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Avis et commentaires pour d'étudiants pour Natural Language Processing with Sequence Models par

910 évaluations
184 avis

À propos du cours

In Course 3 of the Natural Language Processing Specialization, you will: a) Train a neural network with GLoVe word embeddings to perform sentiment analysis of tweets, b) Generate synthetic Shakespeare text using a Gated Recurrent Unit (GRU) language model, c) Train a recurrent neural network to perform named entity recognition (NER) using LSTMs with linear layers, and d) Use so-called ‘Siamese’ LSTM models to compare questions in a corpus and identify those that are worded differently but have the same meaning. 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....

Meilleurs avis


27 sept. 2020

Overall it was great a course. A little bit weak in theory. I think for practical purposes whatever was sufficient. The detection of Question duplication was a very much cool model. I enjoy it a lot.


11 nov. 2021

This is the third course of NLP Specialization. This was a great course and the instructors was amazing. I really learned and understand everything they thought like LSTM, GRU, Siamese Networks etc.

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26 - 50 sur 192 Avis pour Natural Language Processing with Sequence Models

par Julio W

28 juin 2021

I​ learn to hate Trax in this course. In the assignements, Trax is used only for toy problems, and then, we use a precomputed model. Even the fastnp is used in a very slow mode. Why to learn NLP in a obscure and really bad documented framework if we will finish to use a precomputed models?

Moreover, I try to replicate the results in my own machine (or even in colab) it does not work, because Trax change a lot between versions. Again, why to use, in a course, a framework that is not stable?

I​n my opinion, using a new and obscure framework to tech new concepts, only because you love it, is (at least) antipedadogical.

par Jorge A C

13 oct. 2020

As other course reviewers noted, this course did not help much to build the intuition underlying the methods used. The video lectures were short and the explanations, though concise, were convoluted and not clear at all. For a real understanding of what sequence models are capable of I recommend watching the lecture videos of Stanford CS224N. I

par DANG M K

29 août 2020

This course material is not good compare to the Deeplearning Specialization. I hope the instructor will write down to explain detail, not just reading from slide

par bdug

23 avr. 2021

I was disapointed by this course:

I did not like at all the use of Trax. At our level (student), we need a well established and documented library like Keras or Pytorch to illustrate the concepts. Trax is badly documented. And since the installation of the Trax version used in the assignement fails in Google Colab (!!), I had hard time reproducing the assignements in google colab.

Week 3 is just a scam since it says "go and read this blog" or "watch this video in another specialization". At that moment I simply felt robbed.

par Dimitry I

14 avr. 2021

Very superficial course, just like the rest in the specialization. Quizzes and assignments are a joke. Didn't want to give negative feedback at first, but now that I am doing course #4 in the specialization, which covers material I don't know much about (Attention), I've realized how bad these courses are. Very sad.

par Baurjan S

26 sept. 2020

Great Course as usual. Tried siamese models but got a very different results. Will need to study more on the conceptual side and implementation behind them. But overall, I am glad I touched LSTMs.

par Sudharsan

16 août 2020

Learning about the Trax library and solving practical problems with the library was really interesting. Siamese network architecture was great thing to learn.

par Zoltan S

1 août 2020

This is an excellent course with some cutting edge material, and also an introduction to a new learning framework trax.

par Jerry C

11 oct. 2020

Great course! However the assignments are handholding too much step by step... I'd prefer the assignments to allow students to think more for themselves when implementing functions etc. (and only unhide hints or seek help on Slack when struggling for a long time)

par Swakkhar S

23 sept. 2020

First two courses were much better. It introduces trax, which is great. However, the materials of this course is already covered in the 5th course in the deep learning specialization. On the whole, great course, great efforts by the team.

par JJ Y

26 sept. 2020

Sequence models are heavy subjects and it would be unrealistic to expect a 4-week course to go into all the depths of RNN, GRN, LSTM etc. This course does a great job covering important types of neural networks and showing their applications. However, the labs and assignments could have done more in (a) helping us look a little deeper into the implementations of different NN building components, and (b) aligning better with the lecture videos.

Really Good examples: Week1 labs and assignment illustrate the implementations of some of the basic layer classes, and outline the overall flow of NN training with Trax. Week4 labs and assignment illustrate the implementation of the loss layer based on the unique triple loss function.

Not so Good examples: Week1 uses a whole video explaining gradient calculation in Trax. Yet there is no illustration of how it's integrated in backward propagation in Trax. Week2 videos and the labs/assignment are more disjoint. There is a video explaining the scan() function, but it does not show up in the assignment at all.

par Kota M

21 août 2021

Sadly, the quality of the material is much lower than the previous two courses. Assignment repeatedly asks to implement data generators with a lot of for-loops. We should focus more on the network architecture rather than python programming. That being said, the implementation is not good either. Learners would have to learn to program anyways.

par Patrick C

23 déc. 2020

Assignments are very difficult to complete because of inaccurate information (off by one on indices and other sloppy mistakes). You also don't learn much from them because almost all the code is already provided. It would be much better if they built up your understand from first principles instead of rushing through fill in the blank problems.

par Mostafa E

13 déc. 2020

The course did well in explaining the concepts of RNNs... but it may in fact have provided less knowledge than the NLP course in Deep Learning specialization.

I was looking forward to see more details on how translation works using LSTMs, go over some famous LSTM networks such as GNMT, and explain some accuracy measures such as the BLEU score.

par Artem R

1 déc. 2020

Course could be completed without watching videos - just by using hints and comments in assignments, videos are short and shallow, choice of Deep Learning framework (TRAX) is questionable - I won't use it in production.

Despite the course is 4 weeks long it could be accomplished in 4 days - I don't feel that it was worth the time.

par George L

21 mars 2021

Compared with the Deep Learning specialization, this specialization was designed in a way that nobody can understand. Although the assignment could be easy at times, the point is being missed when people cannot really understand and learn. Bad teacher. Andrew Ng, please!

par Youran W

4 déc. 2020

All the assignments are extremely similar.

par Xinlong L

22 août 2021

I did not enjoy the course at all. It looks like the instructor is just reading materials rather than really teaching. He just focused on reading and did not explain anything. I took Andrew's deep learning specialization, and that course was really great. But I am so disappointed at this course. please do strict quality control on the courses otherwise it harms your brand

par Yanting H

13 oct. 2020

Oversimplified illustration of all core definitions and it is not reasonable from any sense to use trax instead of a popular framework like Tensorflow or Pytorch for the assignment. Also, the design of assignment is weak, you can barely learn anything from filling the blanks.

par Ngacim

27 nov. 2020

1) The course videos just throw out various nouns and you need to goole them to understand what they mean.

2) The assignments try their best to explain concepts in a way that often seems redundant.

par Emanuel D

26 janv. 2021

For me, it is very dissapointing, time is spent on irrelevant things, like python syntax and generators in first week. There are missing video tutorials on how to use Trax.

par Siddharth S

19 sept. 2021

Hard to follow explanations and TRAX absolutely made it super hard to learn and follow.

par Alistair M

19 févr. 2022

Superficial descriptions of the topics; quality definitely lacking

par Alice M

15 nov. 2020

No mentors were available or contactable during this course

par Nicolás E C R

23 déc. 2020

very superficial