Facial Expression Recognition with Keras
23.461 ya inscrito
23.461 ya inscrito
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.
Convolutional Neural Network
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por AS20 de may. de 2020
A really good course on how to apply theoretical knowledge into real world.
Course instructor was great!
por JJ30 de jul. de 2020
Good project to know the pipeline and simple deployment. however basic understanding of the machine learning terminology is needed.
por PD10 de abr. de 2020
The course in itself was good, but the rhyme interface kept wasting time by freezing either the videos or the cloud computer
por TS3 de sep. de 2020
Nice project! but the code in camera.py and the main.py file which is used to create a flask app to serve predictions should be explained in more detail.