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

4.8
estrelas
23,751 classificações
2,756 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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2576 — 2600 de 2,734 Avaliações para o Sequence Models

por Suresh D

Mar 26, 2018

I guess as the subject matter becomes more complex, more training is required on the underlying frameworks being used- Keras, TensorFlow etc. Did not feel that sufficient time was spent on understanding the underlying frameworks. Also the TA work is of spotty quality. But I love the way Andrew teaches.

por Salih T A

Apr 05, 2020

The assignments were not good i think. Because they explained the consepts too long and complicated as like we've never seen these on lectures. I was waiting assignment to require more insight about architecture and less python programming knowledge. This comment is for week1 assignments in special.

por Devin F

Mar 11, 2018

For me, there was a large gap on time between when course 4 and 5 were offered (months). This unfortunately was enough for me to forget everything I learned about Keras.

Of course, this course assumes you know Keras so I was behind for the labs

Material is interesting though.

por Marshall

Mar 13, 2020

Of the courses in the specialization, this one seemed the least organized and rushed. Some of the assignments had some annoying auto-grader quirks that made troubleshooting a pain. Overall it is still worthwhile, just be ready to search forums for help during the assignments.

por Kerry D

May 15, 2018

Too many thing introduced in programming assignments without explanation. Why the high dropout values? Why sometimes one dropout layer, sometimes two? Many things are just given as a formula, and not explained in a way that would let me make my own network for my own problem.

por Alessandro P

Jun 22, 2020

The lessons are very good as always, but I'd like to be tested more in the programming exercises rather than literally being told what to do and then fill in missing parts of already completed code. Still super glad I took the specialisation, it has been extremely helpful.

por Mason C

Sep 12, 2018

Had to rate this lower due to problem with the final assignment. Submission and saving situation was a nightmare, I had to redo my work several times. Please fix this, it's a real downer at the end of the course. Otherwise, content stellar as always.

por Ashvin L

Oct 22, 2018

The course content is pretty good for breadth. However, it falls short in going into depth. Assignments need to be more open-ended and probably a bit more involved. It appears that we are cutting and pasting code that is already written in comments.

por Oliverio J S J

Feb 12, 2019

This course presents an interesting review of several strategies that are part of the state of the art. However, it is impossible to assimilate how they work in the time devoted to each one. The "fill in the blanks" exercises do not help much.

por Jorge B S

Sep 23, 2019

This course gives a nice overview of sequence models. If it is true that I do not have an engineering background, I felt it got sometimes a little bit too abstract as compared to other courses of the specialisation. However, I recommend it.

por arnno b

Feb 29, 2020

I would advise giving more tutorials about TensorFlow and Keras. Those are your main tools and eventually, in many cases we were only required to complete the gaps which don't give you a true understanding of how to use those frameworks.

por Heming C

Feb 08, 2018

The programming exercises can be better polished, there was quite a few errors that caused unnecessary confusion to the students. Many times, I felt like I was fighting with the Keras/Tensorflow API rather than solving a ML problem.

por Ben R

Jun 27, 2019

Courses had some issues with the grader, and there were some instances where the expected output in the assignment didn't match the actual output, despite it being correct.

See forums for a range of complaints on the matter.

por Smith R S

Feb 03, 2019

Need more detailed explanation and programming assignments are way too easy.I would suggest to make advanced courses for people to improve their knowledge keeping all this courses also considering not all feel it very easy.

por Dominik B

Apr 27, 2020

In comparison with other courses in this specialisation a lot of assignments were poor quality - vague descriptions and code logic (especially week1, asign 2 & 3) or just broken (last week3 assignment)

por Pier L L

Feb 14, 2018

With respect to the others, this one seems to be prepared almost in a hurry and the learning curve is very steep and sometimes the programming assignment don't have a nice progression as the others.

por Saipuneet M

Feb 02, 2020

Videos were really informative and were equally interesting, but I believe that the programming assignments lacked a bit in clarity. The instructions were really unclear, it could have been better

por Lyn S

Apr 04, 2019

Quite a few bugs or abstractions in this course, in comparison to the others the projects feel a bit rushed and pushed together. Andrews's explanations and video lectures were still great though.

por 田奇

Mar 03, 2018

this course is the most difficult in deep learning specification, but i think Andrew NG should design more homework for word embeddings and bidirectional rnn, i do not understand how it works yet

por Egnatious P

Apr 29, 2020

The course was great. However, coming from finance I was also hoping to see some examples which use time series so I can get a picture of how I can extend this knowledge to my specific domain.

por Ioannis B

May 27, 2020

The module was really good in explaining the concepts, but there wasn't any deep dive on the equations and mathematics behind with the results of making the code assignment harder to achieve.

por Farzad E

Jun 19, 2019

I gave 5 stars to other courses in this series but this one doesn't deserve 5 stars. There were many typos and bugs in the assignments compared to the other courses of the specialization.

por Rohit B

Sep 27, 2019

Video Lectures were excellent.

Assignments, however, were buggy and spoonfed you too much. I completed them, but on more than one occasion, I had no idea what the code was actually doing.

por Удимов Д А

Jun 28, 2020

Boring course:

Short videos instead of lections.

Copy-paste task instead of exercises.

Loading pretrained models instead of training.

Good full keras tutorial will be ten times more useful.

por Noam S

Oct 27, 2018

The lectures were not as good as the previous andrew ng. courses, and the exercises were quite bad in all honesty.

I do appreciate what I have learned, as the lectures WERE clear enough.