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

4.9
stars
63,494 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

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 :)

AM

Oct 8, 2019

I really enjoyed this course. Many details are given here that are crucial to gain experience and tips on things that looks easy at first sight but are important for a faster ML project implementation

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5901 - 5925 of 7,284 Reviews for Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization

By beihai Q

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Nov 12, 2018

Good!

By Vinita M

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Oct 26, 2018

Great

By Simon H

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Oct 7, 2018

Great

By shenzhonghua

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Sep 3, 2018

Great

By 노재화

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Sep 2, 2018

Good!

By He L

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Jul 6, 2018

cool!

By Ashish B

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Jun 24, 2018

great

By jiahaozhou

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Jun 11, 2018

grate

By sachin

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Jun 9, 2018

super

By blaster_fire

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May 23, 2018

Good！

By Du L

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Feb 23, 2018

Best!

By Won J

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Feb 19, 2018

Great

By benedikt h

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Feb 18, 2018

Great

By lalaqingla

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Feb 8, 2018

good!

By 冉祥映

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Feb 5, 2018

nice.

By Minsub W

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Feb 1, 2018

good!

By qingjunwu

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Dec 19, 2017

Good!

By Siddarth V

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Dec 11, 2017

Great

By iron

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Dec 6, 2017

great

By 董雪振

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Dec 3, 2017

Good!

By Hiep N V

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Nov 17, 2017

G

r

e

a

t

By 崔宁

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Nov 13, 2017

GREAT

By Jian X

•

Oct 30, 2017

挺不错的。

By Yates Y

•

Oct 24, 2017

Nice！

By wangdawei

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Oct 15, 2017

good.