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