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

4.9
stars
63,175 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

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

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.

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6851 - 6875 of 7,253 Reviews for Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization

By Lior R

•

Dec 28, 2019

The course was great but I think it needs more quizzes

By Aleksandar O

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May 13, 2018

Useful but rather dry iteration of the specialization.

By Tianxiang Z

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Nov 21, 2017

Great as usual except the typos in the assignment page

By yashwanth v

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Jun 3, 2020

more introduction to tensorflow would be appreciated.

By Clement K

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May 7, 2020

Very disapointed the course does not use tensorflow 2

By Thomas P

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Mar 31, 2020

Some more basics on tensorflow would have been great!

By Alexander S

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Mar 25, 2020

Very good overall, exercises could be a bit more free

By Ritesh R A

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Dec 17, 2019

tensorflow sesssion should have been more descriptive

By Mihaly K

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Nov 6, 2019

Assignments sometimes too easy, minimal input needed.

By Brent D

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Feb 25, 2019

Tensorflow project was rushed and hard to understand.

By Franklin W

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Jul 14, 2018

I want to be challenged more, less tips and more DIY.

By Seth T

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Mar 26, 2018

Short course but was really excited to delve into TF.

By 王婷

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Feb 16, 2018

good PA examples, that could benefit my further study

By Arhan G

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Jan 30, 2018

Andrew is a great lecturer. The videos are excellent.

By Alaa B

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Jun 13, 2023

very useful for both academic and business purposes

By Nicolas M

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Jun 20, 2021

a little more practice on TF would have been nice...

By Hak K C

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Apr 5, 2018

Course was concise and assignments were well guided.

By Stuart R

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Mar 11, 2018

Good course. Minor errors/typos in presented videos.

By Venkatraman N

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Mar 10, 2018

Quite not challenging in the programming assignments

By Uday Y

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Apr 30, 2020

Tensorflow assignment should be modified to use 2.x

By Dmitry K

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Apr 14, 2020

TensorFlow should be updated to the latest version.

By Fangshi L

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May 19, 2019

Good course, although some bugs in homework grading

By Sumeet R

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Feb 10, 2019

very good course - gets to practical aspects of ML!

By Juan O

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Dec 2, 2017

Having slides like in other courses will be helpful

By SPS P

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Jun 27, 2020

Tensorflow could have been taught in a better way.