I wish there were more graded projects.

No practical exercises that one must do

A bit too easy and no real assignments

I wish there were graded assignments.

Coding assignments are much needed!

Assignments need some improvment.

not enough programming exercises

Concepts explained too quiclky

Code should be explained more

Not challenging enough....

No programming assignments

Force me to write code.

Need more practice

Good course

too easy

good

N​ormally the courses of this specialization are well-structured and, although not very demanding, quite complete and self-contained, but in this case the content covered didn't go deep enough and there was very little insight provided into the principle of RNNs and specifically LTSMs, other than pointing to other lectures.

A​lso, while sentiment recognition seemed to be an interesting and promising field, the results of all attempts at text generation were so laughable that it made me wonder as to why was half the course devoted to it instead of some other application area of NLP

Very disappointed in this course. Instructor seems to have limited understanding of how sequence models and word embeddings work, or is unable to communicate the ideas in his teaching. Explanation for the theory is limited, and he has difficulty tying theory to the TensorFlow framework. Not sure why you would begin teaching sequence models with LSTM blocks combined with standard NN, way too complex structure. Instructor doesn't talk about why sequence models are important and useful in the first place. Very very poor.

Instead of taking this course, I could've read the tutorials on the TensorFlow site. Those tutorials are regularly updated, maintained, much more detailed and they're FREE.

This course, along the other courses in this specialization are not good for other than exposition to the TF API. Actually, they're not even good at that because the TF tutorials do a much better job at that.

And it's so frustrating that over-fitting is never tackled in any way and not even a hint at how to solve it is even given.

This was by far the hardest course on the sequence. I actually skip it and did courses on order 1, 2, 4 and now 3.

* Notebooks were not as easy to follow. Maybe put more comments on what was expected and describe the datasets a little more.

* There are typos here and there, for instance "The pervious video referred to a colab environment you can practice one. "-> previous.

The file at https://github.com/tensorflow/datasets/blob/master/docs/datasets.md NOT FOUND

Compared to the first two courses of the IBM specialization, this one is made really bad.

They are rushing through the theory. The programming excercises are only ungraded and not very intuitive to solve. You will almost certainly look at the solutions before getting them to run. If you have a look at the forums of the course, there is not much help to find; it looks as if most people cancel the course before they finish.

None of the labs/coding exercises at the end worked and there were numerous other broken links throughout the course. I felt like my skills actually improved in the past two courses, but in this course I just felt like I increased my knowledge. Not a bad thing, but not what I'm looking for.

If all of the courses for the Tensorflow certification were like this, I'd tell folks to avoid the entire program.

The course is merely a walk-through some Jupiter notebooks of Laurence. There are no proper slides with explanation of what's going on. I also don't see much activity from the course creators on the discussion forums. It is incredibly easy to complete the course without forming any deep understanding.

The weekly programming exercises are not even automatically checked for accuracy.

I am taking these courses to learn via example. (this is not theory course, it is a course on practice). The fact that there are not well thought out programming exercises makes this course much weaker than the proceeding two. The first two courses in this series are much better for this reason. This course looks unfinished. The lectures are okay, the quizzes are okay.

Very limited practice examples for learners. Also the example are very simple. The course should have been made much detailed and much real example problems. For instance, in the Week 4, topic 'Text Generation', generating a Shakespeare poem seemed to be a very silly example. The quality of Coursera Courses are becoming very poor.