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Explainable AI: Scene Classification and GradCam Visualization
Coursera Project Network

Explainable AI: Scene Classification and GradCam Visualization

Taught in English

Ryan Ahmed

Instructor: Ryan Ahmed

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Guided Project

Learn, practice, and apply job-ready skills with expert guidance

Intermediate level

Recommended experience

2 hours
Learn at your own pace
No downloads or installation required
Only available on desktop
Hands-on learning
4.7

(53 reviews)

What you'll learn

  • Understand the theory and intuition behind Deep Neural Networks, Residual Nets, and Convolutional Neural Networks (CNNs)

  • Build a deep learning model based on Convolutional Neural Network and Residual blocks using Keras with Tensorflow 2.0 as a backend

  • Visualize the Activation Maps used by CNN to make predictions using Grad-CAM and Deploy the trained model using Tensorflow Serving

Details to know

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Guided Project

Learn, practice, and apply job-ready skills with expert guidance

Intermediate level

Recommended experience

2 hours
Learn at your own pace
No downloads or installation required
Only available on desktop
Hands-on learning
4.7

(53 reviews)

See how employees at top companies are mastering in-demand skills

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Learn, practice, and apply job-ready skills in less than 2 hours

  • Receive training from industry experts
  • Gain hands-on experience solving real-world job tasks
  • Build confidence using the latest tools and technologies
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About this Guided Project

Learn step-by-step

In a video that plays in a split-screen with your work area, your instructor will walk you through these steps:

  1. Understand the theory and intuition behind Deep Neural Networks, Residual Nets, and Convolutional Neural Networks (CNNs)

  2. Apply Python libraries to import, pre-process and visualize images

  3. Perform data augmentation to improve model generalization capability

  4. Build a deep learning model based on Convolutional Neural Network and Residual blocks using Keras with Tensorflow 2.0 as a backend

  5. Compile and fit Deep Learning model to training data

  6. Assess the performance of trained CNN and ensure its generalization using various KPIs such as accuracy, precision and recall

  7. Understand the theory and intuition behind GradCam and Explainable AI

  8. Visualize the Activation Maps used by CNN to make predictions using Grad-CAM

Recommended experience

Basic python programming and mathematics.

5 project images

Instructor

Ryan Ahmed
Coursera Project Network
60 Courses110,950 learners

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How you'll learn

  • Skill-based, hands-on learning

    Practice new skills by completing job-related tasks.

  • Expert guidance

    Follow along with pre-recorded videos from experts using a unique side-by-side interface.

  • No downloads or installation required

    Access the tools and resources you need in a pre-configured cloud workspace.

  • Available only on desktop

    This Guided Project is designed for laptops or desktop computers with a reliable Internet connection, not mobile devices.

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4.7

53 reviews

  • 5 stars

    75.47%

  • 4 stars

    18.86%

  • 3 stars

    3.77%

  • 2 stars

    1.88%

  • 1 star

    0%

VG
5

Reviewed on Jul 26, 2020

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