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Вернуться к Facial Expression Classification Using Residual Neural Nets

Отзывы учащихся о курсе Facial Expression Classification Using Residual Neural Nets от партнера Coursera Project Network

4.6
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Оценки: 71

О курсе

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....

Лучшие рецензии

NA

29 авг. 2020 г.

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 окт. 2020 г.

the lecturer is so geniuuuuuuussss, thank you so much

Фильтр по:

1–10 из 10 отзывов о курсе Facial Expression Classification Using Residual Neural Nets

автор: Nugraha S A

30 авг. 2020 г.

автор: Endang P G

6 окт. 2020 г.

автор: SYED S

27 нояб. 2020 г.

автор: Jesus M Z F

8 авг. 2020 г.

автор: SASIN N

10 авг. 2020 г.

автор: Partha B

27 сент. 2020 г.

автор: Mudunuri Y V 9

29 июля 2021 г.

автор: Narendra G

30 сент. 2020 г.

автор: Parag

13 февр. 2022 г.

автор: Ed S

14 дек. 2020 г.