Excellent. Isn't Laurence just great! Fantastically deep knowledge, easy learning style, very practical presentation. And funny! A pure joy, highly relevant and extremely useful of course. Thank you!
Great course for anyone interested in NLP! This course focuses on practical learning instead of overburdening students with theory. Would recommend this to every NLP beginner/enthusiast out there!!
创建者 DAVID R M
•This course was quite sloppily presented and superficial overall. There were a couple of longstanding errors that have never been fixed (see the lengthy discussions in forums). One thing that annoyed me was that the important concept of stop-words was not discussed at all, yet it was required for the first assignment.
创建者 Tal F
•All assignments were optional - probably because of all the problems with the scoring system for the previous course. Quizzes often asked things about the dataset we used (eg IMDB) rather than testing that we were learning concepts. Very little meat to the course - mostly links to other resources.
创建者 Hartger
•Overall the video material is fine. The assignments however are very unclear and contain bugs. The grader's test don't match the instructions. It's very frustrating that the assignments clearly haven't been given the same attention the rest of the course has been.
创建者 Prosenjit D
•This course is a far cry from Andrew Ng's deep learning specialization and refers to Sequence Models from that specialization at the drop of a hat. In short, no use doing this one, unless you have done sequence models (course 5) of deep learning specialization.
创建者 Dominik B
•No grader exercises,
sample code in the lectures isn't always updated and gives errors,
everything is a bit chaotic (eg order of sample code, sample code description, introduction to the topic is random; some random parts in the code).
创建者 Venkata S Y T
•The weekly exercises are not graded and the over all content quality of this course in comparison with the previous two in the specialization seems a bit poor and doesn't provide more learning on the topic.
创建者 Amit K
•Not clearly explained and only using toy and irrelevant datasets, nothing realtime industry specific examples. Also, voice quality is very bad for this course.
创建者 Jurica Š
•I would call this entry/beginner level material. There arent any graded coding challenges, which is a shame. No complex topics are covered with this class.
创建者 jack c
•It's a bit too basic and there are not many graded examples to work through like Andrew Ng's course. I feel it could have been more complete and in depth
创建者 Graham W
•Disappointing. Laurence much less able to explain NLP issues than CNN issues. Lots of problems with TF versions in Colabs wasted far too much time.
创建者 Joey Y
•The quality of the audio recording is worse than courses before. The questions at the end of the chapters are also repetitive.
创建者 Amr K
•didn't really feel like a strongly grasped the concept and needed more exercises also the lack of lessons notebooks.
创建者 Maged A
•Too short. Fine as introduction but not in depth course. No assignment except very shallow multiple choices tests.
创建者 Benoît Q
•Not enough content; far too easy; the whole course should be one week of a good tensorflow course.
创建者 Nirzari D
•The audio quality is very bad! It should be improved so the content is audible to the user
创建者 Anton Z
•I wish assignments were provided the same way as in the previous two courses
创建者 Masoud V
•Useful but shorter and easier than expected and not deep enough for me
创建者 Jose R
•It is too mechanical and reinforcement of concepts if very limited
创建者 Md. M R
•Good course. but we expected hands-on assignments to learn better
创建者 Alexander S
•Dont see the value behind predicting words.
创建者 AasaiAlangaram
•Not Much Information provided.
创建者 Daniel C
•Missing code evalution
创建者 Jack P
•Unfortunately, really disappointed with this course.
Having done the previous 2 courses int he specialisation I have come to realise that the courses are much more of a tutorial and could be seen as quick practice content before going for the TF Developer certificate or something. That in itself is fine, I feel there are other places to learn the maths/intuition behind DL (e.g. the Deep Learning Specialisation) but I feel especially in this 3rd course the content really doesn't justify paying for it.
For starters the explanations are very fast, hand wavy and don't go into any real depth other than just quickly explaining each line of the short notebooks (this can be useful). There is no discussion on how to improve the models or actually use this other than just pressing play in the notebook, the length of videos 17min per week is really not worth it, especially when better content can be found for free in Kaggle notebooks or on YouTube. There are also no graded exercises and after the first week they have given up on even providing suggested answers for the ungraded ones. The exercises don't test your TF understanding, just your basic Python loading of data and if you can copy from the example workbooks, they also have inconsistencies new untaught content and prone to errors that you haven't even been told could be an issue which means you just waste time being frustrated at not understanding what code you're even supposed to add rather than trying to understand the content.
I really like Coursera in general so this experience won't change that, but given that the instructor has free content on the TF website and youtube channel it seems like a waste to pay for this course IMO.
Hoping the 4th course will. be better
创建者 Huet P
•Videos are too short. Unlike Andrew did, there was not enough talk on intuition and how to tune the hyperparameters. There are a lot of redundant questions in the quizzes, and not enough explanations on the notebooks. I would prefer graded exams, not ungraded ones with answers. I would prefer the coursera lab instead of the google collab platform as we cannot access again previous works.
创建者 Alexander B
•Very little content. Extremely short videos, with some notebooks thrown in and show only the most basic applications... I would have expected in-depth explanation on Network-Architecutres, instead of having to memorize the name of some methods in keras (fit_on_text or fit_on_texts). Come on! This course seemed rushed, compared to the high standard of other deeplearning.ai courses.