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Avis et commentaires pour d'étudiants pour modèles de séquences par

28,080 évaluations

À propos du cours

In the fifth course of the Deep Learning Specialization, you will become familiar with sequence models and their exciting applications such as speech recognition, music synthesis, chatbots, machine translation, natural language processing (NLP), and more. By the end, you will be able to build and train Recurrent Neural Networks (RNNs) and commonly-used variants such as GRUs and LSTMs; apply RNNs to Character-level Language Modeling; gain experience with natural language processing and Word Embeddings; and use HuggingFace tokenizers and transformer models to solve different NLP tasks such as NER and Question Answering. The Deep Learning Specialization is a foundational program that will help you understand the capabilities, challenges, and consequences of deep learning and prepare you to participate in the development of leading-edge AI technology. It provides a pathway for you to take the definitive step in the world of AI by helping you gain the knowledge and skills to level up your career....

Meilleurs avis


30 juin 2019

The course is very good and has taught me the all the important concepts required to build a sequence model. The assignments are also very neatly and precisely designed for the real world application.


29 oct. 2018

The lectures covers lots of SOTA deep learning algorithms and the lectures are well-designed and easy to understand. The programming assignment is really good to enhance the understanding of lectures.

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126 - 150 sur 3,365 Avis pour modèles de séquences

par Carlos V

14 févr. 2018

Another Excellent Course from Professor Andrew Ng. The detail in the explanations are excellent, and the provided exercises using Jupyter are super fun to complete and put to the test your knowledge offering you at the same time a library of ideas and models to use in your future projects. I enjoyed this last course in the specialization quite a lot, thanks very much to Andrew Ng and the Staff from Coursera. I hope to see more courses like this in the future.


par Rodolfo V

7 août 2020

I love this course. Maybe about 1 and half year I was trying to learn DL. I thought about giving up because I wasn't able to learn, so, in May of this year 2020, Coursera opened its courses to undergraduates. I do not thought twice, at the same hour that I knew that news about free courses, I began this. Then, after that time I want to thank to Professor Ng and all colaborators on that site. One day I will be one of student of Professor Andrew Ng in Stanford.

par Prithvi J

5 mars 2020

A greatly knowledgeable course! I learned a lot about Natural Language Processing and explored RNNs, LSTMs, Word Embeddings, Seq2Seq Models, Attention Mechanism, etc. The course focuses more on the concepts along with providing the essential math. It was fun to implement Language Models, Neural Machine Translation & Speech Recognition. I would surely recommend this course to the ones who are diving into the world of NLP, and need a perfect introduction to it.

par H.S

1 janv. 2022

Another very good course, though not without its failings. Mainly, the Transformers' lectures and programming assignment were confusing and didn't go into enough depth to deliver the intuition.

I also think less time spent on learning word embeddings would've been better. instead, maybe a concise intro to natural language understanding.

All in all, I still consider it a great experience and have nothing but gratitude for Andrew and his great teaching style.

par Huanglei P

31 juil. 2018

This end course is a little more complicated than the previous ones, especially in programming homework. However, it also inherits the merits of the special, gives learners the basic framework of sequence models. What impresses me most is the lesson of "Debiasing word embeddings", it shows that AI could be designed to do more against human stale thoughts, which sets up a good principle for designing AI. Yes, it should be taught to new learners of AI.

par Alireza N

6 juil. 2022

I​ d recommend this course to those who want to find their your paths as they are searching for what it is that makes their life fullfilling. The series of Depp learning specialization made me acquaitned many important technical, some fundumental and some advanced, aspects of AI world and standing here, now, I suppose I can think better about what I'm gonna do in the future.

E​ndless gratitude to the team who provided us with the material and support.

par Andres G

22 mars 2020

Finally... Every piece of effort was worth it! After so many hours, now I understand how proud we can fell of completing these amazing courses! The best one I have tried so far, definitely made a difference in my professional views but above all, it confirmed my expectations: this is the activity sector where I want to develop, the work in which I want to grow without any doubt.

Thanks Andrew. Thanks Team. Thanks to everyone who made this possible.

par Marcus H

26 sept. 2020

This regards all 5 courses of the DL specialisation.

1st of all: great work, it gives a much broader perspectives.

Room for improvement: sometimes the assignments become much of a "Python riddle" where one has to fiddle a lot with language technicalities and loses time for actually playing with the DL subject

2nd: please improve the submitting and savin g behaviour of the notebooks in the new LAB system. It is really painfully slow and unstable.


25 juin 2019

This was the most difficult and most interesting course i had in all of the five courses

but after doing all the 7 assignments i feel like i learned a lot and encountered with some of the amazing thing which i wondered how they are done . Once again I thanks to Andrew Sir and other teachers for beautiful lectures and perfect quizzes assigments and at last a heartly congrats to Coursera for giving this platform to me.

Thank You!

par Mihai L

21 mars 2018

Will give this course also 5 stars. The assignments were easy but required some knowledge of Keras. So you have to invest some time on their site.Otherwise it's like fitting pieces in a bigger puzzle. Most pieces are already layed out for you .. you need to just fit your small ones.

I realize though that deep learning requires a lot of practice and experimentation and completing this course (and specialization) is just a tiny first step ..

par P S R

12 févr. 2018

Course contents and coverage was best. Duration of 3 weeks is little too short to really understand all the details of programming exercises. May be extend this to 4 to 5 weeks and spend little more time on speech recognition, music generation and other audio data processing would have helped.

Unlike all other earlier modules, this one had many issues with grader and many errors in note book templates. Hope these will be addressed in future.

par James B

1 mai 2018

Wonderful course, expert instruction from Prof. Ng. I can't recommend the Specialization enough.

The choices of architecture and of hyperparameters for the assignments' network could have used further explication. Another desire left unfulfilled was that I would want the sequence models course doubled in all dimensions, ie lectures, assignments, etc. It was all over too quickly with questions lingering. Further study required!

par Weinan L

7 avr. 2018

RNN, LSTM, GRU... fun stuff even you don't focus on NLP. As always, Andrew makes complicated things simpler. I certainly will keep all the course materials for future reference.

It may be easier to follow other online course, but this course will teach you not just how, but also why...

Read coding instructions carefully and pay attention to details, otherwise you may end up with hours of debugging. That's what happened on me, LOL.

par Virginia A

7 avr. 2020

Sequence Models are a though subject. many people, during working meeting, mention them as the final resource and solution to everything. I feel I better understand the nuances of them thanks to this course.

I personally enjoyed some of the extra reading ( original papers quoted at the bottom of the videos). Sometimes is hard to navigate in the large sea of publications. It is nice to be pointed towards some piece of reference

par Chris D

11 janv. 2020

I go back and forth on whether the time-saving aspects of the Python Notebooks are worth the reduction in ML coding experience. I suppose these aren't coding classes, but I also feel some of the concepts aren't cemented as well as if the students were led through a more challenging, trial-and-error experience. That's hard to do, though.

Overall, I recommend the specialization. Maybe just be sure to play around offline, too. :)

par Sima M H

25 mai 2021

Immensely grateful for holding this course, specially Prof. Ng. The way he explained the all concept to the mathematical models was very endearing and excellent.

That was great, however I was expecting to learn at least 1 allocated week to time series data and forecasting (prediction) in sequence model.

In addition, if in one assignment we had imported data ourselves, we could have learned the section much better.

Best Regards

par João A J d S

23 avr. 2021

The only trouble with this course is that we're talking about seriously deep networks. That means it's difficult to present working, practical cases (jupyter notebooks) to work all the steps.

Still, I'd recommend presenting more and simpler steps towards building an RNN (particularly an LSTM). I had to come back to the notebooks several times... and honestly, I think I'll get back there again to try and understand better...

par 王浩礴

1 juil. 2019

This series of course provides a comprehensive overview of NLP algorithm and different applications. I really enjoy the projects the deal with audio files. The course skip the linear algebra and differentiation part that not everyone wants to look into. But I hope it will be better if we could also implement the data processing functions of different types of sequential inputs, since data preprocessing is also significant

par KIM T

11 juin 2021

My knowledge has been upgraded to the next level through Coursera's Deep Learning Specialization. Through systematic and easy-to-understand explanations, quizzes, and program exercises, I was able to increase my interest and understanding of Deep Learning. Based on this, I really want to change my field of work and work related to Deep Learning. My goal is to make myself a person who uses AI without being replaced by AI.

par Stefano I

12 déc. 2019

This was a great intro to RNNs and Sequence Models.

Particularly liked the assignment on voice keyword detection. It was useful to learn how to synthesize a dataset quickly and train a proper model for the task.

Also the NLP parts were useful. I would have liked to have more advanced assignments, but still it was a great course that gives you enough knowledge to learn more on your own or explore more advanced courses.

par Najeeb K

24 août 2018

I had struggled with the complexity of Sequence Models ever since I started learning about Machine Learning models. This course gave me an easier intuition to the sequence models without dwelling too deep into the mathematical complexities. As a person who has very little experience with Linear Algebra this helped me a lot to understand and apply such architectures to solve problem. Thanks Prof Andrew and the team! :)

par Frank T

20 févr. 2018

I think it is a great course. There are some issues here and there with notebooks and related materials. However, considering the large and detailed amount of content in this course and it being a new course, things not being 100% perfect is OK by me. I would rather have the thoughtful content and exercises, versus something much lighter that would be easier to produce. Thank you to all who prepare these courses.


22 juin 2020

Superb course structure, the assignments beautifully complement the lectures and the amount of guidance makes it easy even for someone not too acquainted with programming. As a suggestion would have liked slightly organized detailed presentations which would help in reviewing the course material later by glancing through rather than going through the lectures. Over all an awesome course with great learning. Thanks

par Lavan O P

21 mai 2020

I enjoyed learning all the five courses of this deep learning specialization. Special thanks should go to Dr. Andrew and the instructors for delivering the course material in an interesting manner. Quite frankly I'm a little bit disappointed with this specialization being too short. Expect more courses in this specialization in the future. (Maybe reinforced learning).Again thank you all for this great experience.

par Michael Y

23 mai 2020

I'm grateful for the chance to take the 5 courses in this program for a very affordable price. It is the best educational deal I've ever come across. The courses are well taught, I will continue on to take other courses offered online on the same subject. Thanks to everyone who made this possible, and I will definitely try to make a contribution to humanity as Prof. Ng has challenged us to do.

Thanks again!