Sequence Models
Keep adding new skills with 10,000+ programs for $239 (usually $399). Save now.
Sequence Models
This course is part of Deep Learning Specialization
Instructors: Andrew Ng
Top Instructor
461,300 already enrolled
Ask Coursera
31,268 reviews
Recommended experience
31,268 reviews
Recommended experience
Skills you'll gain
Tools you'll learn
Details to know
See how employees at top companies are mastering in-demand skills
Build your subject-matter expertise
- Learn new concepts from industry experts
- Gain a foundational understanding of a subject or tool
- Develop job-relevant skills with hands-on projects
- Earn a shareable career certificate
There are 4 modules in this 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.
Discover recurrent neural networks, a type of model that performs extremely well on temporal data, and several of its variants, including LSTMs, GRUs and Bidirectional RNNs,
What's included
12 videos5 readings1 assignment3 programming assignments
12 videos•Total 112 minutes
- Why Sequence Models?•3 minutes
- Notation•9 minutes
- Recurrent Neural Network Model•17 minutes
- Backpropagation Through Time•6 minutes
- Different Types of RNNs•10 minutes
- Language Model and Sequence Generation•12 minutes
- Sampling Novel Sequences•9 minutes
- Vanishing Gradients with RNNs•6 minutes
- Gated Recurrent Unit (GRU)•17 minutes
- Long Short Term Memory (LSTM)•10 minutes
- Bidirectional RNN•8 minutes
- Deep RNNs•5 minutes
5 readings•Total 10 minutes
- Join the DeepLearning.AI Forum to ask questions, get support, or share amazing ideas!•2 minutes
- Clarifications about Upcoming Gated Recurrent Unit (GRU) Video•1 minute
- Clarifications about Upcoming Long Short Term Memory (LSTM) Video•1 minute
- Lecture Notes W1•1 minute
- (Optional) Downloading your Notebook, Downloading your Workspace and Refreshing your Workspace•5 minutes
1 assignment•Total 50 minutes
- Recurrent Neural Networks •50 minutes
3 programming assignments•Total 540 minutes
- Building your Recurrent Neural Network - Step by Step•180 minutes
- Dinosaur Island-Character-Level Language Modeling •180 minutes
- Jazz Improvisation with LSTM•180 minutes
Natural language processing with deep learning is a powerful combination. Using word vector representations and embedding layers, train recurrent neural networks with outstanding performance across a wide variety of applications, including sentiment analysis, named entity recognition and neural machine translation.
What's included
10 videos2 readings1 assignment2 programming assignments
10 videos•Total 99 minutes
- Word Representation•10 minutes
- Using Word Embeddings•9 minutes
- Properties of Word Embeddings•11 minutes
- Embedding Matrix•4 minutes
- Learning Word Embeddings•10 minutes
- Word2Vec•13 minutes
- Negative Sampling•12 minutes
- GloVe Word Vectors•11 minutes
- Sentiment Classification•8 minutes
- Debiasing Word Embeddings•11 minutes
2 readings•Total 2 minutes
- Clarifications about Upcoming GloVe Word Vectors Video •1 minute
- Lecture Notes W2•1 minute
1 assignment•Total 50 minutes
- Natural Language Processing & Word Embeddings •50 minutes
2 programming assignments•Total 360 minutes
- Operations on Word Vectors - Debiasing•180 minutes
- Emojify•180 minutes
Augment your sequence models using an attention mechanism, an algorithm that helps your model decide where to focus its attention given a sequence of inputs. Then, explore speech recognition and how to deal with audio data.
What's included
10 videos2 readings1 assignment2 programming assignments
10 videos•Total 98 minutes
- Basic Models•6 minutes
- Picking the Most Likely Sentence•9 minutes
- Beam Search•12 minutes
- Refinements to Beam Search•10 minutes
- Error Analysis in Beam Search•10 minutes
- Bleu Score (Optional)•16 minutes
- Attention Model Intuition•10 minutes
- Attention Model•12 minutes
- Speech Recognition•9 minutes
- Trigger Word Detection•5 minutes
2 readings•Total 11 minutes
- Clarifications about Upcoming Attention Model Video •10 minutes
- Lecture Notes W3•1 minute
1 assignment•Total 50 minutes
- Sequence Models & Attention Mechanism •50 minutes
2 programming assignments•Total 360 minutes
- Neural Machine Translation•180 minutes
- Trigger Word Detection•180 minutes
What's included
5 videos5 readings1 assignment1 programming assignment3 ungraded labs
5 videos•Total 42 minutes
- Transformer Network Intuition•5 minutes
- Self-Attention•12 minutes
- Multi-Head Attention•8 minutes
- Transformer Network•14 minutes
- Conclusion and Thank You!•3 minutes
5 readings•Total 33 minutes
- Lecture Notes W4•1 minute
- [IMPORTANT] Reminder about end of access to Lab Notebooks•2 minutes
- References•10 minutes
- Acknowledgments•10 minutes
- (Optional) Opportunity to Mentor Other Learners•10 minutes
1 assignment•Total 50 minutes
- Transformers •50 minutes
1 programming assignment•Total 180 minutes
- Transformers Architecture with TensorFlow•180 minutes
3 ungraded labs•Total 180 minutes
- Transformer Pre-processing•60 minutes
- Transformer Network Application: Named-Entity Recognition•60 minutes
- Transformer Network Application: Question Answering•60 minutes
Earn a career certificate
Add this credential to your LinkedIn profile, resume, or CV. Share it on social media and in your performance review.
Instructors
Offered by
Explore more from Machine Learning
- Status: Free TrialD
DeepLearning.AI
Course
- Status: Free TrialD
DeepLearning.AI
Course
- Status: Free Trial
- Status: Free TrialD
DeepLearning.AI
Specialization
Why people choose Coursera for their career
Learner reviews
- 5 stars
83.77%
- 4 stars
12.87%
- 3 stars
2.56%
- 2 stars
0.47%
- 1 star
0.31%
Showing 3 of 31268
Reviewed on Feb 14, 2020
One of the best thing from this class is not only we can understand the concept of RNN, LSTM, etc, but also I also get the idea about how these technique can be used in many daily life applications
Reviewed on Sep 27, 2018
Great hands on instruction on how RNNs work and how they are used to solve real problems. It was particularly useful to use Conv1D, Bidirectional and Attention layers into RNNs and see how they work.
Reviewed on Sep 21, 2024
Could have been more polished like the earlier courses in the deep learning specialization. Particularly the programming exercises could have benefitted from more comments like in earlier courses.
Frequently asked questions
To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. 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.
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.
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
Financial aid available,