Facial Expression Classification Using Residual Neural Nets

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En este proyecto guiado, tú:
2 hours
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Video de pantalla dividida
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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.

Habilidades que desarrollarás

  • Data Science

  • Deep Learning

  • Machine Learning

  • Python Programming

  • Computer Vision

Aprende paso a paso

En un video que se reproduce en una pantalla dividida con tu área de trabajo, tu instructor te guiará en cada paso:

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En un video de pantalla dividida, tu instructor te guía paso a paso

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