The material is very interesting, but a little light on the mathematics, which I personally would enjoy seeing more of. I would like to see more elaborations and proofs, but they can be optional. A little too much hand-holding in the assignments. Learning to find relevant information is part of the training and as a programmer I find that the assignments consists of a lot of cutting and pasting snippets of code from the instructions in the notebooks.

A great follow-up to the first course in the specialization. Answers a lot of questions that might have occurred to you while taking the first course.

Compared to university courses I have taken, this one feels to me as if it is taught at about a second year level. As such, keep in mind that to fully absorb the material you will need to do more than just follow along, you will need to practice on your own by finding or creating your own problems.

The content of the course is very good and well explained, only this would be 5 stars. I gave only 4 stars because all the lessons are video lessons. Even though there is a transcript, the text is not well formatted and it does not underline core concepts, math formulas, code snippets, etc. So if you prefer a text lesson, it is hard to read, and you still rely on the video because the teacher writes many explanations on the whiteboard.

Love the orthogonalization part and the explanation on why training deep neural networks is possible (local minimum is rare in hyperspace; for the most part there are saddle points). Tho I was hoping there would be some advice on how to design a neural network. Overall I think it's a bit too easy for those who have already known deep learning or taken Ng's Machine Learning course. It'd be great if the homework would be more challenging.

Similar to my previous review in 'Neural Networks and Deep Learning', I found this course to be particularly good. It improved on the vanilla model introduced in Course 1, strengthening notions of tuning and regularization. I found it to be quite useful. My only complaint is that the assignments are similarly too much 'hand-holding' so I would advise those performing the assignments to try to develop some of the functions from scratch.

You can learn a lot of topics related to deep learning algorithm optimization. All of them are explained in great detail, but the last assignment with TensorFlow was a bit confusing because of its own structure, not the instructor's fault. Also there was not an explicit motive why they chose to explain with TensorFlow.

Besides that, the course was very nutritious and I feel more confident about deep learning after completing the course.

In comparison with the first course of Deeplearning.ai , this course was a bit shallow in some topics. I would for example divide first and third week int two parts and add little bit deeper information. On the other hand, it was still very informative. The course provide very nice probe to optimization techniques of training DNNs. I would recommend this course to everyone, who wants to expand the basic knowledge of Neural Networks.

As someone new to deep learning I found this course to be a little more abstract than the first course. I did learn a lot about the subject matter and it puts me in a good spot to dig for more information on the subject. I really enjoyed getting exposed to TensorFlow and learning about all of the other frameworks available out there to make using DL techniques much more approachable for folks without advanced degrees.

Thank you!

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).

Good course, but the standard is not up to par compared to Course 1 and the ML course. The Week 3 Tensorflow assignment has a few mistakes and some of the code seems redundant (probably because the code was updated and the old ones were not removed), which makes it a bit hard to follow. Also the code could do better with the comments for elaboration, but nothing you can't figure out yourself using online resources. Regard

Lectures are really good.

Practical assignments are not challenging at all, the answers are given in problem formulations.

The last week dedicates 20 minutes to Tensorflow and is extremely superficial. I dont understand why tensorflow is even addressed in this course, given the miserable amount of information about it that is presented in the course. I hope tensorflow will be used in later courses of the specialization

First, I love these courses from Dr. Ng. They're great. I think this one was also very good and I learned a lot when we were working in numpy. Unfortunately, I really didn't feel like the Tensorflow exercise was helpful. I got through the exercise and feel like I did little more than cut and paste. I don't know what any of those functions really do even after reading the reference documentation.

Great course, although I felt that introducing TensorFlow in week 3 was quite ambitious. I think it would have been better to have an additional week, during which we could have just learnt about TensorFlow. I found the third week challenge difficult, because I didn't know why we were using some of the functions, and it took me a while to figure it out - and I'm still not certain why what I did worked

I would love to give 5 stars but I have reduced one because of the typos in the assignments. I 'managed' to waste my time to check my code many times as my answer was not matching the expected output. Later I figured out that there was an error in the expected output.

As a course, it gives a thorough understanding of playing around with hyperparameters and fine tuning the NN to get better accuracy.

As usual, the course material, videos and programming assignments were excellent. However, as I am currently not utilizing large datasets, the material on Batch Normalization and Hyper-parameter tuning is not useful to me ( now and in the foreseeable future). The introduction to TensorFlow and the tutorial was very useful, and has certainly whetted my appetite.

Look forward to the next course.

Very good lecturer, but there have to be some ready-made slides also for those who can not take notes during lecture, because my self taking notes also, and can not carrying both listening to lecture and taking notes.

Please Andrew Ng sir!

Make some slides, with a few details for studying whenever facing problems and clearing doubts in the future. please sir

Thank You

Sincerely yours

Kamran Khan

Mentors don;t seem to collaborate, its been 3 days i posted a question in discussion forums but no reply has been received yet. It was a terrible experience. If you got stuck at a problem then only way is to get help from other students because mentors don't give a shit, about your course. I have given a rating of 4.0 only for the teacher Andrew Ng Sir. His teaching style is very impressive.

Good course that covers a lot of practical topics that are rarely given much consideration in an academic setting. Be careful in week 3 as there are a couple of mistakes that someone who has gotten to this point should catch, but it is possible that they will slip by. Overall though, another great class from deeplearning.ai where I learned a huge amount that has a very practical application.

The course content was of very high quality. There were just some issues in the notebooks that are already covered in the forums. I think it's worth fixing them. In the videos there are also some small mistakes made but nothing serious. Also, about the programming assignment, I think it would be useful to have some in which less of the code is readily written and more is left to the student.

As usual this course was really good, but at some parts I had a feeling that Professor Andrew Ng was a bit vague in explaining some concepts. So, I had to spend a lot of time on thinking about it (which I feel could have been avoided). It will really help if the instructors are able to provide additional references regarding the hyperparameters and the other topics discussed in this course.

This course did a good job of covering much of the material. I felt like the explanations of most of the concepts in the videos were good. The last programming assignment, on TensorFlow, felt like a lot of guesswork for me. The basic ideas of TensorFlow were not really covered well in the lectures. Other than this assignment, I thought the programming assignments were generally helpful

A very good, in-depth course. Only small complaint is some of the slight nuance in the final programming exercise, where some details (i.e. the from_logits parameter) being a little bit unclear. Personally, a bit more clarity on how to use that function specified, and the fact that the inputs required transposing should be highlighted more clearly. Other than that though, brilliant!!

The best thing that I get from these courses is to learn about intuitions all the time. Although I really enjoyed that part on optimization and parameter tuning , the same standard wasn't kept in TF part. What is tf graph? why do we need it? why session? .... Unfortunately the tf docs fail on explaining these concepts as well. If I don't get answer to those questions here, then where?

I am slow on the uptake on the maths side of the equation, while the repetition of the class lectures is most appreciated. No, it is not repetitive, Andrew keeps expanding on our prior knowledge for each week.

Even with 30 plus years since I did Calculus I am able to follow and understand thanks to the team.

Though, they do need help with correcting some minor mistakes in the webpages.

i learnt a lot about tuning Neural Networks through various optimization and regularization methods in this course. this helped me a lot in understanding the working and derivatives of optimizing neural networks through various algorithms. this course is making the foundations of deep learning look easy and understandable than other sources to the person who is taking up this course.