Awesome content but one big flaw: After 2 months using numpy to build neural networks (since course 1 of the specialization), briefly touches TensorFlow for around 2 hours. I feel like we should at least do everything we did with numpy (l2 regularization, drop out, 2 layer nn, deep nn, etc) once again using TensorFlown

The course explains great tips for optimizing and tuning NN, bu I miss some more practical examples where observing and compare results when applying the different techniques studied.

Also I miss a general schema of all optimization and tuning tips in order to know when and where apply each depending on conditions, etc.

Exercises are too easy, and lectures are kind of boring. The Jupyter / iPython system does not run smoothly. I ended up downloading everything on my local computer, completing the assignment there, and then pasting the code into the coursera notebook. That makes the assignments take 50% longer than necessary.

Would have rated higher, lost 2 stars because uses Tensor version 1. Keeping courses current is very important to me. Rating 3 even that though I thoroughly enjoyed this course and learned what's under the covers in packages such as tensorflow. Not sure if there is an excuse for not updating the final lab.

The content is 5 stars.

However, technicalities of assignments not getting submitted and then needing to investigate in the discussion board what others did to be able to submit an assignment..

Assignments not getting submitted&graded is a criticial bug, that's why the temporary 3 stars rating on my side.

Considering that this is a refresh from an earlier course, some more attention could have been given to correctness of formulas used; there seems to be a real disconnect between Tensorflow programming assignment and other programming assignments - the Tensorflow functions were not well introduced

Andrew is an excellent teacher, but the programming assignments are weak. Everything is already written for the learner, and the only things one needs to do is to fill few lines of code here and there. To fully understand the material, the learner should write the code by himself/herself.

I wish the course material on Tensorflow was updated to Tensorflow 2, but it is also nice to know what happens under the hood. I also wish there was some programming assignments in which we could tune some hyperparameters and visualise the difference between selecting diferent values.

While reviewing the mathematics is important, not having any formal lectures discussing the lines of code became frustrating at times. A brief overview relating the mathematics to how it should be formatting written would be beneficial (especially when introducing Tensorflow).

Good explanation of hyperparameters and optimization in DNN. As a beginner to tensor flow, I felt it hard to debug the tensor flow assignment. It would have been easier if the assignment included validation of each function before building the complete model.

Would have liked programming assignment in python to understand the various initializations and optimizations. Although tensorflow introduction was good, It felt like being left stranded without a python assignment to cement the things learnt in the class.

The lectures are good and informative. However, the programming assignments are hard to learn from - an unhelpful combination of too easy and too obscure, so that it's hard to believe I'm developing skills that will help me program such things myself.

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.

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.

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.

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

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)

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.

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.

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.

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.

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.

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.

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.

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.