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Über den Kurs

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-Bewertungen

NA
29. Aug. 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. Okt. 2020

the lecturer is so geniuuuuuuussss, thank you so much

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1 - 9 von 9 Bewertungen für Facial Expression Classification Using Residual Neural Nets

von Nugraha S A

30. Aug. 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

von Endang P G

6. Okt. 2020

the lecturer is so geniuuuuuuussss, thank you so much

von SYED S

27. Nov. 2020

cool and best

von Jesus M Z F

8. Aug. 2020

Great course

von SASIN N

10. Aug. 2020

Easy Quiz thanks for this course it helped me to understand concept clearly without wasting much of my time.

von Partha B

27. Sep. 2020

Good course , for a short and introductory portion for a bigger work.

von Mudunuri Y V 9

29. Juli 2021

super

von Narendra G

30. Sep. 2020

Too short, Guided projects are of no use.

von Ed/Deb S

14. Dez. 2020

For me this was impossible. I did not realize how much previous knowledge was required.