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Comentários e feedback de alunos de Sequence Models da instituição

19,818 classificações
2,183 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. 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


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.


May 26, 2019

I am so grateful that Andrew and the team provided such good course, I learn so much from this course, I am so excited that see the wake word detection model actually work in the programming exercise

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401 — 425 de {totalReviews} Avaliações para o Sequence Models

por Aswin S T

Aug 06, 2019

This course extensively covers the aspects of NLP and its various use cases. It will be of an excellent start for me on my pursuit of research in this field.

por Badr S

Feb 20, 2020

As always, Andrew is absolutely amazing at transmitting advanced knowledge and making it accessible to most people. Thank you Andrew and the whole team !

por Arnav C

May 14, 2019

Loved this course. It was everything I expected and more. Always had a hard time understanding attention mechanism and Dr Andrew Ng explained it very well.

por Emilian A

Apr 16, 2018

I find CNN/RNN courses very helpful, but I would highly recommend to improve the exercises. Some typo mistakes can result in hours wondering what is wrong.

por Somashekharappa B H

Oct 15, 2019

As usual Mr.Andrew NG is great in explaining the concepts.I hope to make use of the skills learnt through this course and this one is coming in great way.

por mouette g

Aug 11, 2019

Very interesting course! It has taught me a lot. Special thanks to the Mentors and the Students who gave hints and explanations in the discussion forums.

por Daniel M M

Jan 10, 2019

Extremely clear and useful. Natural Language Processing, Speech Recognition and Automatic Translation are now concepts much more clearer and close for me.

por Laurent J

Feb 24, 2018

Slightly more difficult than the previous course but a fascinating course. Programming assignments require more thoughts but they are real world use cases

por Rajat M

Oct 20, 2019

Amazing Content !

Grateful to Andrew Ng and his Deeplearning.AI team for spending great amount of time and effort to come up with such a quality content !

por Michael O

Apr 30, 2019

Course content and lectures are excellent. Programming assignments have some minor glitches which should be addressed for a course that requires tuition.

por John E S

Dec 24, 2018

Great lectures. Well thought out quizzes and exercises. The only negative point is that the exercises are too easy, they spoon feed too much of the code.

por Janzaib M

May 24, 2018

Perfect Start for Understanding RNNs, LSTMs, BRNN, Attention and Basic NLP. It gets you up and running with very easy KERAS implementation of the things.

por Andrew S

Feb 15, 2018

The lectures are amazing. The assignments were difficult for me, but forced me to understand the concepts better. Really glad to have taken the course.

por Peter D

Feb 11, 2018

Excellent course that not only provides a good foundation for deep learning in general, but also gives some insights into more recent developments in AI.

por Subrata S

Aug 17, 2019

Very much satisfied, to its content. Learnt a lot, thanks a ton to Prof. Andrew Ng for your hard work on its topics & how making learning easy.


por Marcus B

Jun 28, 2019

Very effective at improving my understanding of RNNs (and its variants), Natural Language Processing, and some basics regarding working with audio data.

por Benjamin S S

Aug 16, 2018

Great course, but needs more checks for understanding during the lecture. Course would also benefit with a dedicated module on TensorFlow and/or Keras.

por Uyen H

Mar 16, 2018

The course is well-structured, and a nice introduction to sequence-related neural networks. The programming assignments cover interesting applications.

por Suresh K M

Mar 20, 2020

Fantastic! After this course, i can clearly understand how the basic RNN works. All the programming exercises are very very useful! Thank you so much!

por Kseniia P

Jun 30, 2019

Probably the hardest course of the specialization, but made easier with thorough explanations of basic sequence models' architectures.

por Gökhan

Feb 19, 2018

This is an awesome course like other courses in this specialization. You can easily understand concepts and apply them thanks to Andrew and his team.

por Rooholla K

Feb 20, 2020

Thank you Andrew for being such a good and kind person. You've been a shelter and a kind teacher for all of us. Thank you, Thank you and, Thank you.

por Emmanuel J A

Feb 09, 2019

Great course on how RNNs work and how they are used to solve real problems (speech recognition, translation, names generation, music generation...).

por Ramesh N

Jan 19, 2019

Systematic, step by step approach to understanding sequence models and practical exercises to see them implemented with lots of guidance.

Thanks you!

por Shantanu B

Dec 19, 2018

The toughest course in the deep learning specialization for me. Learnt a lot. Made me ready for further readings and consolidation of the materials.