Chevron Left
Back to Facial Expression Classification Using Residual Neural Nets

Learner Reviews & Feedback for Facial Expression Classification Using Residual Neural Nets by Coursera Project Network

4.6
stars
69 ratings

About the Course

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....

Top reviews

NA

Aug 29, 2020

Wonderful course! I got a lot of new knowledge, particularly about how CNN really works and how to apply it using existing libraries in python! 6/5

EG

Oct 5, 2020

the lecturer is so geniuuuuuuussss, thank you so much

Filter by:

1 - 10 of 10 Reviews for Facial Expression Classification Using Residual Neural Nets

By Nugraha S A

Aug 30, 2020

By Endang P G

Oct 6, 2020

By SYED S

Nov 27, 2020

By Jesus M Z F

Aug 8, 2020

By SASIN N

Aug 10, 2020

By Partha B

Sep 27, 2020

By Mudunuri Y V 9

Jul 29, 2021

By Narendra G

Sep 30, 2020

By Parag

Feb 13, 2022

By Ed S

Dec 14, 2020