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

4.8
estrelas
26,174 classificações
3,088 avaliações

Sobre o curso

In the fifth course of the Deep Learning Specialization, you will become familiar with NLP models and their exciting applications such as speech recognition, music synthesis, chatbots, machine translation, natural language understanding, and more that have become possible with the evolution of sequence algorithms thanks to deep learning. By the end, you will be able to build and train Recurrent Neural Networks 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. DeepLearning.AI is proud to partner with NVIDIA Deep Learning Institute (DLI) to provide a programming assignment on Machine Translation with Deep Learning. Get an opportunity to build a deep learning project with leading-edge techniques using industry-relevant use cases. The Deep Learning Specialization is our 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 gain the knowledge and skills to apply machine learning to your work, level up your technical career, and take the definitive step in the world of AI....

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.

WK
13 de Mar de 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!

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2926 — 2950 de 3,060 Avaliações para o Sequence Models

por Eymard P

31 de Jul de 2018

Far less detailed than the other ones. The programming assignements are less interesting too, as a great part of the work consist of reading documentation

por Reetu H

23 de Dez de 2019

There were lot of bugs in the assignments taking up lot of time to fix. The course was okay, I liked the other courses in the specialization more.

por Kaupo V

7 de Mai de 2018

The Keras programming exercises are quite weak. Please re-think how to teach them more systematically. Currently it is quite a lot of hit and miss.

por Assa E

10 de Fev de 2021

That was much harder than the previous courses of the specialization. However it felt like the videos are more hasty and less understoodable

por Leandro A

18 de Mar de 2018

There was a bug in a programming assignment notebook that took too much time to notice that i was doing ok but the expected ouptut was wrong

por David H P

2 de Abr de 2018

The programming assignments required some extra effort to understand Keras which I thought may need an introduction video like tensorflow.

por Iván V P

18 de Fev de 2018

Several grader issues, only 3 weeks of work, and a lot of errors in the solutions... In addition, less content than in the other courses...

por Rishabh G

19 de Set de 2020

The earlier courses were easy to understand, however, this was way too difficult. Andrew Ng did not make this easy like the other courses.

por Hang Y

24 de Ago de 2018

Compared with previous courses, this one seems to be rushed. The focus on applications seems to be much higher than the theoretic side.

por Yash R S

9 de Mai de 2018

Not as great as the other courses in the specialisation. The assignments can be a little off putting, but lectures are top class again.

por Roberto S

12 de Mai de 2020

Week 1 took double time to be completed. Times proposed for the assingnement are underestimated.

Please readjust the assingement time.

por Ankit S

17 de Jul de 2018

Assignments are not up t the mark.. Expected to have high vocabulary size word embedding assignment, Machine Translation assignments

por Nachiketa M

16 de Fev de 2018

This course was good but in comparison to the other courses in the deeplearning course series, this course lacked adequate depth.

por 1140325971

23 de Jan de 2020

The course is a good course because the lecture Ng.W ,but the exercises is not easy for our beginers for such tools like kears.

por Seng P T P P

8 de Nov de 2019

The programming assignments in this course are difficult to implement. The detail descriptions are needed inside the notebooks.

por Thomas N

9 de Mar de 2018

Good subject, but a lot of the course material (like lecture slides and problem sets) was either unavailable or out of date.

por Vinjosh V

11 de Mai de 2020

The videos are great - however it would be useful to provide some help on how to implement the concepts programatically.

por Søren M

16 de Fev de 2018

Not as good as the previous courses in the series, and some of the assignments where broken, and super hard to debug.

por Laurent B

12 de Jan de 2021

Only on NLP applications, it would have been great to apply GRU or LSTM on numerical data like finance for example.

por Devansh K

20 de Set de 2020

The content covered is interesting, but I feel like the explanations are not as intuitive as the previous 4 courses

por Sandeep P

27 de Jul de 2020

Nice but little stressful. After completing 5 courses in a row, really feeling exhausted. Thanks for the good work.

por Rudolf S

15 de Jul de 2019

Quite a lot of bugs in the first week examples. It took me too much time until I browsed the discussion forums.

por SHALEEN A

3 de Out de 2020

the videos are programming assignments need some serious updates, too many typos and wrong information present

por Maysa M G d M

25 de Jun de 2018

I think you have to know more keras than is explained in the course. The keras documentation it is not enough.

por Raghav G

13 de Jul de 2020

An average course, it is detailed though some of the ending materials get too difficult for me to comprehend.