在TensorFlow中用正则化防止过拟合

Avoid Overfitting Using Regularization in TensorFlow

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Rhyme
Coursera
  • 完成时间大约为 2 个小时
  • 中级
  • 英语
注:本课程由Coursera和Linkshare共同提供,因开课平台的各种因素变化,以上开课日期仅供参考

你将学到什么

Develop an understanding on how to avoid over-fitting with weight regularization and dropout regularization

Be able to apply both weight regularization and dropout regularization in Keras with TensorFlow backend

课程概况

In this 2-hour long project-based course, you will learn the basics of using weight regularization and dropout regularization to reduce over-fitting in an image classification problem. By the end of this project, you will have created, trained, and evaluated a Neural Network model that, after the training and regularization, will predict image classes of input examples with similar accuracy for both training and validation sets.

Note: 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.

课程大纲

TensorFlow Beginner: Avoid Over-fitting Using Regularization

Welcome to this project-based course on Avoid Over-fitting Using Regularization with Keras and TensorFlow. In this project, you will learn the basics of using weight regularization and dropout regularization to reduce over-fitting in an image classification problem. By the end of this 2-hour long project, you will have created, trained, and evaluated a Neural Network model that, after the training and regularization, will predict image classes of input examples with similar accuracy for both training and validation sets.

课程项目

Import the data

Process the data

Regularization and Dropout

Creating the Experiment

Assess the final results

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