Chevron Left
Retour à Natural Language Processing with Sequence Models

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.

Filtrer par :

51 - 75 sur 192 Avis pour Natural Language Processing with Sequence Models

par Yuri C

2 janv. 2021

Among the first three courses of the NLP specialization, this is by far the most exciting. I enjoyed very much all the four weeks and the learning syllabus as a whole! Although many complained about the use of trax as a DL framework, I must say, I found fantastic to be able to learn it from people involved in the development! This per se is already an A+. I congratulate the team in taking this decision and pushing this forward. Trax is intuitive and *very* elegant. Chapeau for the devs! If it is as performant as they say for large data sets, this is the future and I am very pleased that the instructors decided to prepare us for it. Apart from all this positive side, I saw in this third course again some content at the end of the assignments that was not introduced during the corresponding week. For example, the Gumbel sampling at the end of Week 2. This was not a graded exercise, therefore it is not a major problem. Nevertheless, it comes out of the blue for the student and it is hard to connect the dots and understand why are we performing this operation at all for the text generation. So, there are a couple of loose threads here and there along the course. But it is a minimal problem. On the other hand, the presentation and discussion of the sequential models in all 4 weeks are very good, again an optimal balance between mathematical formalism, intuition and ease to code. Moreover, the choice of applications in the four week are just right, classification, generation, NER and one shot learning. All in all an awesome package, congratulations!

par John Y

6 janv. 2022

This was another great course. I previously put on my to do list learning or reviewing about classes and I was happy to see it covered here. I enjoyed learning about data manipulation, sampling, and iteration or generation process and Trax. At first I was a little hesitant about learning a new program or library like Trax but I found Lukas' talk to be helpful and convincing. I feel Trax does simplify the coding process quite nicely. The homework seemed repetitive but I found that approach to be very useful because I think the intent was to help us familiarize with the coding process and Trax more quickly. I previously completed the DL Specialization and appreciated this course very much. Imo, someone new to DL and RNN might find this course confusing because the concepts are not explained as much in depth as in the DL Course.

par Nishant M K

5 avr. 2021

Great course! I needed to check in on some of the discussions in the discussion forums for this one, so the discussion forums are especially useful (for assignments for weeks 3 and 4). As in the first 2 courses in this specialization, this one also adds most value in its 'lab' and assignment Jupyter notebooks. The videos serve as a gentle introduction to the topics and the concepts from the lectures are emphasized upon in the assignments/labs. Great introductory course overall!

par James M

15 déc. 2021

Very good course. The only issue I have is when you have some questions about the code or you have issues if no one else has your problem you seem to be on your own. Sometimes I had just some conceptual coding questions and you can't ask why the code is doing what it is doing. I did learn a lot and for the price it is still worth it.

par Dustin Z

14 nov. 2020

A really good and detailed course on sequence models. This was definitely the most challenging course in the specialization so far in part because of the use of the Trax framework. I really enjoyed reading the source code of Trax and understanding how the ML framework was constructed. This was a very unique part of this course.


29 janv. 2021

Excellent course, I would like to learn a little more to know how to adjust the classification threshold in the Siamese network, tuning of parameters in the LSTM network, and how to solve common error problems in the models performance. This course is a good base to introduce you to the sequence models.

par Ram N P

20 févr. 2022

Would have been more useful if all the code snippets, labs, assignments were in Tensorflow or Pytorch. I understand that Trax is more easy to use and deploy. But untill companies really start using this library it is of very less benefit to learners.

par Sarwar A

28 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.

par Ahammad U

12 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.

par Nikesh B

7 août 2020

Awesome course!! Younes explains all the concepts very nicely :) I enjoyed this course a lot and learned many new things, which I am planning to use in my current project. Thanks a lot, Younes

par Hieu D T

24 avr. 2021

This course is much more difficult than the 2 previous ones in the series. Not because of the way instructor transferring but in the knowledge itself. Totally worth taking this course

par Sebastián G A

4 nov. 2020

Excellent course on sequence models and how to solve problems in industry and academia with them. Beautifully structured assignments and well-explained lectures, quite enjoyable!

par Christopher R

21 mars 2021

I wish the neural networks would be described in greater detail.

Everything else is really nice, Younes explains very well. Assignments are very nicely prepared.

par Sabita B

23 avr. 2021

amazing course. material is very well presented and explained! really loved the data generator part of the code - really drilled in the importance of it!

par Shaida M

19 févr. 2021

Interesting course. I like this specialization very much. I don't understand why one instructor introduces the topic and another instructor explains it.

par Martin B

26 janv. 2021

Concise, to the point, and very insightful/educational. Take it in conjunction with the general Deep Learning Specialization, you'll not regret it.

par Alan K F G

21 sept. 2020

Absolutely satisfied with the tons of things I learnt. Professor Jounes and his team did a great work. Looking forward to enrolling to next course.

par Bharathi k N

19 sept. 2020

The course is great and presented excellently with neat visualizations. Introduction to Trax is great and got a chance to learn new framework.

par Alex M

20 janv. 2021

Excelente curso para iniciar en el mundo de los modelos en secuencia. El nivel de dificultad es el necesario para aprender. #Colombia

par Shahin Z

20 oct. 2020

Excellent slides, notebooks, assignments, course content, conciseness, explanations. All great. Made all the topics very accessible.

par Carlos O

23 août 2020

It's a great introduction to the Trax Framework for Deep Learning. Building cool models for NLP makes the course well worth it.

par Yusa L

7 févr. 2021

It's really good to use generator many times through the assignments. This really helps me understanding why we do need this.

par Piotr B

8 nov. 2020

Course was awesome, got some need to go back here in the future to restore useful knowledge when deplyoing model to my app.

par Cristopher F

22 juin 2021

T​his a very interesting course, even though I wouldn't quite recommend for a beginner in Neural Networks to take it.

par Wong H S

7 juil. 2021

Very useful concept with concrete example. The lab and coding assignment are challenging yet help me to upskill.