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

4.9
stars
63,068 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

AA

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Assignment in week 2 could not tell the difference between 'a-=b' and 'a=a-b' and marked the former as incorrect even though they are the same and gave the same output. Other than that, a great course

AM

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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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3026 - 3050 of 7,244 Reviews for Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization

By Zhi L

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Mar 17, 2020

Thanks to Andrew and the team for providing such great course!

By 靳文彬

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Mar 9, 2020

Andrew is a great teacher, the whole series is almost perfect!

By John G

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Feb 16, 2020

Great overview of optimizing networks and intro to tensorflow.

By Alexandre F

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Jan 20, 2020

Great overview of the rules of thumbs to optimize DL NN tuning

By Apolo T A B

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Oct 29, 2019

Bom curso, mas os notebooks foram muito melhores que as aulas!

By dyfbobby

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Jun 16, 2019

Further understanding on deep nn construction and optimization

By Nelson F A

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Jun 2, 2019

As expected from Andrew Ng's previous courses, just excellent!

By Binjer

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May 25, 2019

讲解的非常细致和深入，对初学者友好。而随着对ML领域的理解不断丰富，也会产生更深入的理解。非常感谢为这门课程做出贡献的所有人

By Sabarish K

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May 21, 2019

Dismantled the Neural Nets into several understandable blocks.

By amravi s

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May 11, 2019

well planned and excellent method of teaching.Loved the course

By Gyuho S

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Mar 31, 2019

Awesome. It is getting serious and more fun as it gets Deeper!

By jackytu256

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Mar 11, 2019

great course to deeply understand the meaning of Deep learning

By Vitalii S

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Mar 3, 2019

Easy, thanks for good suggestions and interesting information.

By Md. M H

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Feb 12, 2019

Great course and great tutorials on a deep learning framework.

By 伟杰 邓

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Dec 5, 2018

I will keep taking the following courses given by professor Ng

By Vinay M

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Aug 15, 2018

Excellent start for using TensorFlow and hyperparameter tuning

By Sudip C M

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Aug 5, 2018

Excellent introduction to hyperparameter tuning and tensorflow

By Shounak D

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Jul 31, 2018

Great course as usual..excited to complete the specialisation.

By SUBHABRATA T

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Jul 23, 2018

In depth theory but i need some more programming explaintion .

By rachit g

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Jul 17, 2018

Excellent course. This course has no substitute in the market.

By Ganesh R

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Jun 30, 2018

Good course to understand the Hyperparameters and Optimization

By Divya R

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Mar 31, 2018

Great, but gets complex. Which is good, but also difficult! :)

By Cheryl A

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Mar 21, 2018

Really great insights and intuition on tuning and optimization

By Phi N V

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Dec 14, 2017

the course is so good but the problem set should be more handy

By Amit P

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Dec 7, 2017

Anyone who wants to learn Deep learning must take this course.