorithms using popular Python libraries. It valuable resource for anyone who wants to learn more about machine learning, regardless of their level of expertise.
Provides a practical guide to deep learning with Python. It valuable resource for anyone who wants to learn more about deep learning with Python.
Provides a practical guide to TensorFlow for deep learning. It valuable resource for anyone who wants to learn more about TensorFlow for deep learning.
Provides a comprehensive overview of digital image processing algorithms and applications. It valuable resource for anyone who wants to learn more about digital image processing.

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