Natural Language Processing with Attention Models from Coursera

In Course 4 of the Natural Language Processing Specialization, you will:

a) Translate complete English sentences into German using an encoder-decoder attention model,

b) Build a Transformer model to summarize text,

c) Use T5 and BERT models to perform question-answering, and

d) Build a chatbot using a Reformer model.

By the end of this Specialization, you will have designed NLP applications that perform question-answering and sentiment analysis, created tools to translate languages and summarize text, and even built a chatbot!

Learners should have a working knowledge of machine learning, intermediate Python including experience with a deep learning framework (e.g., TensorFlow, Keras), as well as proficiency in calculus, linear algebra, and statistics. Please make sure that you’ve completed course 3 - Natural Language Processing with Sequence Models - before starting this course.

This Specialization is designed and taught by two experts in NLP, machine learning, and deep learning. Younes Bensouda Mourri is an Instructor of AI at Stanford University who also helped build the Deep Learning Specialization. Łukasz Kaiser is a Staff Research Scientist at Google Brain and the co-author of Tensorflow, the Tensor2Tensor and Trax libraries, and the Transformer paper.

What's inside

Syllabus

Neural Machine Translation

Discover some of the shortcomings of a traditional seq2seq model and how to solve for them by adding an attention mechanism, then build a Neural Machine Translation model with Attention that translates English sentences into German.

Text Summarization

Compare RNNs and other sequential models to the more modern Transformer architecture, then create a tool that generates text summaries.

Question Answering

Explore transfer learning with state-of-the-art models like T5 and BERT, then build a model that can answer questions.

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Develops a foundation in natural language processing

Improves existing foundation in natural language processing

Taught by experts in the field

Provides practical experience through projects

Covers industry-standard tools and techniques

Requires prerequisite knowledge of certain modeling techniques

Coursework may require access to additional resources

Limited opportunities for direct feedback from instructors during lessons

Reviews summary

Nlp attention models: transformers and beyond

These reviews reveal learners say this Deeplearning.ai course offers a largely positive overview of attention models, particularly transformers and reformed architectures. Taught by Google AI researchers Younes Mourri and Łukasz Kaiser, along with Trax creator and Google Brain researcher Eddy Britton, many learners praise the course for its state-of-the-art content. Several note that concepts like attention mechanisms, transformer models, and reformer models are complex but taught clearly and methodically. Some higher-rated reviews find the intuitive explanations, diagrams, and engaging assignments support their understanding. Others find the detailed instruction in graded and ungraded assignments aids their learning. While the course uses the Trax framework, introduced by Google Brain, highly rated reviews generally find it easy to implement transformers using this framework. One area of frequent criticism is the video lectures, which some learners find too short to address the complexity of the material, overly simplistic, or confusing. Several reviewers also note errors in the material. Despite the issues some learners find with the video lectures, many rate the course highly and recommend it. These reviewers often note that the assignments and notebook exercises compensate for any shortcomings in the video content. Others also appreciate the exposure to the Trax framework and the opportunity to learn from Google AI researchers. Overall, these reviews indicate that learners with a foundation in NLP will likely find this course instructive and engaging. However, those seeking a comprehensive grounding in attention models may want to consider alternative resources.

State-of-the-art NLP techniques using transformer and reformer models

"This course has helped me a lot in developing my NLP skills and now I am confident that I can solve NLP problems easily because both the instructors Younes and Luckerz has thought this course in a way that it can be absorbed in any NLP problem."

Clear explanations of complex NLP concepts, particularly for transformer models

"The lessons need more insights to understand not only the 'how' but a reasonable amount of the 'why', too."

"The instructors were fully prepared though I'd prefer to see more animations in following courses."

Engaging and instructive programming assignments and notebook exercises that enhance learning

"Compared to Andrew Ng's deep learning specialization, this course requires a lot of improvement. Very often disparate facts are put together with not much connection between the ideas. This is probably because of the enormous amount of content covered."

"I liked the BERT sections and references to the theory behind positional encoders"

"Amazing. Got the hard topics, very clear description. Huge Thanks and shoutout :)"

"The most comprehensive course on NLP with challenging quizzes around"

"The course is very good, if we download powerpoint and files in jupyter notebook, that will be great."

"The course was wonderful, full of updated content and explained in a really good way."

"I liked the course. If we download powerpoint and files in jupyter notebook, that will be great."

"The course is good. If we download powerpoint and files in jupyter notebook, that will be great."

Focus on the Trax deep learning framework, which may not align with all career goals

"Although this course gives you understanding about the cutting edge NLP models it lacks details. It is hard to understand a structure of the complex NLP model during the few minute video."

"Some of the items (e.g. what each layers does and why do we need that layer) were not properly explained."

"The course is very educational! I learned a lot about the different NLP models."

Inconsistent quality of video lectures, with varying clarity and depth

"This course glossed over everything and as a result I learned pretty much nothing."

"The presented concepts are quite complex - I would prefer less details as most will not understand them anyway and more conceptual information why these models are build as they are"

"The lecture videos were very confusing, and the course assignments were too easy, so they didn't reinforce the lecture concepts in the same way that assignments from other courses had."

"The course lectures were very confusing, and the course assignments were too easy, so they didn't reinforce the lecture concepts in the same way that assignments from other courses had."

"The videos need more explanation. Even the assignments were quite challenging because of 'trax'"

"The lectures need more insights to understand not only the 'how' but a reasonable amount of the 'why', too."

"The assignments originally taught by Andrew were for me much better. Many of the explanations in this course were not very clear and superficial as I see it."

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 Natural Language Processing with Attention Models with these activities:

Watch video tutorials on NLP

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Watching video tutorials on NLP will help you learn about the latest NLP techniques and how to apply them in practice.

Browse courses on NLP

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Find a video tutorial on NLP that you are interested in
Watch the tutorial and take notes

Review the basics of Python

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Reviewing the basics of Python will help you refresh your knowledge and ensure that you have a strong foundation for the course.

Browse courses on Python

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Go through a Python tutorial
Solve some basic Python coding challenges

Read 'Natural Language Processing with Python'

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Reading 'Natural Language Processing with Python' will help you learn about the foundations of NLP and how to apply them in practice.

View Natural Language Processing with Python:... on Amazon

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Read the book cover-to-cover
Do the exercises in the book

Five other activities

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Join a study group for the course

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Joining a study group for the course will help you learn from your peers and improve your understanding of the material.

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Find a study group for the course
Attend study group meetings regularly

Practice translating sentences using an encoder-decoder attention model

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Practicing translating sentences using an encoder-decoder attention model will help you develop the skills necessary to build your own neural machine translation model.

Browse courses on Neural Machine Translation

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Find a dataset of English-German sentence pairs
Build an encoder-decoder attention model in Python
Train the model on the dataset
Evaluate the model on a held-out set

Build a text summarization tool

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Building a text summarization tool will help you understand the concepts of text summarization and how to apply them in practice.

Browse courses on Text Summarization

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Choose a text summarization algorithm
Implement the algorithm in Python
Create a user interface for the tool
Test the tool on a variety of texts

Build a question-answering model

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Building a question-answering model will help you understand the concepts of question-answering and how to apply them in practice.

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Choose a question-answering dataset
Fine-tune a pre-trained language model on the dataset
Evaluate the model on a held-out set
Deploy the model as a web service

Contribute to an open-source NLP project

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Contributing to an open-source NLP project will help you learn about the latest NLP techniques and how to apply them in practice.

Browse courses on Open Source

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Find an open-source NLP project that you are interested in
Read the project documentation and contribute code
Submit a pull request to the project

Career center

Learners who complete Natural Language Processing with Attention Models will develop knowledge and skills that may be useful to these careers:

Natural Language Processing Engineer

Natural Language Processing Engineers design and develop systems that enable computers to comprehend and interpret human language in a meaningful manner. This course can be particularly useful to those working in this role by bolstering their knowledge of attention models, which have advanced the field of NLP and improved the performance of NLP systems and applications.

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

Machine Learning Engineer

Machine Learning Engineers design and develop machine learning models and algorithms, which often include NLP models, to solve real world problems. Those in this role may find this course especially helpful for gaining in-depth knowledge about attention models and implementing them in NLP systems.

See salaries and explore the career path for Machine Learning Engineer

Data Scientist

Data Scientists use their expertise in math, statistics, and computer science to extract knowledge from data, often using NLP techniques. This course may be especially valuable to Data Scientists looking to enhance their NLP skill set, particularly in the area of attention models.

See salaries and explore the career path for Data Scientist

Software Engineer

Software Engineers design, develop, test, and maintain software systems. Those specializing in NLP may find this course particularly useful for acquiring knowledge about the latest NLP techniques and models, such as attention models, enabling them to build more effective and efficient NLP systems.

See salaries and explore the career path for Software Engineer

Research Scientist

Research Scientists conduct research and develop new theories and technologies. Those specializing in NLP may find this course especially helpful for gaining knowledge of cutting-edge NLP techniques, including attention models, which can inform their research.

See salaries and explore the career path for Research Scientist

Computational Linguist

Computational Linguists use their knowledge of linguistics and computer science to develop NLP systems. This course can be beneficial for those in this role by providing them with in-depth knowledge of attention models, a fundamental concept in modern NLP.

See salaries and explore the career path for Computational Linguist

Technical Writer

Technical Writers create and edit technical documentation, such as user manuals, white papers, and marketing materials. Those who specialize in NLP may find this course helpful for understanding the underlying concepts and techniques used in NLP, enabling them to write more accurate and informative documentation.

See salaries and explore the career path for Technical Writer

Product Manager

Product Managers oversee the development and launch of new products. Those working on NLP products may find this course useful for gaining a deeper understanding of NLP techniques, including attention models, which can help them make better decisions about product features and roadmaps.

See salaries and explore the career path for Product Manager

Business Analyst

Business Analysts analyze business needs and develop solutions using data and technology. Those specializing in NLP may find this course beneficial for gaining knowledge of NLP techniques, such as attention models, which can help them identify and solve business problems more effectively.

See salaries and explore the career path for Business Analyst

Project Manager

Project Managers plan, execute, and close projects. Those managing NLP projects may find this course helpful for gaining knowledge of NLP techniques, such as attention models, which can enable them to better understand project requirements and manage project risks.

See salaries and explore the career path for Project Manager

Data Architect

Data Architects design and build data architectures for organizations. Those specializing in NLP may find this course beneficial for gaining knowledge of NLP techniques, including attention models, which can help them design and build data architectures that support NLP applications.

See salaries and explore the career path for Data Architect

Information Security Analyst

Information Security Analysts protect organizations from cyber threats. Those specializing in NLP may find this course useful for gaining knowledge of NLP techniques, such as attention models, which can help them develop more effective security solutions.

See salaries and explore the career path for Information Security Analyst

IT Consultant

IT Consultants advise organizations on how to use technology to meet their business needs. Those specializing in NLP may find this course beneficial for gaining knowledge of NLP techniques, such as attention models, which can help them provide better advice to their clients.

See salaries and explore the career path for IT Consultant

UX Designer

UX Designers design user interfaces for websites and applications. Those specializing in NLP may find this course helpful for gaining knowledge of NLP techniques, such as attention models, which can help them design more user-friendly and efficient interfaces for NLP applications.

See salaries and explore the career path for UX Designer

Marketing Manager

Marketing Managers plan and execute marketing campaigns. Those specializing in NLP may find this course useful for gaining knowledge of NLP techniques, such as attention models, which can help them develop more effective marketing campaigns.

See salaries and explore the career path for Marketing Manager

Natural Language Processing with Attention Models

What's inside

Syllabus

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Reviews summary

Nlp attention models: transformers and beyond

Activities

Career center

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