Facial Expression Classification Using Residual Neural Nets

Overview

Join our hands-on project titled "Facial Expression Classification Using Residual Neural Nets" offered through Coursera, where we delve into the advanced realms of Deep Learning, specifically focusing on Convolutional Neural Networks (CNNs) and Residual Blocks, to master the art of detecting facial expressions. This cutting-edge project is ideal for those looking to apply Deep Learning techniques for practical scenarios such as understanding customer emotions through facial expressions.

Throughout this project, you'll gain comprehensive knowledge on:

• The foundational theories and principles behind Deep Learning, CNNs, and Residual Neural Networks.
• Importing essential libraries, datasets, and visualizing images for analysis.
• Implementing data augmentation strategies to expand your dataset, enhancing the model's ability to generalize.
• Constructing a powerful deep learning model employing Convolutional Neural Network and Residual blocks with the support of Keras and Tensorflow 2.0.
• Compiling and fitting your Deep Learning model to your training dataset to achieve optimal performance.
• Evaluating your trained CNN's performance with a focus on generalization through various Key Performance Indicators (KPIs).
• Enhancing network performance with regularization techniques such as dropout to prevent overfitting.

This project is classified under deep learning courses, neural networks courses, Keras courses, and facial recognition courses, making it perfect for individuals eager to expand their knowledge in these areas.

Taught by

Ryan Ahmed

Subjects