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Learner Reviews & Feedback for Sequence Models by DeepLearning.AI

4.8
stars
30,390 ratings

About the Course

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....

Top reviews

AM

Jun 30, 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

Mar 13, 2018

I was really happy because I could learn deep learning from Andrew Ng.

The lectures were fantastic and amazing.

I was able to catch really important concepts of sequence models.

Thanks a lot!

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1376 - 1400 of 3,694 Reviews for Sequence Models

By Sagar P

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Oct 24, 2019

This was a great course. I got good knowledge of sequence models and NLP.

By Vincent B

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Oct 6, 2019

Unfortunately, the assigments are just about filling and it's fairly easy

By Jun L

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May 25, 2019

The last course is harder and I'm glad I've passed it. Thank you, Andrew!

By Shi Y

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Feb 19, 2019

Programming Assignments are awesome! I learnt a lot from these notebooks.

By Greg L

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Oct 31, 2018

Not as relevant for my areas-of-interest, but a great class none-the-less

By Bharat R

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Sep 22, 2018

Excellent perspective on the latest models of LSTMs, GRUs built upon RNNs

By Rohit S

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May 27, 2018

Best course .. really gave me a deep understanding of the sequence models

By Frank

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Feb 20, 2018

Good course, thanks for teaching me so great knowledge of AI

Thanks a lot

By Hang D

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Feb 8, 2018

love it, quite clear visual explanation and with some good example codes.

By Tarry S

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Feb 2, 2018

Amazing source of information and teaching ability to tutor the learners.

By Michal S

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Nov 11, 2022

Great course that introduces the key concepts of Sequence Models and NLP

By Mohammad P

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Nov 11, 2022

It was a great journey. thank you Andrew and Kian and other contributors

By Mahendra B

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Jul 26, 2021

Interesting course module but at the beginning it becomes really complex

By Alexandre R d L

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Feb 19, 2021

The course is very good, well explained and the subject is a lot of fun.

By Rishabh R

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Jul 26, 2020

This course needs no introduction.....great content, great assignments!!

By Rajat k s

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Apr 5, 2020

it's a well-knit course for starters in rnn and takes upto a good level.

By Stephen B

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Sep 30, 2018

Great, I can see lots of parallel problems to work on using these ideas.

By Greg L

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Feb 16, 2018

Thank you for the fantastic course, and the superpowers it imparted! :)

By Saqib N

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Oct 8, 2020

Such a great course with content arranged and organized very carefully.

By Leonid K

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May 12, 2020

Amazing increase in knowledge about using RNNs in real-world scenarios!

By Joao N

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May 9, 2020

Really interesting and with enough theory plus hands on practical work!

By Soroush H

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Mar 31, 2020

this was the best course to understand sequence modeling and processing

By Chaitanya P B

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Mar 17, 2020

Learnt about NLP, applications, trigger word detection was pretty cool!

By Siddhartha B

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Feb 29, 2020

Thank you so much to the coursera deeplearning.ai team. You guys rock!!

By Igor N

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Jan 15, 2020

Curso muito bom! Ajuda bastante a ter uma base em modelos de sequência.