JH
Oct 4, 2020
Can the instructors make maybe a video explaining the ungraded lab? That will be useful. Other students find it difficult to understand both LSH attention layer ungraded lab. Thanks
SB
Nov 20, 2020
The course is a very comprehensive one and covers all state-of-the-art techniques used in NLP. It's quite an advanced level course and a good python coding skill is a must.
By Christine D
•Jan 22, 2021
Even though the theory is very interesting, and well explained the videos dive too deep in certain concepts without explaining the practical things you can do with them too very well.
The practical stuff, especially the graded assignments, are very centered around Trax, and the only things you have to know and understand are basic python and logic. You don't really get to make your own stuff, you just fill in stuff like "temperature=temperature" or "counter +=1".
I preferred and recommend the first two courses in this NLP-specialization.
By Thomas H
•May 21, 2021
While the course succeeds in getting the most important points across, the quality of both the video lectures and the assignments is rather disappointing. The more detailed intricacies of attention and transformer models are explained poorly without providing any intuition on why these models are structured the way they are. Especially the lectures on current state-of-the-art models like BERT, GPT and T5 were all over the place and didn't explain these models well at all.
By Eymard P
•Jul 21, 2022
The course is okay, but to be fair, nothing compared to what Andrew Ng had done. The explanations are too vague, I feel a lot of details are missing. I now have a basic understanding of transformers, but it is pretty shallow. The assignments are too mechanical, I was just understanding locally, but not much globally.
The bottom line is that this course is an okay introduction to Attention and Transformers, but you'll need to work on the side to refine the knowledge...
By Junhui H
•Nov 15, 2022
Course four is way more advanced than the previous three courses. If you are not very familiar with tensorflow or the architect for deep learning, it will be a bit hard to keep up with content. Also, the videos do not cover enough detail and sometimes it is difficult to understand the upgrade notebooks.
That said, I can see the course is well prepared, and if you have enough knowledge in deep learning, it will still be quite useful for you.
By Zhuo Q L
•Jul 4, 2021
It is exciting to learn about the state of the art approach for NLP, but as the last course of the specialization, one can feel that the quality/level of details of descriptions just dropped significantly. I like how the course introduces useful things like SentencePiece, BPE, and interesting applications, but some of them felt abrupt and wasn't elaborated.
By Dan H
•Apr 5, 2021
Pros: Good selection of state of the art models (as of 2020). Also great lab exercises.
Cons: The video lectures and readings are not very helpful. Explanations about the more tricky parts of the models and training processes are vague and ambiguous (and some times kind of wrong?). You can find more detailed and easier to understand lectures on Youtube.
By dmin d
•Jan 7, 2021
Have to say, the instructor didn't explain the concept well. A lot of explanation doesn't make sense, or just give the final logic and skip all the details. I need to search on youtube or google to understand the details and concept.
But, it covers state-of-art models for NLP. It's a good starting point and helped save time.
By Oleksandr P
•Apr 4, 2021
Although this course gives you understanding about the cutting edge NLP models it lacks details. It is hard to understand a structure of the complex NLP model during the few minute video. This course should have step by step explanations in the bigger number of lectures or increase their duration.
By martin k
•Apr 26, 2021
Low quality programming assignments, but considering the price it's good overall
By Randall K
•Jun 14, 2021
In the previous 3 courses, the HW was a natural extention of the lectures and provided solid reinforcment of the course material. However, in this course, I found the courses did not prepare me for the HW. Furthermore, I found the lectures too terse, often incoherent, and the homework tried to introduce new concepts that were not discussed in the lectures. Also, the code in the labs was poorly organized and the lack of a consistent and coherent style between assignments and even previous courses, which made it difficult to follow the logic. I often spent a lot of time sorting out tensor indexing issues, which is very difficult in Jupyter without a debugger.
By DAVIDE M
•Mar 9, 2022
This course is good if you want to be theoretically good with Transformers model. I mean now I can explain those concepts to my colleagues or pair. It lacks with the practical parts, a lot of exercises are too guided e there is no project that you can show off. The hugginface part is the most interesting for practicing but there are only a few lessons. In the end, do not expect to make a chatbot in week four, it is "just" a model that generates dialogue between two persons.
By Tianpei X
•Nov 1, 2020
the homework is way too simplified esp. in week 3 and week 4. My impression is that the ungraded lab was actually the real homework but was put aside to allow more people to pass. That is not a good compromise.
By George G
•Dec 6, 2020
Week 1 jumps into material that is better explained in Week 2. Attention deserves a more gradual and a more deep explanation. Weeks 3 and 4 cover a lot of ground, without going into depth.
By Dimitry I
•Apr 17, 2021
Material coverage is very superficial. Do not expect to fully understand or be able to work with Attention models after doing this course.
Sadly, these types of courses and their fake near 5-star reviews are destroying Coursera.
By David M
•Feb 22, 2021
Unfortunately, the classes are given at a very primitive level without explaining what exactly Attention models do. The programming exercises were not explained well, either
By Rabin A
•Apr 19, 2021
The course was pretty good. It introduced me to the state-of-the-art algorithms and techniques needed to have a sound understanding of NLP. One thing I didn't like about the teaching method in the whole specialization is that Younes was the one teaching the course content to us but Łukasz talked as if it was he giving some of the lectures, although we could clearly find out it's Younes from his voice. Thanks especially to Younes for doing all the hard work for the specialization. You deserve a 5 star.
By Dustin Z
•Dec 17, 2020
A very good and detailed course. Definitely the most challenging course I have taken by DL.ai. Gives a good overview of Transformers, the current cutting-edge of NLP models. Also, provides great insight into Trax, Google Brain's ML framework, which was helpful in understanding how deep learning frameworks are built. One of the teachers is one of the authors of Trax!
By Ganesh M
•Oct 10, 2020
Every week's assignment brings a new challenge and it was fun to complete the assignments. Course Instructors explain concepts very well. This course teaches you from the beginner level to a professional level. Covers every topic related to NLP. I enjoyed learning NLP with Deeplearning.ai. I would like to thank deeplearning.ai for making this course.
By Tam H H M
•Sep 30, 2020
Good course in overall. The last two weeks' assignment is a little bit too light. The instructor could introduce more about loading pretrained models and fine-tune them as it is a popular practice nowadays for small companies with limited resources (data/computation). Introduction to "easy-to-use" framework such as huggingface is highly recommended.
By Rajendra A
•Dec 30, 2020
This specialization covers from NLP basics to the advance models currently being used. All the programming assignments, contents and sessions were thoughtful. Exposure to Trax library and learning experience was really excellent. Thanks to the entire team of this specialization and coursera team.
By Peter T
•Jan 1, 2022
The final weeks of this course, especially, introduce cutting edge NLP models and practices, such as T5, Huggingface and Reformer. This entire course was comprehensive in breadth. Highly recommended but you should be prepared to put 10x more hours into it than the Coursera estimates.
By Long L
•Nov 18, 2020
Thank you Coursera and the DeepLearning.AI team. The moment I set foot on this journey I did not think I would love NLP so much. The course is very informative: it teaches NLP from the very first naive algorithm to the State-of-the-art models today.
By Bharathi k N
•Oct 12, 2020
The course is so good and well presented. I really enjoyed the whole specialization. Thank you for this amazing course and the whole specialization which that me a lot. Thank you Andrew NG and deeplearning.ai team for this amazing specialization.
By Alan K F G
•Oct 21, 2020
I learnt a lot about Transformers and Reformers which belong to the most advenced models for NLP tasks. The instructors were fully prepared though I'd prefer to see more animations in following courses. Thank you so much for spreading knowledge!
By Muhammad T W
•Jun 12, 2021
This course has helped me a lot in developing my NLP skills and now I am confident that I can solve NLP problems easily because both the instructors Younes and Luckerz has thought this course in a way that it can be absorbed in any NLP problem.