Emotion AI: Facial Key-points Detection

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.

• Facial Key-point Detection
• In this hands-on project, we will train deep learning model based on Convolutional Neural Networks (CNNs) and Residual Blocks to detect 15 facial key-points. This project could be practically used for detecting customer emotions and facial expressions.