Develop a facial expression recognition model in Keras
Build and train a convolutional neural network (CNN)
Deploy the trained model to a web interface with Flask
Apply the model to real-time video streams and image data
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.
Prior experience with Python programming, and a theoretical understanding of Neural Networks and Convolutional Neural Networks is required.
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.
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 !
Very easy to follow and the instructor was very informative throughout the project. As a beginner myself, it was easy for me to follow along and understand the project
This project gave me complete knowledge for implementiing the face recognition in future.This help me to built an app using FLASK.Its a good project to start with.
指导 项目 可在台式设备和移动设备上学习吗？
指导 项目 的讲师是谁？
指导 项目 讲师是特定领域的专家，他们在项目的技能、工具或领域方面经验丰富，并且热衷于分享自己的知识以影响全球数百万的学生。
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您可以从指导 项目 中下载并保留您创建的任何文件。为此，您可以在访问云桌面时使用'文件浏览器'功能。
您可在页面顶部点按此指导 项目 的经验级别，查看任何知识先决条件。对于指导 项目 的每个级别，您的讲师会逐步为您提供指导。
我能直接通过 Web 浏览器来完成此指导 项目，而不必安装特殊软件吗？
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指导 项目 的学习体验如何？