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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

40,184 ratings
4,276 reviews

About the Course

This course will teach you the "magic" of getting deep learning to work well. Rather than the deep learning process being a black box, you will understand what drives performance, and be able to more systematically get good results. You will also learn TensorFlow. After 3 weeks, you will: - Understand industry best-practices for building deep learning applications. - Be able to effectively use the common neural network "tricks", including initialization, L2 and dropout regularization, Batch normalization, gradient checking, - Be able to implement and apply a variety of optimization algorithms, such as mini-batch gradient descent, Momentum, RMSprop and Adam, and check for their convergence. - Understand new best-practices for the deep learning era of how to set up train/dev/test sets and analyze bias/variance - Be able to implement a neural network in TensorFlow. This is the second course of the Deep Learning Specialization....

Top reviews


Oct 31, 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.


Dec 24, 2017

Exceptional Course, the Hyper parameters explanations are excellent every tip and advice provided help me so much to build better models, I also really liked the introduction of Tensor Flow\n\nThanks.

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4076 - 4100 of 4,206 Reviews for Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization

By Ralf S

Aug 28, 2019

Good course overall. but labs could be expanded. Don't know if the Coursera platform supports it, but labs between lectures about different topics would be nice instead of having all practical exercises at the end.

By Mor k

Aug 30, 2019


By Eamonn G

Sep 04, 2019

Overall good class.

By Lenny F

Sep 29, 2019

Would like to have more practice

By Steve I

Sep 27, 2019

This is a great overview for those wanting their neural networks to run more effectively and efficiently. Lots of ideas to improve your networks. The documentation and description of Tensorflow for the exercises is inadequate to be able to diagnose errors in the "expected" code without expert assistance. When debugging Tensorflow for these exercises, its almost a Trial and Error exercise instead of using first principles taught in the presentations.

By Aurangazeeb A K

Sep 30, 2019

Although I loved this course, I believe there are certain parts that could be broken down into even simpler intuitions. If such a change a possible, this course will be the best one out there. Anyway, I really enjoyed the course and it was a great learning experience. Tensorflow was introduced very finely and it aroused my curiousity to learn more.

By David R

Oct 01, 2019


Overall the courses in the specialization are great and provide great introduction to these topics, as well as practical experience. Many topics are explained clearly, with valuable field practitioners insight, and you are given quizzes and code-exercises that help deepen the understanding of how to implement the concepts in the videos. I would recommend to take them after the initial Andrew Ng ML course by Stanford, unless you have prior background in this topic.

There are a few shortbacks:

1 - the video editing is poor and sloppy. Its not too bad, but it’s sometimes can be a bit annoying.

2 - most of the exercises are too easy, and are almost copy-paste. I need to go over them and create variations of them in-order to strengthen my practical skills. Some exercises are quite challenging though (especially in course 4 and 5), and I need to go over them just to really nail them down, as things scale up quickly. Course 3 has no exercises as its more theoretical. Some exercises have bugs - so make sure to look at the discussion board for tips (the final exercise has a huge bug that was super annoying).

3 - there are no summary readings - you have to (re)watch the videos in order to check something, which is annoying. This is partially solved because the exercises themselves usually hold a lot of (textual) summary, with equations.

4 - the 3rd course was a bit less interesting in my opinion, but I did learn some stuff from it. So in the end it’s worth it.

5 - Slide graphics and Andrew handwriting could be improved.

6 - the online Coursera Jupyter notebook environment was a bit slow, and sometimes get stuck.

Again overall - highly recommended

By Emmanuel T

Oct 03, 2019

Compared to previous module, this one was more of a cookbook and I expected more mathematics in terms of why each optimization work.

Overall, it was still a very interesting hands on approach, finishing with TensorFlow is a bit more difficult to apprehend as all the previous exercices were done in a very different way (Numpy).

By Aditya S

Oct 05, 2019

Good course. However expected some more mathematical proofs for some of the ideas like bias correction and exponential weighted averages.

By Nataliia K

Oct 28, 2019

Quite ok, but programming assignment was mostly copy-paste style. I am not able to repeat something similar independently after the course

By yesid a c m

Oct 28, 2019

hay funciones de tensorflow que ser[ia adecuado que las explicaran en los notebooks.

By qiaohong

Oct 28, 2019


By Sajal J

Oct 28, 2019

Very good course.highly recommended

By Andrew L P

Oct 29, 2019

Wonderful course, would have liked another assignment working with TensorFlow.

By Alexander K

Oct 12, 2019

Too less coding and practice exercises, thou the theory is great

By Nono

Oct 31, 2019

Thank you,Andrew

By Michael R

Nov 02, 2019

Tensor flow should be explained in more detail

By Ramesh K

Nov 04, 2019

I have taken Machine Learning courses earlier from Andrew Ng via Coursera. I have always felt that the delivery of the material and the pedagogy are superb and have always rated a 5 star as also for the first course in this specialization. This second course had several interesting topics I had never learned in my previous NN courses at universities. The programming exercises for weeks 1 and 2 were excellent in helping recap the material in the videos and slides. However as far as TensorFlow is concerned, I was a bit disappointed because it seemed like we were muddling through the various code snippets rather than getting a firm grasp of what is obviously a very complex Deep Learning programming framework. But I understand the time limitations and I realize that this intro to TensorFlow is merely to whet one's appetite and encourage us to explore more about this framework as well as other frameworks. I believe it is up to each individual to explore the concepts further and get a better understanding.

The technology behind the courses is awesome as well as the programming assignment notebooks which were well documented and must have taken gargantuan amount of time and effort in prepping.

In summary, I learned a lot from this course and while my course objectives were not fulfilled, almost all of them were.

By Joao N

Nov 05, 2019

One again the course is a great follow up from the previous one. The only little detail I wish had been done was for the assignment to cover a scenario where we had to improve some hyperparameters by applying different approaches covered in class.

By Gilad F

Nov 03, 2019

I'd make the tesnsorflow section a separate week with much more elaboration, the first time (in both course 1 and course 2) I felt a subject was lacking information. It's mostly noticeable in the programming assignment.

By Fabio S

Nov 04, 2019

Suggestion of references, as a complement, would be very interesting.

By Marcos C D

Nov 03, 2019

Content needs update to leverage the state of the art in the subject.

By Jörg N

Oct 19, 2019

I liked the course a lot and I really adore the way Andrew Ng teaches the subject. As an improvement suggestion I would extend the course to four weeks to deepen the practice on Hyperparameter tuning as well as the introduction to Tensorflow. The Programming exercises of week 3 were really challenging. First since there were partially misleading statements in the comments (Z before activation) and second because variables were given the same names as tf parameters and partially even function definitions. So you could see things like a = a, b = b in tf function calls which just does not fit for beginners in portentously both Python (local variables concepts, etc.) and TF. I am more than grateful though that I could do this course of the specialisation and I would really like to express my deep gratitude to Andrew Ng.

By Ansgar G

Oct 17, 2019

Andrew Ng is great again. Also the assignments are good with very good explanations for each step in the notebooks. The TensorFlow programming assignment at the end could have gone a bit deeper, with more explanations for things that are used in the end like eval. And it had an error as the third parameter of tf.one_hot is not (anymore?) the shape. You have to explicitly pass it as tf.one_hot(indices, depth, shape=shape).

By Mihaly K

Nov 06, 2019

Assignments sometimes too easy, minimal input needed.