Explainable AI: Scene Classification and GradCam Visualization

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En este proyecto guiado, tú:

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

Clock2 hours
IntermediateIntermedio
CloudNo se necesita descarga
VideoVideo de pantalla dividida
Comment DotsInglés (English)
LaptopSolo escritorio

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.

Habilidades que desarrollarás

Deep LearningMachine LearningPython ProgrammingArtificial Intelligence(AI)Computer Vision

Aprende paso a paso

En un video que se reproduce en una pantalla dividida con tu área de trabajo, tu instructor te guiará en cada paso:

  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

Cómo funcionan los proyectos guiados

Tu espacio de trabajo es un escritorio virtual directamente en tu navegador, no requiere descarga.

En un video de pantalla dividida, tu instructor te guía paso a paso

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