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

4.9
stars
63,489 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

NC

Aug 18, 2017

Yet another excellent course by Professor Ng! Really helped me gain a detailed understanding of optimization techniques such as RMSprop and Adam, as well as the inner workings of batch normalization.

BA

May 31, 2020

Very good course, useful and smart. Some of the example are on tensorflow 1 but I think that they will update them soon to keras tf2 Thank you!I will pass on what I have learned here to undergrads :)

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5726 - 5750 of 7,283 Reviews for Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization

By Stefan N

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Aug 13, 2018

Spot on!

By 喆 李

•

Apr 29, 2018

Asesome!

By 张子昂

•

Apr 25, 2018

The Best

By neozgx

•

Apr 6, 2018

很不错，通俗易懂

By Adit K

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Mar 23, 2018

loved it

By Tim K

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Mar 3, 2018

Love it.

By Sarah W

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Feb 18, 2018

Awesome!

By Bharath

•

Dec 29, 2017

Best yet

By mier

•

Dec 13, 2017

awesome!

By Thar M H

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Nov 6, 2017

Awesome!

By Ke L

•

Oct 23, 2017

exciting

By Abhijeet R P

•

Oct 12, 2017

Best! :D

By viper

•

Sep 30, 2017

perfect！

By Jeff W H

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Sep 29, 2017

Amazing!

By zhifeng j

•

Sep 28, 2017

awesome!

By Shuaifeng Z

•

Sep 21, 2017

Perfect！

By geekerryan

•

Sep 19, 2017

吴大大棒棒的~~

By Varun R

•

Sep 10, 2017

Awesome!

By Oswaldo B F

•

Aug 30, 2017

Amazing!

By Liam Y

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Aug 29, 2017

awesome!

By Michael S

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Aug 23, 2017

Awesome!

By Felipe F D R

•

Aug 17, 2017

Amazing!

By Zahra S

•

Nov 26, 2023

Awesome

By 许广峰

•

Aug 17, 2021

Thanks

By Abhishek U

•

Jun 13, 2021

great !