Avoid Overfitting Using Regularization in TensorFlow

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В этом Guided Project вы:

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
IntermediateУчащийся среднего уровня
CloudЗагрузка не требуется
VideoВидео на разделенном экране
Comment DotsАнглийский
LaptopТолько для ПК

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.

Навыки, которые вы получите

Data ScienceDeep LearningMachine LearningTensorflowkeras

Будете учиться пошагово

На видео, которое откроется рядом с рабочей областью, преподаватель объяснит эти шаги:

  1. Import the data

  2. Process the data

  3. Regularization and Dropout

  4. Creating the Experiment

  5. Assess the final results

How Guided Projects work

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На разделенном экране видео преподаватель предоставляет пошаговые

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