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Facial Expression Classification Using Residual Neural Nets

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In this hands-on project, we will train a deep learning model based on Convolutional Neural Networks (CNNs) and Residual Blocks to detect facial expressions. This project could be practically used for detecting customer emotions and facial expressions. By the end of this project, 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.

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Explore the "Facial Expression Classification Using Residual Neural Nets" course, designed for IT & Computer Science enthusiasts. Led by Coursera, this 120-minute foundational course delves into deep learning, CNNs, and Residual Neural Networks. You'll learn to build, train, and optimize models for emotion detection. Ideal for beginners, it offers hands-on experience with Keras and Tensorflow 2.0, enhancing your data science skills. Available via a Starter subscription.