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Comentários e feedback de alunos de Sequence Models da instituição deeplearning.ai

4.8
estrelas
22,678 classificações
2,614 avaliações

Sobre o curso

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....

Melhores avaliações

WK

Mar 14, 2018

I was really happy because I could learn deep learning from Andrew Ng.\n\nThe lectures were fantastic and amazing.\n\nI was able to catch really important concepts of sequence models.\n\nThanks a lot!

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.

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301 — 325 de 2,591 Avaliações para o Sequence Models

por Martin C

Jun 07, 2020

¡Excelente curso! Llegar hasta acá haciendo la especialización vale el esfuerzo. Andrew Ng como siempre un gran maestro.

Excellent course! Getting here doing the specialization is worth the effort. Andrew Ng as always a great teacher.

por Daniel G

Mar 16, 2018

My only constructive criticism is: more difficult homework, or more homework in general. The programming assignments seem very important for internalizing concepts, and this course covers a lot of material in a short period of time.

por Aliaksandr S

Oct 22, 2018

Excellent material; though VERY dense. Notebooks still have some issues, but forums are really helpful. I feel that first 3 courses were much better prepared; though CNNs/RNNs are still super-useful despite not being 100% polished.

por Junliang Z

Oct 17, 2018

Really funny section about sequence model with interesting examples, helping me to understand how to learn the pattern of sequence data and transfer it to useful information. arouse my curiosity about other application in this area

por Sundar S

Sep 13, 2018

Amazing set of courses. Learned a ton of information that I will directly use at my current work and beyond. And Andrew is a fantastic teacher that really engages with his students (despite this being an on-demand online course).

por AKSHAY K C

Mar 21, 2020

The course was very well structured from the basics of RNN progressing slowly towards LSTM, GRU, word embedding and attention model finally. Kudos to the instructor and the team for providing such a good course on sequence models.

por Meer H A

Jun 02, 2019

Thank you Andrew! Learned great things from this deep learning specialization course. The knowledge and certification I gained will help long way, in shaping my career. Thanks once again to the creators of this wonderful course :)

por Carlos A C G

Jun 11, 2020

Amazing job once again by Andrew and his team. The world needs much more of this! Specially more implementations that can be put into use easily in real-life apps and projects. Now we know the theory, we can put it into practice.

por ongole s s

May 27, 2020

I have learnt a lot from this course and it is very interesting to be part of this course as i understood the concepts of NLP in deep learning which is the most fascinating technology to learn and implement in real world problems

por Sergio B S

Sep 14, 2018

The first week of this course is maybe the most harder week of all the Deep Learning Specialization. But, with Sequence Models I have understand infinite better the great possibilities of this techniques for improving the world.

por Shashti K N M

Jun 08, 2020

The Course was Excellent. Sir's teaching was Excellent. I understood the techniques of Sequence Models and Natural Language Processing. The Programming Assignments were Excellent. The Deep Learning Specialization was Excellent.

por George Z

Sep 29, 2019

Amazing course and what a finish line. I only with the graded assignments are revisited as few of them have bugs in them. Also I hope the Word2Vec algorithm and word embedding in general is explained better and with exact steps.

por Sam D

Feb 25, 2019

Awesome course and specialization. Now, to implement everything I learned in my own programs, and of course I will be sure to revisit the videos until everything becomes second nature. Learn, program, improve and repeat. Thanks!

por Himanshu G

Apr 26, 2020

This was particularly intensive course of this whole series, learned a lot.

Thanks to Prof. Andrew NG, accept a Natmastak Pranam from this Student of yours, will always be indebted for what I have learned here. You are the Best

por Rahi A

Jan 09, 2020

I have many of books and blogs related to RNN, but was not clear and confident about it. And after studying only the first week video and lectures, I am so confident and happy that cant tell you!!! Thank you so much Andrew...;)

por leonardo d

Dec 05, 2019

It seems like there are several and very useful RNN models. Many of them are very good at specific tasks, and if you take this course you will be abe to understand and implement many of them. It was a really amazing experience.

por Sandeep P

Jun 27, 2018

An excellent introduction to the theory and practice on recurrent deep neural networks. Great usage of all the 4 courses in this series to culminate with this course as a great finish to deep learning theory and implementation.

por Rafael E

Feb 11, 2018

Yet another amazing class! I'm so grateful for the existence of these classes. It makes mastering deep learning very much easier. My thanks to Andrew, and all others who have worked so hard to make this course possible! :-)

por Hristo B

Feb 25, 2019

Most notably, an exercise guides one through the building of a recurrent network from scratch. More exercises show the value of different architectures and make the learner proficient in using neural network libraries (Keras).

por Aparna D

Oct 30, 2018

This was quite a tough one.. But it was almost magical when the outputs of the assignment were successfully completed. Excellent. The discussion forums helped a lot, as the instructions were not very clear to novices like me.

por Jeffrey T

Apr 02, 2020

Amazing course, Andrew Ng presents the material in a concise and intuitive manner. It would be nice to have access to all of the material needed to fool around with the assignments on our computers in an offline environment.

por Dmitry N

Oct 06, 2019

Thank you for this wonderful sequence of courses! This whole concept is still a bit blurry for me, but as a lot of people during the interview have mentioned, one must simply exercise new skills to understand the technology.

por Gopi P V R

Mar 16, 2019

It's great course to get concepts right and overview. It will be great if you add further programming assignments(other than partially coded ones) or resources as such where one can practice what he had learned as optional.

por Nick S

Mar 30, 2018

Great choice of material, i would be happy to have one more week of that course to see more examples and have more time to familiarise with the concepts. All weeks were very useful and all the material was greatly explained.

por Severus

Jun 05, 2020

This course is good , I learn RNN,LSTM,GRU etc.Just one thing, the last assignment is hard to submit.I guess maybe there is a systematic problem that need to be solved. Everything except that is great. Thanks a lot, Andrew.