Deploy Neural Network Regularizers to Prevent Overfitting
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Learn how to deploy neural network regularizers like dropout and L2 regularization to prevent overfitting in your models. Understand how regularization techniques improve model generalization, ensuring better performance on unseen data.
At a Glance
In this guided project, we will teach you the most famous regularization techniques that are used by Data Science practitioners to mitigate the overfitting problem of training Machine Learning and Deep Learning models.
When you train a neural network to solve a real-world problem, for example, regression or classification, an issue you and many Data Scientists will often face is that the model is overfitted to the training dataset and is not performing well on the test dataset.
Overfitting can easily happen when your model learns too much information from the training set. It can produce a set of model parameters that is a “perfect fit” only for the training set. This doesn’t help with our goal of using neural networks to solve problems as we would want the solution (i.e: the model) to be also applicable to the new data that it has never seen. Thus, we need to constrain the model from fitting too well to the training set, and we do so by using Regularization methods in model training.
In this guided project, you will learn and then practice 4 different regularization methods that Data Scientists use most often to stabilize model performance. They are:
- L1 (Lasso) Regularization
- L2 (Ridge) Regularization
- Batch Normalization
- Dropout
A Look at the Project Ahead
After completing this guided project you will be able to:
- Understand how regularization techniques such as L1, L2, Dropout, and BatchNorm work for neural networks.
- Describe the differences between L1 and L2 regularization, such as the difference in their feasible regions.
- Apply regularization using Keras APIs and train neural networks on real-world datasets.
What You’ll Need
To complete this guided project, you will need a basic understanding of the working mechanics of neural networks, such as activations, optimizers, loss functions, etc. You will also need some prior experience working with Keras to be able to use its APIs to build, compile and train a neural network.
This course mainly uses Python and JupyterLabs. Although these skills are recommended prerequisites, no prior experience is required as this Guided Project is designed for complete beginners.
Frequently Asked Questions
Do I need to install any software to participate in this project?
Everything you need to complete this project will be provided to you via the Skills Network Labs and it will all be available via a standard web browser.
What web browser should I use?
The Skills Network Labs platform works best with current versions of Chrome, Edge, Firefox, Internet Explorer, or Safari.
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