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Вернуться к Facial Expression Recognition with Keras

Отзывы учащихся о курсе Facial Expression Recognition with Keras от партнера Coursera Project Network

Оценки: 938

О курсе

In this 2-hour long project-based course, you will build and train a convolutional neural network (CNN) in Keras from scratch to recognize facial expressions. The data consists of 48x48 pixel grayscale images of faces. The objective is to classify each face based on the emotion shown in the facial expression into one of seven categories (0=Angry, 1=Disgust, 2=Fear, 3=Happy, 4=Sad, 5=Surprise, 6=Neutral). You will use OpenCV to automatically detect faces in images and draw bounding boxes around them. Once you have trained, saved, and exported the CNN, you will directly serve the trained model to a web interface and perform real-time facial expression recognition on video and image data. This course runs on Coursera's hands-on project platform called Rhyme. On Rhyme, you do projects in a hands-on manner in your browser. You will get instant access to pre-configured cloud desktops containing all of the software and data you need for the project. Everything is already set up directly in your internet browser so you can just focus on learning. For this project, you’ll get instant access to a cloud desktop with Python, Jupyter, and Keras pre-installed. Notes: - You will be able to access the cloud desktop 5 times. However, you will be able to access instructions videos as many times as you want. - This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions....

Лучшие рецензии


3 июля 2020 г.

All the concepts are well explained. The project gives a nice insight about how we can integrate different ML frameworks to build a project and also how to deploy the model as a web app by Flask.


26 окт. 2020 г.

This is a great hands-on project ! It is very well designed, and the instructor guides you to do it step by step. I enjoy this learning and practicing process a lot. Thank you !

Фильтр по:

101–125 из 138 отзывов о курсе Facial Expression Recognition with Keras

автор: gajjala s r

21 апр. 2020 г.

need a bit more explanation and more projects its not enough to get on....but for beginners its the best

автор: Edgar I

12 июня 2020 г.

The code of the complet project is not available, over all the flash and the flow of all project

автор: Pragya S

22 апр. 2020 г.

Nice course for those who have prior knowledge of the basics of the related topics.

автор: RAHUL B

16 авг. 2020 г.

It's a good guided project. This project will help you to understand many things.

автор: Harla

5 июня 2020 г.

Loved the project and the hands-on experience. Quick response to doubts !

автор: Belcy B

4 июня 2020 г.

It's very helpful to learn how to implement CNN using keras

автор: Satyam A

28 авг. 2020 г.

Has some mistakes in the code, that could be rectified.

автор: Andres G

19 авг. 2020 г.

The plataform has some problems to disabled block mayus

автор: Pramay S

22 июня 2020 г.

The coding part can be left out for the students.

автор: EL H E

23 июля 2020 г.

this is an amazing guide project for beginners .

автор: Shivam S

13 июня 2020 г.

Good project for applying the concepts of CNN .

автор: kuppala P

23 июня 2020 г.

Good course for new learner's in a short time.

автор: Md.Shamiul I S

20 мая 2020 г.

good one. learned a lot


28 июня 2020 г.

Good for beginners.

автор: Moresh M

19 июля 2020 г.

Informative course

автор: sanjay p

11 июня 2020 г.


автор: daniel s

16 мар. 2021 г.

well explained

автор: Sattenapally s c

4 июля 2020 г.

Good course

автор: Ankit K

8 июня 2020 г.

It is nice

автор: Matapathi S

14 июня 2020 г.

Thank you

автор: Nelson R

2 сент. 2020 г.


автор: Estefania T

16 июня 2020 г.

The content is good but the platform it still not comfortable to use. We have to code in a tiny screen, flexibility is needed. From a developer point of view, nice job Rhyme people, still work to do.

автор: Ayush G

9 авг. 2020 г.

This could be better, the accuracy achieved is not appreciable.

Also, the user interface was bad, rhyme cloud desktop was causing too much of lag collab could be a better option

автор: Ankit P

1 июня 2020 г.

I didn't get the dataset . Sir If You give me the proper link to download the dataset then it is very helpful for me .

автор: Dunna S

10 июня 2020 г.

Good one for very beginners in computer vision. Not for intermediate or advanced audience