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Back to Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization

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

YL

Invalid date

very useful course, especially the last tensorflow assignment. the only reason i gave 4 stars is due to the lack of practice on batchnorm, which i believe is one of the most usefule techniques lately.

XG

Invalid date

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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7201 - 7225 of 7,238 Reviews for Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization

By Sébastien C

•

Aug 4, 2020

Course covers the most important parts of hyperparameter tuning, regularization and optimization.

As a general remark for this specialization, the exercices do not provide any value. We just have to fill in some lines and submit our work.

As I tend to "learn by doing" I had to look for other tutorials and projects on other platforms (Kaggle, MachineLearningMastery's website) in order to complete my learning.

By Fabrizio N

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Dec 7, 2018

Good course content and clear exposition by Andrew. The course material however is not of a good standard. The slides can be downloaded but after all the hand scribbles by the tutor, they are barely decifrable. Some are just blank pages that need to be filled in with screenshots from of the videos. The assignements are often just a copy and paste exercise, and Jupyter crashes cause frequent loss of work.

By Goda R

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Feb 14, 2020

The video content is very good to get a good hang of theoretical aspects but the programming assignments are too spoon-fed because of which after doing filling the blanks, you don't feel confident enough to implement the same on your own. Instead the assignments should be changed to cases where instructions are given in words and entire function should be implemented by students.

By André Ø

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Nov 30, 2017

The TensorFlow part of the course felt out of place and not of the same quality as the previous material. It would have been better if another week was spent using TensorFlow to actually improving a NN and not just copy-paste an example into the assignment. Even after using TensorFlow in the assigment and passing, working with TensorFlow still

By Sergey K

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Jul 6, 2020

In general the course isn't deep enough. There are no summaries of the lectures, there are no excercises during lectures, the programming assignments are very weak, they don't challenge the use of lectures or anything - all necessary data are in the notebooks. All this course will be lost in a week.

By Ivan I P

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Apr 28, 2023

Sadly the last course assignment didn't compile properly in the sections that were outside the "graded functions" (more specifically in Week 3). Also some of the questions of the quizzes were intentionally misleading but for very unimportant details.

By Cristian G

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Sep 28, 2022

They should improve the programming labs.

There's too much repetition in things like programming a function that initializes the parameters while important things, like model definition, are already written in the notebook.

By James H

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Nov 18, 2018

The whole tensor flow introduction is weak - it clearly requires further reading, which is fine but totally out of kilter with the videos so far, which have taken things from first principles very clearly.

By Martin B

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Nov 16, 2017

A technical problem with the grader caused my grade to be artificially lower on the last project. Although I was instructed to resubmit, the course ended with a lower grade than I should have received.

By Anne R

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Oct 9, 2019

Not much implementation required of the students. More testing of the methods would be useful or if the concepts are the focus then this course should be merged into another course in the sequence.

By Brian R

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Jun 10, 2018

The course material is good but Jupyter notebook interface does not work correctly. You will waste a lot of you precious time fiddling and redoing work that you lose when the notebook fails to save.

By Abhishek K

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Dec 20, 2019

could be accomplished in a week. wastes time and doesnt go in sufficient depth.

After completion, you will get a 'taste' of optimization techniques, but it is not way comprehensive.

By Don F

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Feb 1, 2018

The course was good but there were multiple mistakes in the final programming assignment. These mistakes were reported in the forums over 4 months ago and have not been addressed.

By Sam G

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Mar 12, 2020

I find that the programming assignments have a LOT of copy pasta. Also, wasn't enthused to hear that we are using an out of data framework.

By Jaime T G

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Jan 23, 2019

Very poor explanations, not correlated with the quitz and exercises. Missing more theoretical material in pdf with some more explanations.

By Harshit G

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Feb 7, 2020

the instructor must give some more detailed explanation of the optimization techniques and hyperparameter tuning

By Deleted A

•

Sep 30, 2017

From my point of view this course deserves a bit more time. Too much material rushed through in too little time.

By Antoine J

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Apr 11, 2020

Was way harder than the Course1 and Course2. Should be simplifier on topic la batch norm, expo. weighted avg..

By Nathen N

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Sep 13, 2020

The notebooks of this class are terrible. They should receive more attention so you actually learn something.

By Sanford F

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Jan 24, 2019

There are many errors in the course materials and no one seems willing to fix them.

By Jay L

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Mar 26, 2021

When I submit the assignment, the output is the same, but there is no point for it

By Daniel T

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Sep 21, 2017

The programming assignment for week 3 was full of bugs

By Shankar N

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Feb 17, 2019

Could have been a 4 week course

By ENDER D I

•

Aug 22, 2021

last pa needs revision

By Rahul K

•

Apr 4, 2020

very basic course