In this 1-hour long project-based course, you will be able to:
- Understand the theory and intuition behind Deep Learning, Convolutional Neural Networks (CNNs) and Residual Neural Networks.
- Import Key libraries, dataset and visualize images.
- Perform data augmentation to increase the size of the dataset and 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.
- Improve network performance using regularization techniques such as dropout.