Comentários e feedback de alunos de Sequence Models da instituição deeplearning.ai

Very good course

It was tough :)

Very usefull!!!

easy to learn

A bit cryptic

Good material

very useful

a bit hard!

Very Useful

productive

Thank you.

Excellent

Average

good

nice

good

Good

Good

Good

good

g

o

o

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in comparison to the previous courses from this sequence, this one is even less structured - ptobably this is because even broader knowledge is tried to be shown in only 3 weeks, but i feel like a lot is skipped between videos (which are ok) and the tasks - in many assignment tasks in this course it is not very well explained what is meant to be done - i mean this especially in case of Keras objects. In many cases it is quite unclear how those classes are supposed to be handled in the context of our task. There are some hints but those are mostly links to documentation (btw, some of the links are no longer up to date), but it is often not too well explained which properties those objects have, what one can do etc. so one ends up with trying using those objects in different configuarations, then googling around, looking on the course forum for the right answer but it is very difficult to derive it. There should be more precise instructions regarding handling Keras objects - the examples in the documentation and in blogs are often much simpler than those from assignments so one ends up not knowing what is going on. In summary - there is a big jump and a big gap between the intuitions in videos (which btw are much more fuzzy than those in first cources in the specialization, the intuitions get more and more superfluous as one doesnt go into detail) and what is being done in the assignments. One thing i really liked about hte previous assignments was that when writing the code one could really know very well what is going on. And this is no longer the case in this course...

As we head to the last course in the specialization (and the last two courses are the ones that interested me), we have error after error in the assignments, including problems with the kernel that are not obvious until you've struggled with incoherent stack trace output for a while.

Searching the disorganised discussion centre for the course/week in question you can find that these errors affect everyone and go back for a couple of years, never having been fixed. The mentors there help explain, but mentors cannot edit to fix the code as they do not have permission, and the course supervisors have long since disappeared. So you have to submit incorrect code to pass, then fix the code for your personal private code store - as the fixed code generates the correct numerical answers that unfortunately do not match the numerical answers that the grader requires to pass you!

It feels like, in the hurry to get the full specialization out, the final courses go downhill in terms of care & attention in the rush. Then afterwards, all of the errors and badly designed code in the assignments cause many unexpected headaches, nothing to do with DL, and were never fixed or maintained afterwards by the course supervisors.

In the end, the delays caused to me in the final (two) course(s) added at least one extra monthly payment on to my subscription. Overall I can't complain, the specialization is good. But feels abandoned by the lecturer & assistant lecturers since early 2018

It's helpful to have this course since there aren't enough beginner-oriented courses on these topics, especially ones that also get into actual equations like he does. However I think he doesn't provide enough explanations of complicated topics like GRU's and LSTM's. There are lots of confusing aspects of both such technologies, and he could afford to spend even more time in explanation than he does.

EDIT: I am now on week 2. This course feels rushed and he doesn't take the time to clarify confusing issues - for example, when he first introduces how to learn word embeddings he calls the neural network you use a "language model" even though the network bears no resemblance to the language model we learned in week 1. This really confused me and he doesn't address this point. Also, he variously describes the embeddings as the "input" and the "parameters" of this neural network, even though those are clearly two different things. There are more issues where that came from.

Unlike all of his previous courses, I've found myself needing to go to Wikipedia and Google to try to fill in various holes in the presentations here.

Also, there is essentially no help on the forums. That isn't the reason for my low rating, since for a cheap course I didn't expect much. Still, it would have been nice if they had tried to do a little bit more there.