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

4.9
stars
63,221 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

JS

Apr 4, 2021

Fantastic course and although it guides you through the course (and may feel less challenging to some) it provides all the building blocks for you to latter apply them to your own interesting project.

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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6651 - 6675 of 7,257 Reviews for Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization

By 杨之龙

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Apr 4, 2018

I have to complain why dont accompany videos with quiz and notes just like the ML coursera.

By Hyatt B

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Jan 9, 2018

Content great! I'm not convinced Jupyter notebooks are the best approach for this material.

By Joe S

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Jan 6, 2021

A good course that provides a lot of insight into how to improve upon Deep Neural Networks

By Yen L B

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Aug 31, 2018

Good explanations. But tutorials can be improved to demonstrate the various tuning effects

By Surya J

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Apr 22, 2019

Great course to build intuition about tuning NN. Solid Foundation in very short duration.

By Kritika A

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Mar 25, 2019

There was a lot of hand holding in programming assignments. It needs to be more rigorous.

By Vasilis S

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Sep 26, 2018

Very informative course. The assignments are too trivial. Could've been more challenging.

By David D

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Oct 7, 2017

Last programming assignments had some errors in them that could've easily been corrected.

By Pradeep S

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Aug 6, 2023

Great course to get the basics of neural networks and deep learning as solid foundation.

By Bhargav R

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May 21, 2020

Great content. Filled with rich techniques to improve models, hyperparameter tuning etc.

By Václav R

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Feb 14, 2019

Could have focused a bit more on the tensorflow. Other than that - Great course, thanks!

By Rajiv C

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Aug 25, 2017

It was fun to get to know other optimization techniques and how to speed up the network.

By Prajwal M H (

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Apr 21, 2020

The difficulty of the course is medium. More time should be spent by learners for this

By Andrew W

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Jul 29, 2019

Felt fast faced. But a good introduction to neural network hyperparameter optimization.

By Gaurav B

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Nov 13, 2018

Course was really good, but I feel in tenserflow regularization should also be covered.

By Ahmed A

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Oct 28, 2018

The course was very informative but the tensorflow notebook was buggy and needs fixing.

By Arjan G

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Dec 7, 2017

Good course, but still has some minor issues in the assignments that needs to be fixed.

By Nils-Jörn

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Dec 3, 2020

Don't like the Jupyter environment - i loved the Octave used in the basic ML Course...

By Oleksandr T

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Jul 28, 2019

Last code assignment is a mess. Looks like organizers have no intention to fix errors.

By Cindy Q

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Apr 1, 2018

Week 3 feels a little rushed. The tensorflow material can be explained more in detail.

By Nicola P

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Nov 11, 2017

Lessons are very clear and insightful. I would have expected more complex assignments.

By sree v

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Sep 5, 2017

TensorFlow assignment is not good. There are many issues in submitting the assignment.

By Mohammad M H

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Jul 31, 2020

most of the topics and assignments are great! but few parts of them are a bit boring!

By Mayur S

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May 9, 2020

Some more programming exercise with an combination of all the weeks would have helped

By Simona T

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Mar 9, 2020

The assignements are very instructive and useful, but they could be more challenging.