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

4.9
stars
62,958 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

NC

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Just as great as the previous course. I feel like I have a much better chance at figuring out what to do to improve the performance of a neural network and TensorFlow makes much more sense to me now.

NC

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Yet another excellent course by Professor Ng! Really helped me gain a detailed understanding of optimization techniques such as RMSprop and Adam, as well as the inner workings of batch normalization.

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7076 - 7100 of 7,225 Reviews for Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization

By Kevin J

•

Aug 1, 2020

Ich hätte mir gewünscht, dass Hyperparameter Tuning tiefer behandelt worden wäre.

Anstelle eines randomisierten Ausprobierens hätte ich mir mehr Erfahrungswerte gewünscht, wie man situationsabhängig Netze konstruiert und Parameter wählen sollte.

By Andrew W

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Nov 2, 2019

The material is very well and intuitively explained. I am disappointed with the assignment. It seems to be based on older versions of Tensorflow, and seems a bit outdated. This becomes very clear if one tries to run the assignment locally.

By Patrick P

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Sep 21, 2017

The course notes don't lend themselves for use as reference materials. The programming exercises are spoon-fed. The material is more up-to-date than Andrew Ng's Machine Learning course, but that set a higher standard for online education.

By John D G

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May 23, 2018

the lectures in this course seemed very packed and rushed, squeezing in a lot of content that felt skipped over instead of delving into the math a bit. The jupyter notebooks also have alot of errata that haven't been updated in a while

By Brieuc D

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Jan 31, 2024

This course does not go as deep as the previous one in the specialization and the transition to TF lacks some explanation (got stuck for a while during programming assignment because TF seems to lay out labels in rows vs cols in numpy)

By Maggie Z

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Jan 7, 2022

I find the course material not very well organized especially in week 1, as there are lots of random tactics taught which don't seem to fall into a common theme. For example, weight initialization seems better belong to week 2.

By Vahid N

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Jul 2, 2019

The exercise although long was only related to the last section. There are some mistakes already reported by the students but no action yet. This is a good course do not ruin the reputation by some minor unaddressed issues.

By sean j

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Dec 23, 2019

It's a good lecture for background but the programming assignment is outdated. Tensorflow 1 is very uncomfortable and the assignment would have been a lot easier and intuitive if it was Tensorflow 2, Keras or PyTorch.

By Deeplaxmi

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Apr 1, 2020

Thankyou for your great guidance sir. I am diploma student where we ain't taught much maths related to ML. I found difficult to understand mathematical equations. So i request you to upload a course on that too.

By Imad M

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Nov 4, 2018

Week 1 and week 2 needs more examples of python programming in the videos. The videos for week 3 were a lot more interesting. Without the python implementation examples in the videos, the course can be very dry.

By Nikolay B

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Dec 5, 2017

Lessons are nicely explained

Assignments should be more challenging. Same as first course, this one basically make you cope-paste instructor notes and just change variable names to pass all assignments.

By Caleb M

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Jun 4, 2019

Enjoyed learning the concepts but it all seemed slow and tedious. It also seems like building up tensorflow throughout the weeks would be more useful then just piling it in the notebook at the end.

By Christopher D

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Aug 1, 2020

It was a really good course, as I have come to expect when Andrew Ng is involved. The reason I only gave it three stars was for the sole fact that the version of Tensorflow is not up to the date.

By Riccardo F

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Sep 24, 2020

Not enough about tensorflow, not a lot of extra information on hyperparmeter tuning, exercises simple and unchallenging. I like the instructor, but I wish we could get more challenging material.

By Srini A

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Jan 9, 2019

its great foundational course but i feel with frameworks available the math behind it was little boring.Andrew NG is pretty good with explaining it well but sometimes felt it was too trivial

By Alexander V

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Feb 25, 2018

Tests are very easy, and the programming exercises are very straight-forward - to the point where it is really obvious what to do. I could have learned more if both were more challenging

By Griffin W

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Jun 29, 2019

Tensorflow was introduced in a very confusing way and most of the intuitions were not explained. Besides from lack of explanation for tensorflow, great course that complements the first

By Jorge G V

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Mar 7, 2019

The lessons are good, the programming assignment has mistakes that have apparently been reported over a year ago and have yet to be fixed - there is no excuse for this to be the case.

By Aniceto P M

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Apr 21, 2019

The course was well, but the last graded test was use Tensorflow and this requires a lot more knowledge than the last video which was an example of another completely different kind

By Peiyu H

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Oct 12, 2018

Lots of error on the final exercise. It seems some errors exist from previous sessions already. Hope the teaching team will fix the errors and make learning less confusing for us.

By Jonathan A

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Sep 10, 2020

The first course was really well put together. This one not so much. I learned a lot, but it seems that adding the TensorFlow exercise at the end of week 3 was an after thought.

By Vincent T

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May 4, 2022

Sometimes Andrew doesn't emphasize concepts or topics clearly and skips over details key for ones understanding. It becomes frustrating when you're given incomplete information.

By Ignacio L

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Mar 3, 2021

dint like that the tensor flow that we used for the lab is an old one. Specially after I did the tensorflow specialization , the old version is nothing like the newer one.

By John D

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Feb 13, 2021

The content was solid, but some of the labs seemed a bit buggy (getting full credit even though my code didn't run). I also wish the TensorFlow tutorial used TensorFlow 2.0

By Debjit G

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Jun 19, 2020

The course was amazing as expected. But the quality of videos needs improvement. Also if programming part was explained in the videos then that would be great. Thank you.