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Voltar para modelos de sequência

Comentários e feedback de alunos de modelos de sequência da instituição

27,663 classificações
3,305 avaliações

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


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!


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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651 — 675 de 3,306 Avaliações para o modelos de sequência

por Marcus B

28 de jun de 2019

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

por AasimBaig M

2 de ago de 2020

This has been an excellent journey and I personally learned a lot from this courses. I want to thank AndrewNg for being the best teacher I ever had. <3

por V V

14 de jun de 2020

This specialization really helped me understand DL thoroughly. I really thank Coursera and Andrew's team at for this wonderful content!

por Srividhya S

1 de mai de 2020

Awesome assignments. This course was a little difficult to understand but the assignments helped in understanding some of the complex topics discussed.

por Benjamin S S

15 de ago de 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

16 de mar de 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

20 de mar de 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

30 de jun de 2019

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

por Gökhan

19 de fev de 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 Sergei B

25 de fev de 2021

Great course. The way Andrew explains the material makes it very understandable. Labs are very neat - especially the comments and markdwon portions.

por mcvean s

20 de nov de 2020

Always a pleasure to learn from sir Andrew, and this is one of the best courses that teach Natural Language Processing and Sequence Models in depth!

por Vivek M

11 de abr de 2020

Sir Andrew teaches in a very friendly way, also the programming assignment is great to check your understanding of the concepts. Highly recommended.

por Rooholla K

20 de fev de 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 A

9 de fev de 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

19 de jan de 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

18 de dez de 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.

por Jun W

6 de nov de 2018

Concepts are covered very well. They are not very easy to grasp. But Professor Ng makes it easy. Hopefully, I will practice some of the knowledge.

por David G

8 de fev de 2018

Thank you Andrew for sharing all these great and latest staff in the AI Deep Learning field. Fantastic course. Will recommend it to all my IT staff.

por Mohit s

14 de ago de 2020

the hole module properly designed and the lecture was very interesting and helpful also

coding assignments were really challenging but very useful.

por Fuat O

10 de jul de 2019

This course have been very useful to learn fundamentals of sequence models. I'm really very happy to apply this course and being able to finish it.

por Aishwarya R

7 de mai de 2019

Excellent course. RNN is a complicated topic which has been taught so easily. Thank you Professor Andrew Ng. Loved every programming exercises too.

por Alberto G

29 de set de 2018

Very good quality course taught by Professor Andrew Ng. You will learn the basics to master Deep Learning Sequence Models using Keras / TensorFlow.

por Du L

3 de mar de 2018

Excellent course! I learned a lot. The assignment are not as well prepared as previous courses. Probably they'll be better as students raise errors

por Mihajlo

20 de fev de 2018

As a novice in seq-2-seq models, I learned so much! This is a great source of state-of-the art knowledge. I only wish it was at least 4 weeks long.

por WAI F C

17 de fev de 2018

Professor Ng's lectures provided intuitive ways to understand the complex recurrent neural networks and how to apply it in real world applications.