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Learner Reviews & Feedback for Sequence Models by DeepLearning.AI

4.8
stars
29,883 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

JY

Oct 29, 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.

MK

Mar 13, 2024

Cant express how thankful I am to Andrew Ng, literally thought me from start to finish when my school didnt touch about it, learn a lot and decided to use my knowledge and apply to real world projects

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1376 - 1400 of 3,624 Reviews for Sequence Models

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.

By Hendri S W

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Dec 6, 2018

A comprehensive review of recurrent neural network and sequence models.

By Ram R

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May 20, 2018

Great course! THanks for putting this together for the world to learn!

By Yu Q

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May 6, 2018

Very helpful and practical. Thank you Andrew NG and other teachers/TAs!

By Benji T

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Mar 18, 2018

Quite a heavy course. Still alot of concepts unclear, will revise again

By Jan Z

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Mar 6, 2018

Very informative, concise, well presented, builds necessary intuition.

By Pankaj P

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Feb 19, 2018

Perfect course to understand how does sequence model work in real life.

By Taha A A

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Apr 12, 2023

Actually liked this more than the other courses in the specialization.

By Chandan D

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Jun 28, 2021

This is an amazing course to learn NLP ans seq models. I learnt a lot!

By Bogdan-Ioan O

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Jul 16, 2020

The best attention model explanation I found anywhere on the internet.

By Renzo B

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Nov 19, 2019

Excellent Course! I have nothing more to say. Thank you Sir Andrew Ng!

By Юрий П

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Aug 3, 2019

Exceptional review of the state-of-the-art techniques! Got me inspired

By Ramesh M

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Jun 3, 2019

The best content i can find and its taught by Andrew Ng, my favourite.

By jason z

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Mar 26, 2019

Cover many important topics.

Need more explanation on attention network

By Edwin V

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Mar 24, 2019

Good course. Have a better understanding of RNN now, and terminology.

By jia-you, C L

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Nov 2, 2018

this course is helpful learning basic sequence data,Thanks for courser

By dsp

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Sep 24, 2018

Well structured. Full of good and interesting content. Very enjoyable.

By Andrew B

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Sep 8, 2018

Quick course on Sequential models. Assignments are set up pretty well.

By liangyi

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Jul 26, 2018

Compared to the other four courses,I think this course little abridged

By Robin L

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May 9, 2018

a bit hard, but no one else can explain things like how andrew does it

By Luis A A G

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Apr 13, 2018

This course was really useful, I learned so much, I am really grateful

By Willem v G

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Apr 10, 2018

Excellent course as usual. Very to-the-point and pragmatic instructor.