Sequence Models from Coursera

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.

What's inside

Syllabus

Recurrent Neural Networks

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,

Natural Language Processing & Word Embeddings

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.

Sequence Models & Attention Mechanism

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.

Transformer Network

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Suitable for learners with some machine learning knowledge

Suitable for learners with some programming experience

Taught by experienced instructors in the field of deep learning

Could be a good fit for learners interested in natural language processing

May appeal to learners interested in developing skills for AI technology

Course assumes learners have some background in AI technology

Reviews summary

Sequence models

Learners say this 4-8 week course in Sequence Models is largely positive, engaging, and challenging. According to students, you will explore transformer networks, sequence models, deep learning applications, and more. Andrew Ng and the deeplearning.ai team introduce LSTMs, RNNs, word embeddings, and other sequence model building blocks. This course is the last in a five-part deep learning specialization. Learners with at least some Python experience and a background in deep learning principles may be ready for this course. However, it's important to approach this subject with patience because the concepts can be complex. Assessments include quizzes and programming assignments that help reinforce the material. Note that the auto-grader can be overly strict at times, but don't get discouraged. Overall, students say this course is an excellent overview of sequence models.

I really liked it, however I don't feel like it really went into some of the more practicle issues with sequence models. I was left feeling like I wouldn't really know what to do in a situation where I had highly variable sequence lengths.

"I really liked it"

This course makes you have great understanding around the Sequence Models such as GRU, LSTM, Word Triggered, Word Sampling, Translation using deep learning algorithm . Andrew did a fantastic job and keep everything simple so that everything can be understandable.

"This course makes you have great understanding around the Sequence Models such as GRU, LSTM, Word Triggered, Word Sampling, Translation using deep learning algorithm ."

The final course in this specialization was a great journey. Thank you Sir Andrew NG for making such a wonderful course. Thank you coursera for giving us a great platform. Talking about course, it was a great course to learn NLP from scratch.

"This was the last course of specialization and it was a great journey."

"Talking about course, it was a great course to learn NLP from scratch."

programming assignment ins WEEK 1 was a bit ambiguous in nature. helped me improve my debugging skills. Also a huge thank you to MENTOR Mr. GEOFF for the instant support to all my queries. His way of providing HINTS lead me to finally complete the course.

"programming assignment ins WEEK 1 was a bit ambiguous in nature."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Sequence Models with these activities:

Read 'Deep Learning for Natural Language Processing' by Jason Brownlee

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This book provides a comprehensive overview of NLP techniques and applications.

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Read the book.
Complete the exercises and assignments in the book.

Review basic concepts of natural language processing

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This activity will provide you with a better foundation for understanding the course concepts.

Browse courses on Natural Language Processing

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Review the course syllabus and identify the key concepts of natural language processing.
Read a book or article on NLP.
Complete online tutorials or exercises on NLP.

Follow a tutorial to build a neural machine translation model

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This activity will provide you with step-by-step guidance on building a neural machine translation model.

Browse courses on Neural Machine Translation

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Find a tutorial on building a neural machine translation model.
Follow the tutorial and build the model.
Test and evaluate your model.

Five other activities

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Show all eight activities

Join a study group or online forum for NLP enthusiasts

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This activity will allow you to interact with other students and experts in the field.

Browse courses on Natural Language Processing

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Find a study group or online forum for NLP enthusiasts.
Participate in discussions and ask questions.
Help other students with their questions.

Build a chatbot using a pre-trained language model

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This activity will help you gain hands-on experience with building and training NLP models.

Browse courses on Chatbots

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Choose a pre-trained language model.
Create a dataset of questions and answers.
Train the language model using your dataset.
Test and evaluate your chatbot.

Contribute to an open-source NLP project

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This activity will provide you with experience in contributing to the NLP community.

Browse courses on Natural Language Processing

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Find an open-source NLP project that you are interested in.
Review the project's documentation and code.
Identify a way to contribute to the project.
Make your contribution to the project.

Build a music recommendation system using recurrent neural networks

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This project will provide you with experience in designing, implementing, and deploying a real-world NLP application.

Browse courses on Recurrent Neural Networks

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Collect a dataset of music tracks.
Preprocess the data and extract features.
Build a recurrent neural network model for music recommendation.
Train and evaluate your model.
Deploy your model and make it available to users.

Write a research paper on a topic related to sequence models

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This activity will demonstrate your understanding of the course concepts and allow you to contribute to the field of NLP.

Browse courses on Sequence Models

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Choose a topic and conduct a literature review.
Develop a hypothesis and design your research.
Collect and analyze data.
Write your research paper.

Career center

Learners who complete Sequence Models will develop knowledge and skills that may be useful to these careers:

Speech Recognition Engineer

Speech Recognition Engineers design, develop, and deploy machine learning models for speech recognition tasks. This course will be particularly useful for a Speech Recognition Engineer who works on developing speech recognition systems for use in applications such as voice assistants, customer service chatbots, or medical transcription. The course covers a variety of sequence models, including recurrent neural networks, attention mechanisms, and transformer networks. By taking this course, Speech Recognition Engineers can gain the skills and knowledge necessary to develop and deploy more effective and accurate machine learning models.

See salaries and explore the career path for Speech Recognition Engineer

Natural Language Processing Engineer

Natural Language Processing Engineers design, develop, and deploy machine learning models for natural language processing tasks. This course will be particularly useful for a Natural Language Processing Engineer who works on developing chatbots, machine translation systems, or other natural language processing applications. The course covers a variety of sequence models, including recurrent neural networks, attention mechanisms, and transformer networks. By taking this course, Natural Language Processing Engineers can gain the skills and knowledge necessary to develop and deploy more effective and accurate machine learning models.

See salaries and explore the career path for Natural Language Processing Engineer

Machine Learning Engineer

Machine Learning Engineers design, develop, and deploy machine learning models to solve real-world problems. This course will be particularly useful for a Machine Learning Engineer who works on natural language processing or speech recognition tasks. The course covers a variety of sequence models, including recurrent neural networks, attention mechanisms, and transformer networks, and provides hands-on experience with building and training these models on real-world datasets. By taking this course, Machine Learning Engineers can gain the skills and knowledge necessary to develop and deploy more effective and accurate machine learning models.

See salaries and explore the career path for Machine Learning Engineer

Computational Linguist

Computational Linguists use computer science and linguistics to study language. This course will be particularly useful for a Computational Linguist who works on developing natural language processing or speech recognition applications. The course covers a variety of sequence models, including recurrent neural networks, attention mechanisms, and transformer networks, and provides hands-on experience with building and training these models on real-world datasets. By taking this course, Computational Linguists can gain the skills and knowledge necessary to develop and deploy more effective and accurate machine learning models.

See salaries and explore the career path for Computational Linguist

Research Scientist

Research Scientists design, conduct, and interpret scientific research in order to advance knowledge and solve problems. This course will be particularly useful for a Research Scientist who uses machine learning algorithms to perform natural language processing and speech recognition, as it provides a comprehensive overview of sequence models. The course covers recurrent neural networks, attention mechanisms, and transformer networks, which are all essential techniques for working with sequential data. By taking this course, Research Scientists can gain the skills and knowledge necessary to develop more effective and accurate machine learning models.

See salaries and explore the career path for Research Scientist

Data Scientist

Data Scientists use data to solve business problems. This course will be particularly useful for a Data Scientist who works on natural language processing or speech recognition tasks. The course covers a variety of sequence models, including recurrent neural networks, attention mechanisms, and transformer networks, and provides hands-on experience with building and training these models on real-world datasets. By taking this course, Data Scientists can gain the skills and knowledge necessary to develop and deploy more effective and accurate machine learning models.

See salaries and explore the career path for Data Scientist

Product Manager

Product Managers are responsible for managing the development and launch of new products. This course may be useful for a Product Manager who works on developing natural language processing or speech recognition products. The course covers a variety of sequence models, including recurrent neural networks, attention mechanisms, and transformer networks, and provides hands-on experience with building and training these models on real-world datasets. By taking this course, Product Managers can gain the skills and knowledge necessary to develop and launch more successful products.

See salaries and explore the career path for Product Manager

Software Engineer

Software Engineers design, develop, and maintain software systems. This course may be useful for a Software Engineer who works on developing natural language processing or speech recognition applications. The course covers a variety of sequence models, including recurrent neural networks, attention mechanisms, and transformer networks, and provides hands-on experience with building and training these models. By taking this course, Software Engineers can gain the skills and knowledge necessary to develop more effective and accurate machine learning models.

See salaries and explore the career path for Software Engineer

Consultant

Consultants provide advice and guidance to businesses on a variety of topics. This course may be useful for a Consultant who works with businesses on projects involving natural language processing or speech recognition. The course covers a variety of sequence models, including recurrent neural networks, attention mechanisms, and transformer networks, and provides hands-on experience with building and training these models on real-world datasets. By taking this course, Consultants can gain the skills and knowledge necessary to provide more informed advice to their clients.

See salaries and explore the career path for Consultant

Business Analyst

Business Analysts help businesses improve their operations and make better decisions. This course may be useful for a Business Analyst who works on projects involving natural language processing or speech recognition. The course covers a variety of sequence models, including recurrent neural networks, attention mechanisms, and transformer networks, and provides hands-on experience with building and training these models on real-world datasets. By taking this course, Business Analysts can gain the skills and knowledge necessary to develop more effective and accurate machine learning models for their projects.

See salaries and explore the career path for Business Analyst

Freelancer

Freelancers provide services to clients on a project-by-project basis. This course may be useful for a Freelancer who wants to offer services in the field of natural language processing or speech recognition. The course covers a variety of sequence models, including recurrent neural networks, attention mechanisms, and transformer networks, and provides hands-on experience with building and training these models on real-world datasets. By taking this course, Freelancers can gain the skills and knowledge necessary to provide valuable services to their clients.

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Technical Writer

Technical Writers create documentation and other materials to explain complex technical concepts. This course may be useful for a Technical Writer who needs to write about natural language processing or speech recognition. The course covers a variety of sequence models, including recurrent neural networks, attention mechanisms, and transformer networks, and provides hands-on experience with building and training these models on real-world datasets. By taking this course, Technical Writers can gain the skills and knowledge necessary to write more accurate and informative documentation.

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Entrepreneur

Entrepreneurs start and run their own businesses. This course may be useful for an Entrepreneur who wants to start a business in the field of natural language processing or speech recognition. The course covers a variety of sequence models, including recurrent neural networks, attention mechanisms, and transformer networks, and provides hands-on experience with building and training these models on real-world datasets. By taking this course, Entrepreneurs can gain the skills and knowledge necessary to develop and launch a successful business.

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Teacher

Teachers educate students in a variety of subjects. This course may be useful for a Teacher who teaches computer science or a related field. The course covers a variety of sequence models, including recurrent neural networks, attention mechanisms, and transformer networks, and provides hands-on experience with building and training these models on real-world datasets. By taking this course, Teachers can gain the skills and knowledge necessary to teach their students about the latest advances in machine learning.

See salaries and explore the career path for Teacher

Researcher

Researchers conduct original research in a variety of fields. This course may be useful for a Researcher who works on natural language processing or speech recognition. The course covers a variety of sequence models, including recurrent neural networks, attention mechanisms, and transformer networks, and provides hands-on experience with building and training these models on real-world datasets. By taking this course, Researchers can gain the skills and knowledge necessary to conduct more effective research.

See salaries and explore the career path for Researcher

Reading list

We've selected seven books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Sequence Models.

Cross-Lingual Word Embeddings

Save

Provides a comprehensive overview of deep learning for natural language processing, including the theoretical foundations and practical applications. It valuable resource for anyone interested in learning more about deep learning for natural language processing.

Cross-Lingual Word Embeddings (Synthesis Lectures...

Paperback

Deep Learning

Save

Provides a comprehensive overview of deep learning, including the theoretical foundations and practical applications. It valuable resource for anyone interested in learning more about deep learning.

Deep Learning

Paperback

$$$

Deep Learning (Adaptive Computation and Machine...

Hardcover

$$$

Deep Learning (Adaptive Computation and Machine...

Kindle Edition

$$$

Supervised Sequence Labelling with Recurrent Neural...

Save

Provides a comprehensive overview of recurrent neural networks, including the theoretical foundations and practical applications. It valuable resource for anyone interested in learning more about recurrent neural networks.

Supervised Sequence Labelling with Recurrent Neural...

Paperback

$$$$

Natural Language Processing with Transformers,...

Save

Provides a comprehensive overview of natural language processing with transformers, including the theoretical foundations and practical applications. It valuable resource for anyone interested in learning more about natural language processing with transformers.

Natural Language Processing with Transformers,...

Paperback

Natural Language Processing with Transformers,...

Kindle Edition

Neural Network Methods for Natural Language...

Save

Provides a comprehensive overview of natural language processing with deep learning, including the theoretical foundations and practical applications. It valuable resource for anyone interested in learning more about natural language processing with deep learning.

Neural Network Methods for Natural Language...

Paperback

Speech and Language Processing. An Introduction to...

Save

Provides a comprehensive overview of speech and language processing, including the theoretical foundations and practical applications. It valuable resource for anyone interested in learning more about speech and language processing.

Speech and Language Processing An Introduction to...

Paperback

Machine Learning for Hackers

Save

Provides a practical introduction to machine learning, with a focus on using Python to build real-world applications. It valuable resource for anyone interested in learning more about machine learning.

Machine Learning for Hackers: Case Studies and...

Paperback

Machine Learning for Hackers: Case Studies and...

Kindle Edition

Sequence Models

What's inside

Syllabus

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Reviews summary

Sequence models

Activities

Career center

Featured in The Course Notes

Reading list

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