courseraАнализ данных

Facial Expression Recognition with PyTorch

Курс от Coursera

Начальный≈ 1.6 чАнглийский

О курсе

In this 2-hour long guided-project course, you will load a pretrained state of the art model CNN and you will train in PyTorch to classify facial expressions. The data that you will use, consists of 48 x 48 pixel grayscale images of faces and there are seven targets (angry, disgust, fear, happy, sad, surprise, neutral). Furthermore, you will apply augmentation for classification task to augment images. Moreover, you are going to create train and evaluator function which will be helpful to write training loop. Lastly, you will use best trained model to classify expression given any input image.

Навыки, которые вы освоите

Model Training PyTorch (Machine Learning Library)Data Transformation Model Evaluation Applied Machine Learning Convolutional Neural Networks Transfer Learning Computer Vision Fine-tuning Classification Algorithms Image Analysis Data Preprocessing Deep Learning

Программа курса

1 модулей · 4 учебных материалов

01Project Overview4 материалов

Your Learning Journey

Project OverviewЧтение Facial Expression Recognition with PyTorch Лабораторная Assess Your KnowledgeЗадание Course End Survey - We appreciate your feedback!

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Parth Dhameliya

Machine Learning Instructor