Very Easy Course. A basic course marked as intermediate

It´s well explained but way too basic and short.

这课挺水的， 不如 deep learning specialization多矣。

notebooks need work from the instructors

Course was not rigorous enough

Because the LAB is so lagging

Too much superficial.

Too basic.

meh

This course is FULL of errors (both in code and math), inconsistencies and wrong explanations. I tried to document them, but I just gave up, because it is so many of them... For example the math which explains multiclass classification (Week 2 Video “Coding a Computer Vision Neural Network”) is wrong – the output of multiclass classification should be pseudoprobabilities, not numbers ranging 1 to 9… There are also unsolved problems reported in GitHub (https://github.com/lmoroney/dlaicourse). It seems like they really don’t care about correctness, completeness and quality of this course… If you want to learn TensorFlow I highly discourage to use this course - you will just learn wrong things and would have to unlearn them later... Also graded exercises are in TensorFlow 1.x and materials are prepared for TensorFlow 2.x which means that sometimes the code from materials does not work in graded exec, eg. logs.get('accuracy') does not work in tf 1.x and you need to use logs.get('acc'). I did this course only to get some practice and pass TensorFlow exam, because I'm academic who works with PyTorch.

First of all, it's an introduction to Keras and not Tensorflow.

Secondly, the explanations the author gives are lacking/misleading.

For example,in week one the net didn't learn exactly the hypothesis 2x-1 for other reasons than the ones he mentioned (oh,and the net did not give some kind of a probabilistic interpretation to the data...).

I am not sure why Andrew NG (who is the best instructor in the world to my mind) allowed this kind of instructor to be branded as deeplearning.ai.

This course claims to be over 28 hours, however, I was able to finish it (watching every video, reading every bit of text, doing every exercise) in less than 6 hours. The lab work is a complete copy-and-paste of the examples covered in the course. If you want to "buy a credential" take this course, but if you want to actually learn anything, take another course.

This is a poor course. The course assignments often fail for mysterious reasons (Grader timed out, Grader ran out of memory), and the course points to outside resources more than its own explanation.

I found the explanation deeply lacking. I would suggest not taking this course, and I will not be taking the remaining courses in this specialization.

the instructor did not give us enough explanation for the code written, it is just reading it. many things he added in his code does not make sense for the beginners like me. in addition that he said that it is out of the scope of the course. so why did you add it in your code. i feel the instructor should explain things more than that

This course does not go very much into detail and way too much time is given for easy exercises and homework. The homework contains a lot of bugs, which need to be fixed since students waste a lot of time debugging errors which are not due to their fault. Furthermore, the homework is just about copying the code given in the videos.

The videos don't contain much usefull information, but only a demonstration of the most basic concepts of tensorflow. Practical assignments does not aim at teaching you any skills, but copying code from one notebook to another. And after this course I actually have some concerns about the author qualification on the subject.

There's barely enough material to qualify it as a course. Each week's videos combined are less than 30 minutes. They just link you to more resources such as Andrew Ng's Deep Learning course material. It took me less than 3 hours to finish this course. There's nothing of substance in this course. A disappointment.

Vague, you should really have prior knowledge of deep learning, this specialization won't teach you anything in detail. The specialization focuses on just a few examples of not TensorFlow, but of Keras. You will have experience in solving almost already solved, arbitrary problems.

Unexpectedly from coursera and sadly, there are too many abandoned courses and these courses are a few of them. Outdated datasets, outdated codes, students trying to help each other but many people don't understand what's happening here and there are no mentors to help along.

Course material is outdated. There is many mismatches between the videos and the notebooks. Material is not updated for smooth progress. Many sections are talking about examples and no links for them. It seems that no one reviewed the course content since it was launched.

Extremely bad experience with the coding exercises, lots of things broke in the notebooks. Just take a look at the discussion forums. It seems the creator, the Google advocate people took no effort in making sure things works.

The quality of teaching is low compared to other deeplearning.ai courses. Compounded with the vague instructions in notebooks and improper grading algorithm , it make for a very painful learning experience

The entire series is rudimentary and not worth the money. Its basically a high-level keras tutorial. I would recommend taking the series that is taught by Andrew Ng if you want to understand deep learning.

In fact, for me, it was an introduction for using Tensorflow for computer vision. It is not an introduction to tensorflow. There is any introduction for the concept of graphs, tensors...

Numerous problems with this course. Lots of unexplained details and deprecated examples. Quizzes reward instance data ,memorization, rather than testing concepts.