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Avis et commentaires pour l'étudiant pour Sequence Models par deeplearning.ai

4.8
15,907 notes
1,741 avis

À propos du cours

This course will teach you how to build models for natural language, audio, and other sequence data. Thanks to deep learning, sequence algorithms are working far better than just two years ago, and this is enabling numerous exciting applications in speech recognition, music synthesis, chatbots, machine translation, natural language understanding, and many others. You will: - Understand how to build and train Recurrent Neural Networks (RNNs), and commonly-used variants such as GRUs and LSTMs. - Be able to apply sequence models to natural language problems, including text synthesis. - Be able to apply sequence models to audio applications, including speech recognition and music synthesis. This is the fifth and final course of the Deep Learning Specialization. deeplearning.ai is also partnering with the NVIDIA Deep Learning Institute (DLI) in Course 5, Sequence Models, to provide a programming assignment on Machine Translation with deep learning. You will have the opportunity to build a deep learning project with cutting-edge, industry-relevant content....

Meilleurs avis

JY

Oct 30, 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.

AM

Jul 01, 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.

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1 - 25 sur 1,723 Examens pour Sequence Models

par Lewis C L

Apr 15, 2019

Full of appalling errors that have been present for over 1 year. No one fixes it. It is clear that since Ng was let go by Stanford and Baidu, he is trying to earn a living with deep learning_ai. This apparently is not working as the small income from Coursera is not sufficient. As a result the prerecorded classes remain on Coursera to accrue some residual income. But, Andrew Ng and the staff are apparently gone.

Sadly, since these classes are no longer based on REAL Stanford classes the quality has gone downhill. I would recommend not taking the deeplearng_ai classes. Stick to classes offered by currently employed professors at established universities--preferably classes that ARE the same as the university classes or, at least, those derived from actual classes.

par Dylan R

Oct 20, 2018

Tons of editing errors in lectures, and the programming problems rely more on knowledge of Keras (essentially untaught throughout the course) than they do on understanding of lecture material. A disgraceful end to an otherwise solid course sequence.

par Alex R

Jun 15, 2018

Keras is required to pass the assignments but no training provided for it. I can learn it myself of course but then the question is this - what am I paying for?

par Bogdan P

Nov 03, 2018

I really like the deeplearning.ai specialization. And also I like the Sequence Models course. However, I feel that I have learned less during this course comparing to the other ones in the specialization. First, I believe it was an extensive use of Keras. Whereas the framework is great, it would be much better for understanding if all the exercises were in numpy, whereas Keras tween-projects be optional. Doing both numpy and Keras versions would allow to better understand the material and learn through repetition. Second, even though the course is great, I perceived the number of errors/typos was much higher than in other courses. Is that true? For example, the Jazz Improvisation exercise was a nightmare. Overall, thank you for the course. Despite those problems, I would still recommend it.

par Nathan P

Feb 20, 2019

I'm blown away by how quickly this series of courses brought me from thinking a neural network was a magic box full of fairy dust, to being able to understand even the (al)most complex of network architectures and what makes them tick at every level at a glance. A lot of time has obviously gone into structuring this course; not an ounce of fat present and the format of developing intuition before diving into the nitty gritty and optional further learning resonates with me on so many levels. Thank you Andrew Ng and the team at deepmind.ai and coursera!

par khushal m

Apr 11, 2019

I think it is the best courses designed so far. Gives you exactly the appropriate amount of information needed to understand basics behind sequence models. A must do course for all the students who want to pursue a career in this field.

par Ng K W P

Apr 11, 2019

If not Internet, I would not have been able to study a world-class Deep Learning course at an affordable price. Thanks Andrew and team.

par Jizhou Y

Mar 02, 2019

Professor Andrew is really knowledgeable. I learn a lot from his lecture videos.

par Andrés F R

Nov 08, 2018

I want to thank Andrew Ng and his team for the amazing work. You definitely make the world a better place sharing this knowledge, and it is an inspiration.

To the contents: the course covers many uses of sequence models, for many different formats (many-to-one, many-to-many...), the questionnaires are focused but comprehensive and the programming exercises cover a wide range of difficulty levels, from no-brainer-one-liners (most of them) to implementing LSTM backprop by hand (optional). They take away the dirty work from you but make sure you get how you would do it. At the end you get to work with pretty complex setups like the attention model, but you still get the feeling of knowing how it ticks from the very bottom up.

The actual merit is that, even if it feels simple, it actually does work and is a takeaway knowledge that can be directly applied for personal setups. And mr Ng's videos are a charm, you can totally feel the care. Glad to see him back after so many years :)

Cheers

par Ozioma N

Jun 09, 2019

Great module, I am lucky to have used this resources in learning sequence models, I can imagine running LSTM using one of the frameworks without ever implementing it myself, Andrew Ng/Deeplearning.ai is the best!

par Abhijeet M

Jul 01, 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.

par Zhongyi T

Jun 11, 2019

Poor submission system. Failed many times to upload and had to redo the assignments. I was using a 250Mbps high speed network. Also course materials are problematic. The instructors are not willing to fix the problems for many years.

par Bhaskar D

Dec 12, 2018

Excellent course. Highly recommended!

par Yoan S

Dec 11, 2018

Excellent state of the art deep learning models made easy. Great job Andrew! And THANKS SO MUCH!!!

par Daniel G

Dec 11, 2018

Auch der Letzte Kurs war sehr gut! Diese Spezialisierung hat sich sehr gelohnt. Der Stoff wurde gut erklärt und war sehr gut anwendbar.

par Jhon S

Nov 26, 2018

cool

par Veeresh S

Nov 24, 2018

Thank you Andrew NG for teaching AI

par Srivathsan A

Nov 24, 2018

Awesome conclusion to deep learning. The 1 side trigger detection algorithm is good final touch. Thank you Andrew NG..

par zhiqing h

Nov 24, 2018

Very detailed hands-on assignment. Hard tho

par Brian L

Nov 26, 2018

This was an awesome sequence. I wanted to understand Deep Learning and the techniques and ideas that had advanced the state of the art beyond ordinary neural nets. I was particularly interested in Sequence Models which are of interest in my line of work.

par Sushanta P

Dec 15, 2018

Andrew Ng at it best.

par Mukund C

Dec 14, 2018

Best in the series

par 罗炜儒

Dec 14, 2018

该门课程对序列模型的讲解由浅入深,一步步带领我们从最基础的RNN走到最后的LSTM及更复杂的模型,作业十分有趣,尤其是课程最后一次作业能让我们真切感受到深度学习的力量及其给我们的作业

par Michał K

Dec 15, 2018

Very good course to start dealing with RNN's.

Thank You Andrew for Your whole specialization. Now i feel like a superhero on a rise

par Kiwoong Y

Nov 27, 2018

As we proceeded with the lecture, we were able to gain a better understanding of the sequence model and further assist in envisioning entrepreneurial items. Thank you.