Explainable AI: Scene Classification and GradCam Visualization

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Explainable AI: Scene Classification and GradCam Visualization Understand the theory and intuition behind GradCam and Explainable AIVisualize the Activation Maps used by CNN to make predictions using Grad-CAM …

In this Guided Project, you will:

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

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.

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:

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

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

Perform data augmentation to improve model generalization capability

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

Compile and fit Deep Learning model to training data

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

Understand the theory and intuition behind GradCam and Explainable AI

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

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This project could be practically Coursera

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