Avoid Overfitting Using Regularization in TensorFlow

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Avoid Overfitting Using Regularization in TensorFlow 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

In this Guided Project, you will:

Develop an understanding on how to avoid over-fitting with weight regularization and dropout regularization

Be able to apply both weight regularization and dropout regularization in Keras with TensorFlow backend

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.

Learn step-by-step
In a video that plays in a split-screen with your work area, your instructor will walk you through these steps:

Import the data

Process the data

Regularization and Dropout

Creating the Experiment

Assess the final results

To prevent overfitting, the best solution is to use more complete training data. The dataset should cover the full range of inputs that the model is expected to handle. Additional data may only be useful if it covers new and interesting cases. A model trained on more complete data will naturally generalize better.

Overfitting can be fixed by reducing the number of features in the training data … Regularization techniques reduce the possibility of a neural network … how to implement them within neural networks using TensorFlow(Keras).

project-based course, you will learn the basics of using weight regularization and dropout regularization to reduce over-fitting in an image …

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