Avoid Overfitting Using Regularization in TensorFlow

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En este proyecto guiado, tú:

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

Clock2 hours
IntermediateIntermedio
CloudNo se necesita descarga
VideoVideo de pantalla dividida
Comment DotsInglés (English)
LaptopSolo escritorio

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.

Habilidades que desarrollarás

Data ScienceDeep LearningMachine LearningTensorflowkeras

Aprende paso a paso

En un video que se reproduce en una pantalla dividida con tu área de trabajo, tu instructor te guiará en cada paso:

  1. Import the data

  2. Process the data

  3. Regularization and Dropout

  4. Creating the Experiment

  5. Assess the final results

Cómo funcionan los proyectos guiados

Tu espacio de trabajo es un escritorio virtual directamente en tu navegador, no requiere descarga.

En un video de pantalla dividida, tu instructor te guía paso a paso

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