Volver a Avoid Overfitting Using Regularization in TensorFlow
Opiniones y comentarios de aprendices correspondientes a Avoid Overfitting Using Regularization in TensorFlow por parte de Coursera Project Network
75 calificaciones
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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.
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1 - 4 de 4 revisiones para Avoid Overfitting Using Regularization in TensorFlow
por Ishwari R
•7 de ago. de 2020
please enable me to reset the deadlines as i was unable to complete..
por tale p
•26 de jun. de 2020
good
por Ricardo D
•30 de ene. de 2021
Good introduction to regularization techniques. It's nice to learn these techniques with a relevant, but simple, example code.
por Deleted A
•12 de may. de 2020
Not efficiently
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