Avoid Overfitting Using Regularization in TensorFlow

Course Overview

What does this course tell?
(Please note that the following overview content is from Alison)
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

We consider the value of this course from multiple aspects, and finally summarize it for you from three aspects: personal skills, career development, and further study:
(Kindly be aware that our content is optimized by AI tools while also undergoing moderation carefully from our editorial staff.)
What skills and knowledge will you acquire during this course?
By taking this 2-hour long project-based course, learners will acquire the skills and knowledge necessary to reduce over-fitting in an image classification problem. Learners will learn the basics of using weight regularization and dropout regularization to create, train, and evaluate a Neural Network model. After the training and regularization, learners will be able to predict image classes of input examples with similar accuracy for both training and validation sets. This course is best suited for learners based in the North America region, however, the same experience is being worked on for other regions.
lHow does this course contribute to professional growth?
This course provides learners with the opportunity to gain a better understanding of how to use weight regularization and dropout regularization to reduce over-fitting in an image classification problem. Through a project-based approach, learners will be able to create, train, and evaluate a Neural Network model that will be able to predict image classes of input examples with similar accuracy for both training and validation sets. This course is beneficial for professional growth as it provides learners with the skills and knowledge necessary to effectively reduce over-fitting in image classification problems.

Is this course suitable for preparing further education?
Yes, this course is suitable for preparing further education. It provides learners with the basics of using weight regularization and dropout regularization to reduce over-fitting in an image classification problem. By the end of the course, learners will have created, trained, and evaluated a Neural Network model that will predict image classes of input examples with similar accuracy for both training and validation sets. This course works best for learners who are based in the North America region, but the same experience is being worked on for other regions.