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

4.9
stars
63,169 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.

HD

Dec 5, 2019

I enjoyed it, it is really helpful, id like to have the oportunity to implement all these deeply in a real example.

the only thing i didn't have completely clear is the barch norm, it is so confuse

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6901 - 6925 of 7,253 Reviews for Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization

By Harsha S

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Jan 23, 2018

Builds on fundamentals, which is always good!!

By Tiến H N

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Apr 6, 2021

The coding assignment is not challenge enough

By Paraskevas P

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Mar 29, 2020

More practical examples would be very useful.

By Aymen S

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Aug 13, 2019

Cours intéressant merci beaucoup Mr Andrew Ng

By Anna W

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Sep 12, 2018

batch optimization is good but not graded :-(

By Yiping W

•

Jun 23, 2018

should provide more materials for tensor flow

By Abhishek A

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Nov 25, 2021

Tensor Flow could have been elaborated more.

By MD N F

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Feb 15, 2021

Concepts explanation was not up to the mark.

By Freddy A C F

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Jun 25, 2020

great now I understand Adam optimizer better

By 長谷川一輝

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Jan 9, 2020

I can understand concept of deep neural net!

By Bernardt D

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Jun 26, 2018

There were some typos throughout the course.

By Michael N

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Apr 13, 2018

Great but som explanations seems a bit wierd

By Karl B

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Oct 28, 2018

Tensor flow stuff could be better explained

By Mecrux

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Sep 4, 2018

Maybe should spend more time on tensorflow?

By Arnav D

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May 21, 2018

Best TensorFlow tutorial I have seen so far

By Sandor T C

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Jan 29, 2018

too much hand holding, no struggle to learn

By John R G P

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Aug 19, 2020

Its necessary to actualize to tensorflow 2

By Paulo A V

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Nov 16, 2017

Nice complement to the first course on ANN

By Lina H

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Aug 4, 2022

I wish it had moe practical assignments.

By thibault c

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Nov 7, 2019

more intuitive insights would be helpful

By EURICO O D C D S C

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Jan 8, 2018

Having tensorflow is great. It's a must.

By taofeek o

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Mar 12, 2020

A great course and it's well explained.

By Dex D X

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Oct 16, 2017

programming assignments are too easy XD

By John Y

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Sep 15, 2017

Programming assignments are too simple.

By Michael B

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Dec 19, 2017

Could do with more tensorflow examples