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学生对 Coursera Project Network 提供的 Facial Expression Classification Using Residual Neural Nets 的评价和反馈

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课程概述

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....

热门审阅

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

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1 - Facial Expression Classification Using Residual Neural Nets 的 10 个评论(共 10 个)

创建者 Nugraha S A

Aug 30, 2020

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Oct 6, 2020

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Nov 27, 2020

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Sep 27, 2020

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Feb 13, 2022

创建者 Ed S

Dec 14, 2020