Facial Expression Classification Using Residual Neural Nets

Course Cover

5

(4)

compare button icon

Course Features

icon

Duration

2 hours

icon

Delivery Method

Online

icon

Available on

Limited Access

icon

Accessibility

Desktop, Laptop

icon

Language

English

icon

Subtitles

English

icon

Level

Beginner

icon

Teaching Type

Self Paced

icon

Video Content

2 hours

Course Description

This hands-on project will teach us how to train a deep learning model that uses Convolutional Neural networks (CNNs), and Residual Blocks in order to detect facial expressions. This project could be used to detect customer emotions and facial expressions. This project will allow you to: - Learn the theory and intuition behind Deep Learning and Convolutional Neural Networks. - Import key libraries and datasets, and visualize images. Data augmentation is used to increase the data size and enhance model generalization. Convolutional Neural network and residual blocks are used to build a deep learning model using Keras with Tensorflow2.0 as a backend. - Fit Deep Learning model to training data. - Evaluate the performance of trained CNNs and verify its generalization with various KPIs. Regularization techniques like dropout can improve network performance.

Course Overview

projects-img

Virtual Labs

projects-img

International Faculty

projects-img

Case Based Learning

projects-img

Post Course Interactions

projects-img

Case Studies,Instructor-Moderated Discussions

projects-img

Case Studies, Captstone Projects

Skills You Will Gain

What You Will Learn

Understand the theory and intuition behind Deep Neural Networks, and Residual Neural Networks, and Convolutional Neural Networks (CNNs)

Build and train a deep learning model based on Convolutional Neural Network and Residual blocks using Keras with Tensorflow 20 as a backend

Assess the performance of trained CNN and ensure its generalization using various Key performance indicators

Course Instructors

Author Image

Ryan Ahmed

Adjunct Professor & AI Enthusiast

Ryan Ahmed is a professor who is passionate about education and technology. Ryan holds a Ph.D. degree in Mechanical Engineering from McMaster* University, with focus on Mechatronics and Electric Vehi...

Course Reviews

Average Rating Based on 4 reviews

5.0

100%

Course Cover