Chevron Left
Volver a Facial Expression Classification Using Residual Neural Nets

Opiniones y comentarios de aprendices correspondientes a Facial Expression Classification Using Residual Neural Nets por parte de Coursera Project Network

4.6
estrellas
69 calificaciones

Acerca del Curso

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....

Principales reseñas

NA

29 de ago. de 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

5 de oct. de 2020

the lecturer is so geniuuuuuuussss, thank you so much

Filtrar por:

1 - 10 de 10 revisiones para Facial Expression Classification Using Residual Neural Nets

por Nugraha S A

30 de ago. de 2020

por Endang P G

6 de oct. de 2020

por SYED S

27 de nov. de 2020

por Jesus M Z F

8 de ago. de 2020

por SASIN N

10 de ago. de 2020

por Partha B

27 de sep. de 2020

por Mudunuri Y V 9

29 de jul. de 2021

por Narendra G

30 de sep. de 2020

por Parag

13 de feb. de 2022

por Ed S

14 de dic. de 2020