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Learner Reviews & Feedback for Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization by DeepLearning.AI

4.9
stars
62,825 ratings

About the Course

In the second course of the Deep Learning Specialization, you will open the deep learning black box to understand the processes that drive performance and generate good results systematically. By the end, you will learn the best practices to train and develop test sets and analyze bias/variance for building deep learning applications; be able to use standard neural network techniques such as initialization, L2 and dropout regularization, hyperparameter tuning, batch normalization, and gradient checking; implement and apply a variety of optimization algorithms, such as mini-batch gradient descent, Momentum, RMSprop and Adam, and check for their convergence; and implement a neural network in TensorFlow. The Deep Learning Specialization is our foundational program that will help you understand the capabilities, challenges, and consequences of deep learning and prepare you to participate in the development of leading-edge AI technology. It provides a pathway for you to gain the knowledge and skills to apply machine learning to your work, level up your technical career, and take the definitive step in the world of AI....

Top reviews

XG

Oct 30, 2017

Thank you Andrew!! I know start to use Tensorflow, however, this tool is not well for a research goal. Maybe, pytorch could be considered in the future!! And let us know how to use pytorch in Windows.

JS

Apr 4, 2021

Fantastic course and although it guides you through the course (and may feel less challenging to some) it provides all the building blocks for you to latter apply them to your own interesting project.

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5876 - 5900 of 7,216 Reviews for Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization

By Yates Y

Oct 24, 2017

Nice!

By wangdawei

Oct 15, 2017

good.

By 段立溟

Oct 14, 2017

表白吴伯伯

By Dave P

Oct 10, 2017

Hello

By Shiyuan Z

Oct 2, 2017

good!

By 刘其波

Oct 2, 2017

good!

By Rafael L

Sep 26, 2017

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By Hong W

Sep 24, 2017

Sweet

By Edisson R

Aug 24, 2017

12/10

By Omm J

Sep 18, 2023

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Apr 7, 2022

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Mar 10, 2022

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Jan 11, 2022

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Sep 5, 2021

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Aug 14, 2021

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Aug 7, 2021

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Jul 7, 2021

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Jun 28, 2021

good

By Aniruddha B

Apr 22, 2021

Nice

By GC L

Feb 18, 2021

good

By Ibadurrahman

Dec 17, 2020

nice

By Ivri K

Dec 11, 2020

nice

By Gulnara A

Oct 15, 2020

Best

By Atrayee C

Sep 30, 2020

good

By Souvik P

Sep 29, 2020

good