Activity Recognition using Python, Tensorflow and Keras

提供方
Coursera Project Network
在此指导项目中,您将:

Learn about data augmentation.

Learn about transfer learning using training the pre-trained model InceptionNet V3 on the data.

Clock1.5 hours
Beginner初级
Cloud无需下载
Video分屏视频
Comment Dots英语(English)
Laptop仅限桌面

Note: The rhyme platform currently does not support webcams, so this is not a live project. This guided project is about human activity recognition using Python,TensorFlow2 and Keras. Human activity recognition comes under the computer vision domain. In this project you will learn how to customize the InceptionNet model using Tensorflow2 and Keras. While you are watching me code, you will get a cloud desktop with all the required software pre-installed. This will allow you to code along with me. After all, we learn best with active, hands-on learning. Special Feature: 1.Manually label images. 2. Learn how to use data augmentation normalization. 3. Learn about transfer learning using training the pre-trained model InceptionNet V3 on the data. Note: This project works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.

您要培养的技能

  • Deep Learning
  • Python Programming
  • Tensorflow
  • cognitive data science
  • keras

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:

  1. Learn how to normalize data to improve accuracy of the final results.

  2. Learn how to fine tune the model to improve it's accuracy.

  3. Learn how to apply transfer learning using InceptionNet V3.

  4. Learn how to augment data to prevent overfitting of the model.

  5. Learn how to label data manually as 0 or 1.

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

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