A truly great course, focuses on the details you need, at a good pace, building up the foundations needed before relying more heavily on libraries an abstractions (which I assume will follow).
A neatly organized course introducing the students to basics of Processing text data, learning word embedding and most importantly on how to interpret the word embedding. Great Job!!
By Saurabh S•
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By Mohammad B A•
I am so happy
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By Mohamed S•
I loved it
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By D. R•
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 Eloy S•
Es bastante completo, y en general, claro; salvo un detalle: explica demasiado superficialmente PCA, pero luego para la tarea hay que implementarlo manualmente. También tiene algunos bugs desde hace meses a pesar de haber sido reportados con solución. Además, las lecturas posteriores a los videos a veces son escuetas y le hacen falta algunos diagramas que se ven en el video (conviene sacar capturas de los videos para tomar nota).
It is quite complete, and generally speaking very clear, except PCA: it's covered only superficially but it is required to implement by hand on the assignment. Also it has some unsolved bugs since several months ago, despite they were reported with solutions. Also, the readings after the videos are sometimes narrow and lack of some diagrams shown on the videos (it is useful to take screenshots to take notes).
By Nima M•
The content of the course was really interesting an engaging. But the assignments mostly only helped in understanding the details of the algorithms and processes. It would have been nice to get to learn how to use state of the art libraries, which would've been more practical. Although, in fairness, anybody who completes this course should be able to make use of off-the-shelf libraries. Another point was that when the instructor was narrating the slides, his intonation was occasionally a bit off, making me lose track of the subject and having to re-listen few times.
By Yen S L•
Good for the basics of NLP. Good mix of examples from classical NLP (e.g. n-grams) and neural nets (e.g. embeddings). As usual from deeplearning.ai, great motivating examples such as autocorrect and autocomplete to help us understand the materials. The neural net examples could do with more equations as in other deeplearning.ai courses.
By Mares B•
Thank you for the Lecture. I enjoyed it a lot! One thing I did not like too much was reading aloud and fast complex equations. I got distracted a lot when that happened. Also the Grade of the programming assignment is very slow and some additional verification of the programming task would be helpful.
By Kostyantyn B•
A good course overall. I wish the assignments were a bit more challenging though. Still, we have covered a lot of ground. And for those who know nothing about the word embeddings, I think this would be a perfect first course to take. So all in all, time well spent.