Weight Initialization for Deep Networks

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Skills You'll Learn

Tensorflow, Deep Learning, Mathematical Optimization, hyperparameter tuning

Reviews

4.9 (59,699 ratings)

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NA

Jan 13, 2020

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After completion of this course I know which values to look at if my ML model is not performing up to the task. It is a detailed but not too complicated course to understand the parameters used by ML.

CV

Dec 23, 2017

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Exceptional Course, the Hyper parameters explanations are excellent every tip and advice provided help me so much to build better models, I also really liked the introduction of Tensor Flow\n\nThanks.

From the lesson

Practical Aspects of Deep Learning

Discover and experiment with a variety of different initialization methods, apply L2 regularization and dropout to avoid model overfitting, then apply gradient checking to identify errors in a fraud detection model.

Taught By

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    Andrew Ng

    Instructor

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    Kian Katanforoosh

    Senior Curriculum Developer

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    Younes Bensouda Mourri

    Curriculum developer

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