A good mix of theory and practice. The learning curve was perfect for me, and the course schedule is right if you study the material and work through the assignments in your spare time. Assignments are very well structured, I feel that trying to create the same implementations by myself (i.e. without the guides in the assignments and intermediate tests / check) would have taken 10x long.

This is a good review of the concepts. It helped even more once I finished the course and reflected on the material by working out the equations for back propagation by my own hand. Looking forward to the next course in the series.

The course is good and it helps to clear the basic concepts of Neural Networks,

And the interactive assignments are just Awesome

The course is quite slow, but covers the basics of early deep neural networks (NNs). It does seems not to assume any prior knowledge on calculus, which is emphasised extensively, which sometimes leads to more confusion than that it is helpful. Before starting, some knowledge on python, numpy and linear algebra is highly recommended.

In the end you will have a basic understanding of what a NN is all about, and you will have built a photo-classifier. The course however, spends a lot of time explaining simpler concepts, while quickly glossing over the deeper stuff. Because of the elaborate explanation of simpler concepts, the big picture often gets lost. Furthermore, it seems like the videos, quizzes, and programming exercises were made by different people. The quizzes cover things not covered in the videos, and the programming assignments cover things not covered in either.

This course is good to just learn the terms and the basic aspects on architecture of deep learning. There is hardly any big explanations on the mathematical foundations of the topic which are of extreme importance to understand it.

It is a course for someone that dos not know much about neural networks or mathematics.

Is unfortunate that lead researcher in the area is able to say that it is not necesary to understand what a derivative is to be able to understand deep learning and the algorithm to update the weights of the network. I guess only for a first time course that is true, but I was expecting more from this course.

Programming assignment is too simple

Very basic level

The course is well structured and the explanation is linear and mostly clear, but:

1- in 2020 I expect that in doing such a course are going to be applied relatively modern teaching standards, like for example avoiding handwritten text. What is the purpose of writing on the screen if you can use animations to more clearly connect concepts during your lessons?

2- I don't expect that errors to be just rectified before the video. Reupload the video? Errors like that during long formulas and explanations are just going to kill the learning. It is pointless to write before the video that in the future video you will make an error. Just correct it ON the video.

3- If you can't explain in-depth calculus, just to di with the help of someone else. You cannot exclude calculus.

4- The only thing i've learned in this course is vectorization (thank you). The rest is just copy the formula given during the explanation (handwritten on the screen.....) and paste during the exam. I didn't learn how to apply a neural network because during the "exams" it was built already. I expected assignments to make me build an create every piece of the network, instead it was all already done and all i had to do was repeat what Andrew says in the video. This is NOT learning. You need an assignment per video for that kind of thing, you can't just go forward and write some formulas on the screen pretending you have "explained it" because nothing seems explained to me. Why should i use those methods or formulas instead of others? Nothing is explained.

Not enough explanation or support to complete the very vaguely worded assignments in anything like the specified timescales.

I respect the source of this course but as a teaching resource it is really very poor.

Course teaches bad programming practices, such as naming variables dZ and b. Also it is little outdated - neural networks are not written in numpy anymore.

This is a very good course for people who want to get started with neural networks. Andrew did a great job explaining the math behind the scenes. Assignments are well-designed too. Highly recommended.

This course helps me to understand the basic concept of Deep Learning. However I think this course should include at least 1 week (or 2-3 videos) about math so learners can have a better understanding

Excellent course !!!

The flow is perfect and is very easy to understand and follow the course

I loved the simplicity with which Andrew explained the concepts. Great contribution to the community

The course does not have the same quality as the “Machine Learning” course Andrew Ng made with Stanford.


The biggest issue are the programming exercises, that do not require the learner to think at all. Most tasks in them are on the level of “copy and paste this piece of code”, “retrieve a value from a python dictionary” or “use a mathematical formula displayed directly above”. I appreciate the effort to make the course more inclusive to people with a weaker background in Computer Science. It would however make the course much more worthwhile to have challenging exercises with optional hints, instead of giving the solution away in each task description.

“Neural Networks and Deep Learning” hardly teaches anything, that wasn’t already covered my “Machine learning”. The major differences is that it uses Python instead of Octave and arranges features as rows instead of columns. In my eyes, the learners time is better spent, skipping the first course of the Deep Learning specialization entirely and taking the Machine Learning Course instead. To the creators / maintainers of the course I would advise creating a summary, that covers the most fundamental differences between the two courses (different notation, numpy fundamentals) and make a suggestion where someone who has taken Machine Learning should join the Deep Learning specialization.

While the audio quality has improved, the video editing is poor. There are multiple occasions where misspoken content, that was clearly meant to be edited out, remained part of the video. Many videos are preceded by a “Clarification” reading task that corrects some mistake in the video. How hard is it to get an intern to fix this in post?

The programming assignments provided a good framework in order to practice coding the main functions in a neural network. This was helpful to understand the matrix operations underlying the forward and backward processing in a general L layer network. Without a previous background in linear algebra and in neural networks however this course would be challenging and maybe very frustrating due to the limited debug information available.

The course videos need to be a lot more focused on the details being conveyed. The verbal and visual discussion and explanation provided is in my opinion not effective. The slides are cluttered and contain many errors, the verbal portion is like a casual conversation that repeats quite a bit, and the script provided for those that get tired of the repetition contains many transcription errors. I would recommend that someone be paid to correct the scripts to help those that prefer this way of working through the course material.

Very abstract, and the examples are not as concrete as they could be. I'd use better visuals to ensure that the concepts in each video are understood 100% visually.

Really just mathematical background knowledge. Nothing you would ever need, since there is keras. No own thinking during assignments neccessary, either.

Easy course if you have coursed the ML course before. I would like to see more explanations in detail. Still some bugs in the assignments... why???

Course starts with a lot of math without any context what all those computations and parameters are used for or what they have to do with N

The assignments are not good. Too easy and too much handholding. Also lots of technical issues.

Few Detail. Mathematics missing.

This course is really good but assignment given to solve is not understandable.

Great course, definitely taught me the basics of Neural Networks and Deep Learning as it's supposed to. Assignments are quite engaging when you try to thoroughly solve them. Even with minimal mathematics, the course will handhold you the whole way. Definitely a great course for anyone with minimal programming to get into. For me, the most challenging part was understanding how Python syntax worked with numpy. If you are taking this course I recommend taking your time with implementing the projects, they can definitely give you an understanding behind the logic of neural networks by following the code. The instructor is quite nice and warm, sometimes a bit dry, but nonetheless, he seems very warm; wanting to teach the next generation of individuals to do ML/AI. The course does have a few downsides such as how buggy the iPython notebook can be. This is the programming environment you will be using. An the video quality isn't always the best with the audio, but overall the content was presented in a great way and prepared in a manner in which you learn one step at a time.

The content was very good and intellectually curated, and no complaints about a teacher of such high quality "Andrew Ng". Actually, I took the "Machine Learning" Course long before on Coursera from the same instructor, as I took this course now, which highly helped me to finish this in less than a week, although I never got time to complete the former course. Advice to any new students on this course would be to have a basic understanding of Machine Learning, which includes linear regression, vectorization et.al. , (or simply, "ML" course on Coursera).

One small amendment on this course could be to reshuffle the contents a little, from different weeks as I found the content which was in Week 4, to have high importance to be taught earlier in this course (for eg, getting matrix dimension right ), and there were others sub-topics in week 3 as well. I don't remember all of them, as I took 4 weeks worth of information, in just a single week :)

Very excellently taught, and contents, as well as assignments, were of topmost quality.

One might dislike that the derivation of formulas is not talked about in this course, but I think it's the right decision for this course. I took the Coursera Math for Machine Learning Specialization before taking this course, and the derivation for the formulas took at least 4 weeks of background material about linear algebra and multivariable calculus. Thus, this course aims to give you a conceptual understanding of neural networks that will allow you to implement it on your own. While some might argue that the programming assignments are too easy, or that too many hints are given, I think they're necessary for guiding you in the correct direction during the assignments. If you take the time to read the prewritten code, you will be able to get the understanding you get from writing it fully from scratch and possibly taking hours to debug and to read NumPy documentation. Overall, a very solid course for those who want to build a neural network on their own.