Buy Now Free
- Level Intermediate
- Duration 2 hours
-
Offered by
About
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.Auto Summary
Discover how to enhance your machine learning models with "Avoid Overfitting Using Regularization in TensorFlow," a focused course in the Big Data and Analytics domain. Guided by Coursera, this intermediate-level, project-based course spans 2 hours and delves into the essentials of weight and dropout regularization techniques to mitigate overfitting in image classification tasks. Perfect for those looking to deepen their machine learning expertise, this course offers practical, hands-on experience at no cost. Ideal for data enthusiasts and professionals aiming to refine their skills and ensure robust model performance.