Facial Expression Classification Using Residual Neural Nets
Understand the theory and intuition behind Deep Neural Networks, and Residual Neural Networks, and Convolutional Neural Networks (CNNs).
Build and train a deep learning model based on Convolutional Neural Network and Residual blocks using Keras with Tensorflow 2.0 as a backend.
Assess the performance of trained CNN and ensure its generalization using various Key performance indicators.
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.
您要培养的技能
- Data Science
- Deep Learning
- Machine Learning
- Python Programming
- Computer Vision
分步进行学习
在与您的工作区一起在分屏中播放的视频中，您的授课教师将指导您完成每个步骤：
Project Overview/Understand the problem statement and business case
Import Libraries/datasets and perform preliminary data processing
Perform Image Visualization
Perform Image Augmentation, normalization and splitting
Understand the theory and intuition behind Deep Neural Networks and CNNs
Build and Train Residual Neural Network Model
Assess the Performance of the Trained Model
指导项目工作原理
您的工作空间就是浏览器中的云桌面，无需下载
在分屏视频中，您的授课教师会为您提供分步指导
审阅
来自FACIAL EXPRESSION CLASSIFICATION USING RESIDUAL NEURAL NETS的热门评论
Wonderful course! I got a lot of new knowledge, particularly about how CNN really works and how to apply it using existing libraries in python! 6/5
Easy Quiz thanks for this course it helped me to understand concept clearly without wasting much of my time.
Good course , for a short and introductory portion for a bigger work.
the lecturer is so geniuuuuuuussss, thank you so much
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