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Comentários e feedback de alunos de modelos de sequência da instituição deeplearning.ai

4.8
estrelas
28,139 classificações

Sobre o curso

In the fifth course of the Deep Learning Specialization, you will become familiar with sequence models and their exciting applications such as speech recognition, music synthesis, chatbots, machine translation, natural language processing (NLP), and more. By the end, you will be able to build and train Recurrent Neural Networks (RNNs) and commonly-used variants such as GRUs and LSTMs; apply RNNs to Character-level Language Modeling; gain experience with natural language processing and Word Embeddings; and use HuggingFace tokenizers and transformer models to solve different NLP tasks such as NER and Question Answering. The Deep Learning Specialization is a foundational program that will help you understand the capabilities, challenges, and consequences of deep learning and prepare you to participate in the development of leading-edge AI technology. It provides a pathway for you to take the definitive step in the world of AI by helping you gain the knowledge and skills to level up your career....

Melhores avaliações

AM

30 de jun de 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.

MH

21 de abr de 2020

Very good. I have no complaints. I though instruction was very clear. Assignments were very helpful and challenging enough that I learned something, but not so challenging that I got stuck too often.

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2826 — 2850 de 3,375 Avaliações para o modelos de sequência

por SHAHAPURKAR S M

16 de mai de 2020

Faced issues regarding assignment submissions. Otherwise, the course is perfect. Would upgrade my review to 5 stars if this issue seems to be fixed later

por Alex M

15 de fev de 2020

Es buen, algo extenso, pero suficiente para avanzar. Algo importante es actualizar los cursos con los nuevos algoritmos, al menos uno, por ejemplo BERT.

por minsq n

19 de ago de 2019

This course is quite challenging, but at least the concepts were well explained. Wished that Andrew and his team could conduct a crash course on Keras :)

por Maxim V

5 de out de 2019

A great intro to RNN, LSTM, GRU, Activation. Programming assignments are rather messy though (unlike those in the other courses of this specialisation).

por Harshit S

25 de mai de 2019

Great course, I like the practical application and assignments discussed in this course , wish latest research papers were also discussed in the course,

por Jun W

16 de mai de 2019

This course introduces mainly about RNN, GRU and LSTM. Great assignments. 1 score off for the in-correction in assignments. 4.5 scores from me actually.

por Octav I

23 de dez de 2018

Great lectures, really well explained, assignments could request more from the trainee to devise the logic instead of having it already defined for him.

por Marcela H B

28 de jun de 2021

Good course, however I would like to have more Transformers application in the last part as well as some information regarding the fine tuning of them.

por Thierry L

30 de jun de 2020

Thank you very much for all the work you have done. I have learned so many things... I will try to use this stuff in the coming months. Yours, Thierry

por Tiago C G M

3 de mar de 2019

The course is really good, I would recommend it to anyone who wants to learn the subject, but it lacks support from the staff in the discussion forums.

por Tomasz D

3 de out de 2020

Very good course. Some editing issues in the lectures and small issues with the programming exercises (outdated Keras instructions and documentation).

por Nicola P

14 de fev de 2018

The lectures are excellent. The assignments are an extremely valid trace of significant deep learning application, while they lack a bit of challenge.

por Alon M

13 de out de 2018

As always, this course is great. however, for some reason this course is much more difficult then the others, and i feel as if it is packed too much.

por Michael S

12 de jul de 2018

Really good course, like the others. A bit too black box in some of the programming exercises, so I expect to struggle when developing my own models.

por Ethan X

10 de abr de 2018

The videos are really informative and well structured. However, the exams felt like Keras tests. A detailed Keras tutorial would have been helpful.

por Takeo S

28 de mar de 2019

It was great course,

I wish we have more speech recognition contents

Hope, you add new course a bit focus on audio/speech recognition etc

Thank you!

por Ara B

31 de dez de 2019

too much content and not much chance to exercise. I will suggest for more frequently and smaller programming assignments through out the course!

por Rodrigo N S

17 de fev de 2021

Outstanding course, but the end of it uses many architectures not fully explained (GRU and such). Incredible course and specialization, though!

por Reda M

19 de out de 2020

Excellent course, but I would have liked to work on predictive maintenance examples leveraging RNN and LSTM networks. Big thanks to whole team.

por Nishant B

11 de ago de 2019

The course is nicely designed and every topic is explained in a very lucid manner by Andrew Ng. Must be done as a beginner in sequence models.

por Suraj S J

20 de mai de 2019

Simplified content delivered in just the right way to give a perfect intuition of the complex concepts. Really enjoyed doing the whole course.

por Harry T

17 de jan de 2019

Great content, but Andrew often starts his phrases then restarts saying them. Audio could use some cleanup, then this course would be perfect!

por Yunhua J

11 de jun de 2018

Most optional assignments contain bugs/errors. Other than that, this is a great course, just as the 4 other courses in this specialist series.

por 王煦中

3 de fev de 2018

I give 4 star because some fomulas are not correct! Though this course is really great. I can not understand why you made mistakes on fomulas.

por Makito K

25 de mai de 2021

Great materials and programming exercises. The programing exercise in week 4 could be improved for the more beginner-friendly style probably.