学生对 deeplearning.ai 提供的 Natural Language Processing with Sequence Models 的评价和反馈

This course is good for practical knowledge with really good projects but it lags in the theoretical part you must be familiar with the concepts to get the most out of this course.

I am now confusing by too many Deep learning framework. Also the content is somehow repeated with the Deep learning specialization.

I just want them to use Keras, since I have no inclination to study new shiny fancy framework such as Trax.

Lectures are quite good, but assignments are really bad. Not helpful at all

assignments were easy and similar.learned less than expected.

Content is interesting, but some details are under explained.

Would've been better with a better documented library.

I prefer to learn more about nlp in pytorch

Grader output could be more useful.

Very superficial information about different types of neural networks and their uses. Use of Trax makes it nearly impossible to Google anything helpful - a lot of the assignments just tell you to read the documentation. To finish the assignments, you can basically copy-paste the code you're given to set up Trax neural networks and generators without having any idea what you're doing (because, again, the content doesn't go over it).

For example, Week 1 in this course introduces you to Trax (can't be run on Windows), covers aspects of object-oriented programming, and talks at a very high level about how to do things in Trax before moving onto a cursory discussion of generators. Then the assignment has you increment counters, set limits in FOR loops, copy negative-sentiment code from positive-sentiment code that's already completed, and fill in some code that's basically given right above where you write it.

Overall, any code you write is usually very simple but often easy to get wrong because of the lack of direction, e.g. model(x,y) vs model((x,y)). The discussion boards are invaluable because the mistake might have been 3 functions earlier that the built-in tests didn't catch. It feels like a good effort was put in all around to establish this course, but it feels like a first draft course that was never updated.

Sadly, the quality of the material is much lower than the previous two courses. Assignment repeatedly asks to implement data generators with a lot of for-loops. We should focus more on the network architecture rather than python programming. That being said, the implementation is not good either. Learners would have to learn to program anyways.

Assignments are very difficult to complete because of inaccurate information (off by one on indices and other sloppy mistakes). You also don't learn much from them because almost all the code is already provided. It would be much better if they built up your understand from first principles instead of rushing through fill in the blank problems.

The course did well in explaining the concepts of RNNs... but it may in fact have provided less knowledge than the NLP course in Deep Learning specialization.

I was looking forward to see more details on how translation works using LSTMs, go over some famous LSTM networks such as GNMT, and explain some accuracy measures such as the BLEU score.

Spends a lot of time going over tedious implementation details rather than teaching interesting NLP topics and nuances, especially in the assignments. Introduction to Trax seems to be the only saving grace, one bonus star :)))).

For having Andrew Ng's course as suggested background for this course this is a big step (read as fall) down.

Course could be completed without watching videos - just by using hints and comments in assignments, videos are short and shallow, choice of Deep Learning framework (TRAX) is questionable - I won't use it in production.

Despite the course is 4 weeks long it could be accomplished in 4 days - I don't feel that it was worth the time.

The course is good as an example of code that executes tasks correctly; that is, you can see how neural networks are defined and used in Trax. However, from a pedagogical point of view, I find it quite weak. Concepts are poorly explained and notebooks consist of little more than copying and pasting previously displayed code.

Compared with the Deep Learning specialization, this specialization was designed in a way that nobody can understand. Although the assignment could be easy at times, the point is being missed when people cannot really understand and learn. Bad teacher. Andrew Ng, please!

All the assignments are extremely similar.

I did not enjoy the course at all. It looks like the instructor is just reading materials rather than really teaching. He just focused on reading and did not explain anything. I took Andrew's deep learning specialization, and that course was really great. But I am so disappointed at this course. deeplearning.ai please do strict quality control on the courses otherwise it harms your brand

Oversimplified illustration of all core definitions and it is not reasonable from any sense to use trax instead of a popular framework like Tensorflow or Pytorch for the assignment. Also, the design of assignment is weak, you can barely learn anything from filling the blanks.

1) The course videos just throw out various nouns and you need to goole them to understand what they mean.

2) The assignments try their best to explain concepts in a way that often seems redundant.

For me, it is very dissapointing, time is spent on irrelevant things, like python syntax and generators in first week. There are missing video tutorials on how to use Trax.

Hard to follow explanations and TRAX absolutely made it super hard to learn and follow.

No mentors were available or contactable during this course

very superficial