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

4.8
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21,594 classificações
2,463 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!

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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276 — 300 de {totalReviews} Avaliações para o Sequence Models

por Tashreef M

May 19, 2020

I think considering the other courses, Sequence Model course was a little bit faster and it covered a lot of topics, so it was a little tough to keep up. But what I learnt was quite a lot and I think it is is quit a very good course indeed.

por Pedro M H V

Dec 06, 2018

Great course: material, instructor, assignments, everything! Perfect way to introduce or consolidate your knowledge in recurrent neural networks to model sequence data with a really strong component of direct industry-relevant application.

por hervind

Mar 23, 2018

Well structured and easy to comprehend.

Mr Andrew Ng and team has organised the course in the best way.

Through this course I get new theory and programming experience for my self.

The exercise is fun like naming dinosaur

Thanks after all

por Mehul

Aug 27, 2019

Feels great to complete this wonderful journey. Once again thank you Andrew Sir for creating this type of novel courses, great to have you as an instructor. Hope I someday would build a great model of my own, and get a chance to meet you!

por Alex B

Mar 23, 2018

Very good, by necessity it's a little hand-holdey but otherwise it would take an unreasonable amount of time to cover the material. Hopefully I'll be able to leverage the background i gained in the specialization in future job experience.

por 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.

por Amanda W

Mar 22, 2019

Andrew Ng does an excellent job of explaining and teaching various sequence models in a format that any layman can understand. I really enjoyed this course and look forward to taking more corses with Mr. Ng and his staff in the future.

por 刘尧

Nov 17, 2018

Finally, I finished all the courses of the specialization within 4 weeks! Great courses, and I will recommend everyone I met who wants to learn ML and DL to go and see this series of courses. Thank Andrew Ng and course assistant as lot!

por Surya B

Jan 11, 2020

Excellent course. The instructor is able to teach difficult concepts in a very simple way which make them very easy to understand. Enjoyed the programming assignments. Looking forward to any course Andrew Ng might teach in the future.

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 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 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).