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Learner Reviews & Feedback for Natural Language Processing with Attention Models by DeepLearning.AI

4.4
stars
968 ratings

About the Course

In Course 4 of the Natural Language Processing Specialization, you will: a) Translate complete English sentences into German using an encoder-decoder attention model, b) Build a Transformer model to summarize text, c) Use T5 and BERT models to perform question-answering, and d) Build a chatbot using a Reformer model. By the end of this Specialization, you will have designed NLP applications that perform question-answering and sentiment analysis, created tools to translate languages and summarize text, and even built a chatbot! Learners should have a working knowledge of machine learning, intermediate Python including experience with a deep learning framework (e.g., TensorFlow, Keras), as well as proficiency in calculus, linear algebra, and statistics. Please make sure that you’ve completed course 3 - Natural Language Processing with Sequence Models - before starting this course. This Specialization is designed and taught by two experts in NLP, machine learning, and deep learning. Younes Bensouda Mourri is an Instructor of AI at Stanford University who also helped build the Deep Learning Specialization. Łukasz Kaiser is a Staff Research Scientist at Google Brain and the co-author of Tensorflow, the Tensor2Tensor and Trax libraries, and the Transformer paper....

Top reviews

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.

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51 - 75 of 237 Reviews for Natural Language Processing with Attention Models

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 Chenjie Y

Nov 18, 2020

I think the last course is a bit rush... Many concepts are not natural and cannot be explained by one or two sentences. Comparing to the previous courses in the specialisation which really explains concepts and intuitions in detail, this last course is a bit too rough. I would rather spend another month to study the materials in two courses, instead of staying up late to read papers and blogs to understand what was not explained clearly in the course. And also, i see that trax is a good library but i think up to now it is not yet mature, and i really wish all the assignments can have tensorflow versions and let the students to choose.

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.