Avoid Overfitting Using Regularization in TensorFlow

About this guided project

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.

What skills will you gain?

Machine Learning

Keras (Neural Network Library)

Deep Learning

Tensorflow

Artificial Neural Networks

Artificial Intelligence and Machine Learning (AI/ML)

Applied Machine Learning