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Voltar para modelos de sequência

Comentários e feedback de alunos de modelos de sequência da instituição

28,110 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


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.


29 de out de 2018

The lectures covers lots of SOTA deep learning algorithms and the lectures are well-designed and easy to understand. The programming assignment is really good to enhance the understanding of lectures.

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2976 — 3000 de 3,369 Avaliações para o modelos de sequência

por Vinod C

29 de abr de 2019

Good course. Feel a little bit rushed. Difficult to retain the concepts

por Chen L

14 de mar de 2018

The content is great, but the programming exercises are full of errors.

por Xiao

8 de mar de 2018

Some techniques for keras need to be clarified. Generally a good course

por Jon M

7 de jul de 2021

I liked this particular set of lectures, too, now on to something new.

por Vamvakaris M

8 de set de 2019

It required coding on keras and tensorflow not appropriate introduced.

por Rafael B d S

6 de ago de 2019

The Course is great! But the programming assignments has too many bugs

por Pete H

12 de dez de 2018

it's very difficult to submit last programming exercise "trigger word"

por Ishan S

27 de jun de 2020

More clarification on what we are doing in the programming exercises

por Emanuel G

13 de dez de 2018

Great introduction to LSTMs, RNNs, GRUs, NLP and speech recognition.

por Nilesh R

20 de mar de 2018

Great content but I felt it was bit rushed and squeezed in 3 weeks .

por Alex M

14 de mar de 2018

The quality was a bit down but still very worthwhile and interesting

por Vivek K

20 de jul de 2018

Great practical experience. Would have preferred a bit more theory.

por Fady B

1 de jun de 2018

it covered a lot of interesting topics but it was a bit high level.

por Alireza S

18 de jun de 2020

great course to understand intuition of sequence modeling for NLP.

por guolianghu

5 de abr de 2020


por Bobby A

2 de jul de 2019

Well explained, I feel like it could go a bit more in depth though

por Martin T

13 de fev de 2018

Muy buen curso, resulta sumamente estimulante el ejemplo de woebot

por Vikas C

20 de jun de 2019

The good course as the theoretical basis for RNN and other models

por Shilpa S

18 de mar de 2019

Attention Models is not that clear. Everything else is excellent.

por Andres R

2 de abr de 2018

Excellent lectures. Some programming exercises need more clarity.

por Armand L

28 de abr de 2019

Very hard, but not your fault, very good course ! Thank you !!!!

por Julio T T

28 de set de 2020

Exercises have less quality than the ones in previous courses.

por Josele F

29 de mai de 2019

El curso esta muy bien peor deben añadir subtitulos en español

por Marisa F

18 de nov de 2018

I think this course needs to have a continuation to go deeper.

por Alex N

27 de mar de 2018

Grader was wrong sometimes. Typos everywhere in the notebooks.