Avoid Overfitting Using Regularization in TensorFlow

Overview

Class Central Tips

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.

Syllabus

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.