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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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von Nugraha S A

30. Aug. 2020

von Endang P G

6. Okt. 2020

von SYED S

27. Nov. 2020

von Jesus M Z F

8. Aug. 2020

von SASIN N

10. Aug. 2020

von Partha B

27. Sep. 2020

von Mudunuri Y V 9

29. Juli 2021

von Narendra G

30. Sep. 2020

von Parag

13. Feb. 2022

von Ed S

14. Dez. 2020