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Natural Language Processing with Classification and Vector Spaces に戻る

deeplearning.ai による Natural Language Processing with Classification and Vector Spaces の受講者のレビューおよびフィードバック

4.6
3,519件の評価

コースについて

In Course 1 of the Natural Language Processing Specialization, you will: a) Perform sentiment analysis of tweets using logistic regression and then naïve Bayes, b) Use vector space models to discover relationships between words and use PCA to reduce the dimensionality of the vector space and visualize those relationships, and c) Write a simple English to French translation algorithm using pre-computed word embeddings and locality-sensitive hashing to relate words via approximate k-nearest neighbor search. 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! 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....

人気のレビュー

MN

2021年5月24日

Great Course,

Very few courses where Algorithms like Knn, Logistic Regression, Naives Baye are implemented right from Scratch . and also it gives you thorough understanding of numpy and matplot.lib

SK

2020年7月17日

One of the best introductions to the fundamentals of NLP. It's not just deep learning, fundamentals are really important to know how things evolved over time. Literally the best NLP introduction ever.

フィルター:

Natural Language Processing with Classification and Vector Spaces: 476 - 500 / 724 レビュー

by Doan N L V

2020年10月5日

Nice one

by Nam V T

2022年8月15日

Awesome

by courage l

2020年11月1日

Good!!!

by Zoizou A

2020年10月25日

amazing

by Muhammad A B

2020年10月1日

perfect

by Mohamed S

2020年9月8日

PERFECT

by Thành H Đ T

2021年10月14日

thanks

by Prateek S P

2021年1月17日

thanks

by Jeff D

2020年11月8日

Thanks

by Rafael C F d A

2020年9月28日

Great!

by Kamlesh C

2020年8月30日

Thanks

by Qamar A

2020年8月5日

Cool!!

by Prins K

2021年7月28日

Great

by 克軒廖

2021年2月5日

Nice!

by 刘世壮

2021年12月4日

good

by GANNA H

2021年8月4日

good

by Efstathios C

2020年8月12日

Good

by Abhinav S

2022年5月2日

bk

by Dave J

2021年1月1日

Having previously completed the Deep Learning Specialization, I came to this course with the intention of completing the whole NLP specialization, rather than because I was especially interested in the content of this first course from that specialization.

The Deep Learning Specialization sets a high standard of teaching quality and I have to say I found this course is not quite to the same standard. It's pretty good but not as good. The instructors are very knowledgeable, they make the effort to explain each topic clearly and they do a pretty good job of that.

What I felt could be improved is providing context of where each topic fits into the broader picture of both the theory and current practice of NLP. I was often left feeling, why are we spending time on this particular topic? Is this technique used in current practice or is it just of didactic or historical interest? Great teachers always have the broader context in mind and make sure that students see how everything fits into the bigger picture and why it is worth studying.

Although techniques were clearly explained, I felt that the underlying concepts were sometimes less well explained. An example is vector representations of words: we were shown the use of vector arithmetic to find analogies, but without much in the way of explanation of how this is possible. To me, this was the wrong way around: it makes more sense to me to first build an understanding of the representations, then introduce the remarkable result that these representations allow finding analogies.

In this course, sentences are represented as a "bag of words". This is processing natural language in the way a food processor processes food: chopping it up into a word soup. Since one of the most fundamental aspects of language is its structure, this might seem a hopeless approach. However it gives surprisingly good results for some simple tasks such as classifying tweets as having positive or negative sentiment. If you've done course 5 of the Deep Learning Specialization (Sequence Models), this will feel like a step backwards. There's no deep learning in this course. But I signed up for the course knowing that, so I can't criticise it on that basis. I'm taking the view that this course lays the foundations for more advanced and current topics in the subsequent courses in the specialization and I look forward to getting onto those.

The labs and assignments generally work smoothly. There are a few inconsistencies and a couple of the hints were a bit misleading but generally OK. It's a bit paint-by-numbers though, filling in bits of code within functions rather than working out for yourself how to structure the code.

by Kaiquan M

2022年1月22日

This "Natural Language Processing with Classification and Vector Spaces" course covers: - Logistic regression - Laplacian smoothing, log likelihood, naive bayes models to predict sentiment of tweets - Euclidean distance, cosine similarity between word vectors to understand relationship between sets of text, and Principal Component Analysis - Language translation using rotation matrices, k-nearest neighbours and locality sensitive hashing The course has weekly lecture videos and has a summary reading after almost every video, which was especially helpful when trying to understand the concepts discussed in a video as a whole. There are also shorter labs to familiarise you with NLP concepts before the weekly graded programming assignment. Be sure to walk through and understand how the functions in utils_%.py accompanying each lab work. Similarly, walk through the functions in utils_%.py and how unit test cases are prepared in unittest.py accompanying each assignment. A good part of this course has been that the course team periodically releases new versions of the labs and assignments containing fixes or new approaches. Therefore bugs discovered by users in your assignment 3 months ago could already be fixed by the time you work on your assignment. The downside to the course is that the discussion forums were not actively monitored. Therefore there are some questions I have on certain concepts which were not answered by the time I completed the course.

by D. R

2021年3月22日

I'm a master/graduate student who took an NLP course in Uni.

I think that overall this is a very a good introduction to the topic. Some concepts are really well explained - in a simple manner and with a lot of jupyter-lab code to experiment with.

In general in this specialization - the first 3 courses are good. There are some quirks (e.g. why Lukas is needed at all? He doesn't really teaches, just passes it on to Younes) but nevertheless I learned from it. And I think they have good value in them.

The 4th one, however, is completely disappointing. First 2 "weeks" are confusing, not really well explained, but somewhat "bearable". The last 2 weeks are complete sham. They claim to teach "BERT" and "T5" but don't really give any value. You're better off going elsewhere to learn these concepts.

If it wasn't for this, I would give the overall experience a 5 stars, but because of this, I think the overall is more like 3 or 4.

by Li J

2022年1月4日

Took me roughly 5 days to finish this course. The course is of high quality. The instructors had done a great job preparing for the materials in this course. The videos are short and concise, and they focus more on intuition instead of mathematic details, which is great for beginners.

However, I would say the coding portion in this course is pretty limited. We did not get to write a lot of codes as most of the frameworks and functions had been implemented for us. What we need to do was simply filling in the blanks (and the comments will reveal the answers, making it even much easier). I also feel that some helper functions should not be provided. I think the course will be better if we get to write codes for labs also. Nevertheless, the course is open to the general public so I think this design is rational.

by Jeff

2020年7月2日

The course is interesting because it takes a look at NLP processing from a different view than just deep learning or word-in-bag type analysis. Most concepts are clearly explained, allowing most anyone with some python language experience to make it through the course. The programming assignments are similar to other courses. That is.... the assignments can be frustrating because you are trying to fill in the blanks through someone else's style of coding, but satisfying when the assignment is completed. I imagine that a few more iterations of the developing the course material and this will be another 5star course for the deeplearning.ai organization.

by Mohammed A E

2022年2月6日

The course is a great introduction to NLP where various techniques and algorithms were explained clearly either through videos or notebooks. I'm giving 4 stars as some question in the quizzes had a bad formatting or couldn't submit the correct (answers are straightforward but still gives wrong answer as a non numerical entry). Another negative comment about some questions for example: can we use Kmeans to reduce the time of searching? and Kmeans was a wrong answer, though intuitavely it can be used as well. All in all, the course was great and I glad I took it.