Wouldn't recommend because of the very low quality of the assignments, but I don't regret taking them because the content is great. Seriously the quality of deeplearning.ai courses is the lowest I have ever seen! Glitches in videos, wrong assignments (both notebooks and MCQs), and no valuable discussions on the forums. Too bad Prof Ng couldn't get a competent team to curate his content for him.

The theoretical part was clearly understandable but the programming assignment was very poor in my opinion.

Did I miss the introduction to tensorflow somewhere? I could not find it in the cousre. It was possible to solve the assignments with guessing and reading some forum posts. But honestly I did not understand very much...

I'm a bit disappointed about the introduction to tensorflow.

The course is good. It covers important ideas, and they are well explained in the videos. However, the formulation of the assignments is sloppy. There are mistakes and inconsistencies, in some cases necessary explanations are missing, and in some cases the instructions are misleading (I suspect the assignment has changed over time, but the instructions have not been consistently updated).

A much sloppier and poorer course than previously. Grading mishaps (on the fault of the grader), a few errors in the lectures (the variance in the normalization), and very basic and unhelpful feedback from staff made for a course that did not live up to the level of the previous one. If at any point you need further help, it is generally unavailable, or difficult to find at best.

I really like the course material, topics discussed, and neural networks in general. I also have a lot of respect and gratitude toward Andrew, but the way he organized quizzes and programming assignments are rather a monkey-see-monkey-do strategy. You rarely get challenged. Overall the course is worth taking, but I would not recommend this to more advanced practitioners.

I have written this before in the discussions. I feel, there should be assignments to implement everything from scratch. I feel, i can fill in the code, and understand ,most of the mathematical functions, and reasoning, but i am still not confident that i can "CODE" a new problem from scratch. I was really hoping this certification will give me practice to achieve this.

Some exercises were wrong , like Z3 em tensorflow model, you said z3=w*z2+b3, but it was A2 ,not Z2.

Several exercises did not check the result for each function, so when I arrived at the huge model function, it was hard to discover where I was wrong.

I think this third week could be two. I missed exercise with normalization, there were all with tensorflow.

A bit less good than the previous course. It would have been good to have a chance to concretely implement Batch normalization. Then I think the tutorial on tensorflow needs more details and explanations of the what and why of the conventions. Anyway I was really happy to learn a bit about tensorflow, I hope I will use it more through the course.

this course provided me with very fair insight, however, i felt that the Tensorflow portion was covered ina hurry. I had no background of tensor flow, and I am believing that the way it is covered might be the right way and I will build up on it. Even while covering the last assignment i had not much familiarity with the syntax of tensorlfow....

The fact that the lectures are not available to keep is problematic. Also, the programming assignments leave too little to do. Only few lines of code, that in most cases are simply copied from the problem description. It would make sense to broaden the programming tasks, and let the students really cope with many of the real-world challenges.

The last programming assignment in the course is a bit better than the rest, while lectures are of rather high quality. In Quizes some questions are confusing. E. g. Andrew Ng several times said that parameters should be revised from time to time, but there is a question that (in couple with correct answer) states the opposite:(

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.