This course is part of the Deep Learning Specialization

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Andrew Ng +2 more instructors

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Deep Learning SpecializationDeepLearning.AI

About this Course

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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.

Flexible deadlines

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Shareable Certificate

Earn a Certificate upon completion

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Course 5 of 5 in the

Deep Learning Specialization

Intermediate Level

Intermediate Python skills: basic programming, understanding of for loops, if/else statements, data structures
A basic grasp of linear algebra & ML

Approx. 38 hours to complete

English

Skills you will gain

Natural Language Processing
Long Short Term Memory (LSTM)
Gated Recurrent Unit (GRU)
Recurrent Neural Network
Attention Models

Flexible deadlines

Reset deadlines in accordance to your schedule.

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Course 5 of 5 in the

Deep Learning Specialization

Intermediate Level

Intermediate Python skills: basic programming, understanding of for loops, if/else statements, data structures
A basic grasp of linear algebra & ML

Approx. 38 hours to complete

English

Instructors

Instructor rating

4.92/5 (2,274 Ratings)

Offered by

DeepLearning.AI

Syllabus - What you will learn from this course

Content Rating94%(40,142 ratings)

Week

Week 1

12 hours to complete

Recurrent Neural Networks

12 hours to complete

12 videos (Total 112 min), 4 readings, 6 quizzes

12 videos

Why Sequence Models?2m

Notation8m

Recurrent Neural Network Model16m

Backpropagation Through Time6m

Different Types of RNNs9m

Language Model and Sequence Generation12m

Sampling Novel Sequences8m

Vanishing Gradients with RNNs6m

Gated Recurrent Unit (GRU)16m

Long Short Term Memory (LSTM)9m

Bidirectional RNN8m

Deep RNNs5m

4 readings

Clarifications about Upcoming Gated Recurrent Unit (GRU) Video1m

Clarifications about Upcoming Long Short Term Memory (LSTM) Video1m

How to Download your Notebook2m

How to Refresh your Workspace5m

1 practice exercise

Recurrent Neural Networks 50m

Week

Week 2

9 hours to complete

Natural Language Processing & Word Embeddings

9 hours to complete

10 videos (Total 99 min), 1 reading, 4 quizzes

Week

Week 3

9 hours to complete

Sequence Models & Attention Mechanism

9 hours to complete

10 videos (Total 98 min), 3 readings, 4 quizzes

10 videos

Basic Models6m

Picking the Most Likely Sentence8m

Beam Search11m

Refinements to Beam Search10m

Error Analysis in Beam Search9m

Bleu Score (Optional)15m

Attention Model Intuition9m

Attention Model12m

Speech Recognition8m

Trigger Word Detection4m

3 readings

Clarifications about Upcoming Bleu Score (Optional) Video 1m

Clarifications about Upcoming Attention Model Video 10m

Instructions If You Are Unable to Open Your Notebook10m

1 practice exercise

Sequence Models & Attention Mechanism 50m

Week

Week 4

8 hours to complete

Transformer Network

8 hours to complete

5 videos (Total 42 min), 4 readings, 3 quizzes

Reviews

4.8

3517 reviews

5 stars
83.58%
4 stars
13.07%
3 stars
2.56%
2 stars
0.48%
1 star
0.28%

TOP REVIEWS FROM SEQUENCE MODELS

by PGJan 25, 2019

This was a tough one. The specialization is well structured and slowly progresses in terms of complexity. Having worked on RNN, i thought I would ace the projects. Different story though at the end

by NMFeb 20, 2018

Hope can elaborate the backpropagation of RNN much more. BP through time is a bit tricky though we do not need to think about it during implementation using most of existing deep learning frameworks.

by WKMar 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!

by AMJul 4, 2018

Excellent course! This course extensively covers all of the relevant areas of NLP with a strong practical element allowing you to applying Deep Learning for Sequence Models in real-world scenarios.

View all reviews

About the Deep Learning Specialization

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.

In this Specialization, you will build and train neural network architectures such as Convolutional Neural Networks, Recurrent Neural Networks, LSTMs, Transformers, and learn how to make them better with strategies such as Dropout, BatchNorm, Xavier/He initialization, and more. Get ready to master theoretical concepts and their industry applications using Python and TensorFlow and tackle real-world cases such as speech recognition, music synthesis, chatbots, machine translation, natural language processing, and more. AI is transforming many industries. The Deep Learning Specialization 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. Along the way, you will also get career advice from deep learning experts from industry and academia.

Frequently Asked Questions

When will I have access to the lectures and assignments?
Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:
The course may not offer an audit option. You can try a Free Trial instead, or apply for Financial Aid.
The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
What will I get if I subscribe to this Specialization?
When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.
Is financial aid available?
Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

More questions? Visit the Learner Help Center.

Sequence Models

About this Course

Skills you will gain

Instructors

Andrew Ng
Top Instructor

Kian Katanforoosh
Top Instructor

Younes Bensouda Mourri
Top Instructor

Offered by

DeepLearning.AI

Syllabus - What you will learn from this course

Recurrent Neural Networks

Natural Language Processing & Word Embeddings

Sequence Models & Attention Mechanism

Transformer Network

Reviews

TOP REVIEWS FROM SEQUENCE MODELS

About the Deep Learning Specialization

Frequently Asked Questions

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Sequence Models

About this Course

Skills you will gain

Instructors

Andrew NgTop Instructor

Kian KatanforooshTop Instructor

Younes Bensouda MourriTop Instructor

Offered by

DeepLearning.AI

Syllabus - What you will learn from this course

Recurrent Neural Networks

Natural Language Processing & Word Embeddings

Sequence Models & Attention Mechanism

Transformer Network

Reviews

TOP REVIEWS FROM SEQUENCE MODELS

About the Deep Learning Specialization

Frequently Asked Questions

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Start or advance your career

Popular Courses and Certifications

Popular collections and articles

Earn a degree or certificate online

Coursera

Community

More

Andrew Ng
Top Instructor

Kian Katanforoosh
Top Instructor

Younes Bensouda Mourri
Top Instructor