Explainable AI: Scene Classification and GradCam Visualization

位教师：Ryan Ahmed

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指导项目

在专家的指导下，学习、练习并应用工作就绪技能

4.6

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中级等级

推荐体验

2 hours

自行安排学习进度

实践学习

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指导项目

在专家的指导下，学习、练习并应用工作就绪技能

4.6

（59 条评论）

中级等级

推荐体验

2 hours

自行安排学习进度

实践学习

了解更多

您将学到什么

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

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授课语言：英语（English）

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仅桌面可用

了解顶级公司的员工如何掌握热门技能

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Petrobras, TATA, Danone, Capgemini, P&G 和 L'Oreal 的徽标

在 2 小时内学习、练习并应用岗位必备技能

接受行业专家的培训
获得解决实训工作任务的实践经验
使用最新的工具和技术来建立信心

关于此指导项目

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.

分步进行学习

在与您的工作区一起在分屏中播放的视频中，您的授课教师将指导您完成每个步骤：

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