About this Course

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Learner Career Outcomes

39%

started a new career after completing these courses

40%

got a tangible career benefit from this course

13%

got a pay increase or promotion

100% online

Start instantly and learn at your own schedule.

Course 5 of 5 in the

Flexible deadlines

Reset deadlines in accordance to your schedule.

Intermediate Level

Approx. 18 hours to complete

Suggested: 11 hours/week...

English

Subtitles: Chinese (Traditional), Chinese (Simplified), Korean, English, Spanish

Skills you will gain

Recurrent Neural NetworkArtificial Neural NetworkDeep LearningLong Short-Term Memory (ISTM)

Learner Career Outcomes

39%

started a new career after completing these courses

40%

got a tangible career benefit from this course

13%

got a pay increase or promotion

100% online

Start instantly and learn at your own schedule.

Course 5 of 5 in the

Flexible deadlines

Reset deadlines in accordance to your schedule.

Intermediate Level

Approx. 18 hours to complete

Suggested: 11 hours/week...

English

Subtitles: Chinese (Traditional), Chinese (Simplified), Korean, English, Spanish

Syllabus - What you will learn from this course

Content RatingThumbs Up94%(20,709 ratings)Info
Week
1

Week 1

6 hours to complete

Recurrent Neural Networks

6 hours to complete
12 videos (Total 112 min), 2 readings, 4 quizzes
12 videos
Notation9m
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)17m
Long Short Term Memory (LSTM)9m
Bidirectional RNN8m
Deep RNNs5m
2 readings
Gated Recurrent Unit (GRU) *CORRECTION*1m
Long Short Term Memory (LSTM) *CORRECTION*1m
1 practice exercise
Recurrent Neural Networks30m
Week
2

Week 2

4 hours to complete

Natural Language Processing & Word Embeddings

4 hours to complete
10 videos (Total 102 min), 1 reading, 3 quizzes
10 videos
Using word embeddings9m
Properties of word embeddings11m
Embedding matrix5m
Learning word embeddings10m
Word2Vec12m
Negative Sampling11m
GloVe word vectors11m
Sentiment Classification7m
Debiasing word embeddings11m
1 reading
GloVe word vectors *CORRECTION*1m
1 practice exercise
Natural Language Processing & Word Embeddings30m
Week
3

Week 3

5 hours to complete

Sequence models & Attention mechanism

5 hours to complete
11 videos (Total 103 min), 3 readings, 3 quizzes
11 videos
Picking the most likely sentence8m
Beam Search11m
Refinements to Beam Search11m
Error analysis in beam search9m
Bleu Score (optional)16m
Attention Model Intuition9m
Attention Model12m
Speech recognition8m
Trigger Word Detection5m
Conclusion and thank you2m
3 readings
Bleu Score *CORRECTION*1m
Corrections10m
Instructions if you are unable to open your notebook10m
1 practice exercise
Sequence models & Attention mechanism30m
4.8
2,101 ReviewsChevron Right

Top reviews from Sequence Models

By WKMar 14th 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!

By AMJul 1st 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.

Instructors

Instructor rating4.91/5 (446 Ratings)Info
Image of instructor, Andrew Ng

Andrew Ng 

CEO/Founder Landing AI; Co-founder, Coursera; Adjunct Professor, Stanford University; formerly Chief Scientist,Baidu and founding lead of Google Brain
3,131,957 Learners
11 Courses
Image of instructor, Head Teaching Assistant - Kian Katanforoosh

Head Teaching Assistant - Kian Katanforoosh 

Lecturer of Computer Science at Stanford University, deeplearning.ai, Ecole CentraleSupelec
555,579 Learners
5 Courses
Image of instructor, Teaching Assistant - Younes Bensouda Mourri

Teaching Assistant - Younes Bensouda Mourri 

Mathematical & Computational Sciences, Stanford University, deeplearning.ai
Computer Science
556,030 Learners
6 Courses

Offered by

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deeplearning.ai

About the Deep Learning Specialization

If you want to break into AI, this Specialization will help you do so. Deep Learning is one of the most highly sought after skills in tech. We will help you become good at Deep Learning. In five courses, you will learn the foundations of Deep Learning, understand how to build neural networks, and learn how to lead successful machine learning projects. You will learn about Convolutional networks, RNNs, LSTM, Adam, Dropout, BatchNorm, Xavier/He initialization, and more. You will work on case studies from healthcare, autonomous driving, sign language reading, music generation, and natural language processing. You will master not only the theory, but also see how it is applied in industry. You will practice all these ideas in Python and in TensorFlow, which we will teach. You will also hear from many top leaders in Deep Learning, who will share with you their personal stories and give you career advice. AI is transforming multiple industries. After finishing this specialization, you will likely find creative ways to apply it to your work. We will help you master Deep Learning, understand how to apply it, and build a career in AI....
Deep Learning

Frequently Asked Questions

  • Once you enroll for a Certificate, you’ll have access to all videos, quizzes, and programming assignments (if applicable). Peer review assignments can only be submitted and reviewed once your session has begun. If you choose to explore the course without purchasing, you may not be able to access certain assignments.

  • 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.

More questions? Visit the Learner Help Center.