Explainable AI: Scene Classification and GradCam Visualization

작업 영역이 있는 분할 화면으로 재생되는 동영상에서 강사는 다음을 단계별로 안내합니다.

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