4* only because the TensorFlow lectures and assignment were too much in too little time. Also from what I see, TF has massively changed syntax to 2.0 so it felt a bit pointless to learn TF1 syntax (which is ***horrible***) at this point. To me it detracted a lot from the learning experience.

The remaining lectures and modules were excellent as usual though. I'd still recommend this highly, and Andrew's insights into what tends to work and why are brilliant as always.

Another great course, the amount of information per week is right on point (not too packed and not too poor). Also, it was interesting to go behind the scenes and learn what batch normalization and regularization actually does and how it can actually help a neural network perform better. And, to top it off, it gives a brief introduction on TensorFlow and how to use it, although it would have been better if the course thought the material on TF 2.0 instead of 1.0.

The course is great!

It really helps in understanding how the algorithms work, under the hood and the implementation tips

are very helpful! (This is visible in both the Optimization and Batch Normalization algorithms sections)

It is awesome that a programming framework is also introduced in the course, Tensorflow. But to be honest PyTorch could be also introduced, in order to select the framework in which the student could implement the last programming assignment.

The content covers a wide variety of useful topics in deep learning. Andrew's explanations are sufficient, as are his use of both examples and analogies. I was only slightly disappointed to see that he has left the derivation of the equations governing back propagation along a batch normalized neural network as an exercise to his audience. The quizzes were sufficiently challenging, and the programming exercises were either informative or insightful, or both.

I felt this interesting but bit easier compared to the first course. Please don't provide the parameters of a method directly in the description above. I want to learn how this method can be executed by thinking and understanding the parameters I have to pass to this method. So, I felt the tensorflow assignment little straight forward. But overall a very good course. I need to practice a lot to actually understand and write the code from scratch. Thank you.

I wish to give 4.5 instead. The only pitfall is the whole video series have a high frequent sound which keep distracting me when try to concentrate to the content.

content vise is very good. Coding practice is very helpful in understand the process. However there is only a basic level. With giving too much help on the background, it's very easy forget afterwards. May be a suggestion more practice would be better.

After all, it's the best course in DL for me.

The number of mathematical symbols grows quickly, and I started getting a little lost trying to remember which Latin or Greek symbol meant what and in which context. Still, I think that I've learned enough about overfitting (bias), underfitting (variance), regularization, adaptive learning rates, and normalization that I'll at least have the concepts in mind moving forward, even if I didn't memorize the equations and code necessary to do it from scratch.

This was a good course to deal with some of the inner working of the machine learning and neural network models. It was good to see one of the existing frameworks (TensorFlow); although, I find it to be more difficult to configure than Torch (PyTorch). And it was unclear from the lab whether or not this framework was using GPU acceleration; although, this could probably be determined with a little research and experimentation in the lab environment.

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.

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