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Younes Bensouda Mourri,
Łukasz Kaiser,
and
Eddy Shyu
In Course 3 of the Natural Language Processing Specialization, you will:
a) Train a neural network with GLoVe word embeddings to perform sentiment analysis of tweets,
b) Generate synthetic Shakespeare text using a Gated Recurrent Unit (GRU) language model,
c) Train a recurrent neural network to perform named entity recognition (NER) using LSTMs with linear layers, and
d) Use so-called ‘Siamese’ LSTM models to compare questions in a corpus and identify those that are worded differently but have the same meaning.
Read more
In Course 3 of the Natural Language Processing Specialization, you will:
a) Train a neural network with GLoVe word embeddings to perform sentiment analysis of tweets,
b) Generate synthetic Shakespeare text using a Gated Recurrent Unit (GRU) language model,
c) Train a recurrent neural network to perform named entity recognition (NER) using LSTMs with linear layers, and
d) Use so-called ‘Siamese’ LSTM models to compare questions in a corpus and identify those that are worded differently but have the same meaning.
Read more
In Course 3 of the Natural Language Processing Specialization, you will:
a) Train a neural network with GLoVe word embeddings to perform sentiment analysis of tweets,
b) Generate synthetic Shakespeare text using a Gated Recurrent Unit (GRU) language model,
c) Train a recurrent neural network to perform named entity recognition (NER) using LSTMs with linear layers, and
d) Use so-called ‘Siamese’ LSTM models to compare questions in a corpus and identify those that are worded differently but have the same meaning.
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!
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.
Register for this course and see more details by visiting:
OpenCourser.com/course/ik5n4x/natural
What's inside
Syllabus
Recurrent Neural Networks for Language Modeling
Learn about the limitations of traditional language models and see how RNNs and GRUs use sequential data for text prediction. Then build your own next-word generator using a simple RNN on Shakespeare text data!
Read more
Syllabus
Recurrent Neural Networks for Language Modeling
Learn about the limitations of traditional language models and see how RNNs and GRUs use sequential data for text prediction. Then build your own next-word generator using a simple RNN on Shakespeare text data!
Read more
LSTMs and Named Entity Recognition
Learn about how long short-term memory units (LSTMs) solve the vanishing gradient problem, and how Named Entity Recognition systems quickly extract important information from text. Then build your own Named Entity Recognition system using an LSTM and data from Kaggle!
Siamese Networks
Learn about Siamese networks, a special type of neural network made of two identical networks that are eventually merged together, then build your own Siamese network that identifies question duplicates in a dataset from Quora.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Introduces Recurrent Neural Networks (RNNs) for Language Modeling, highlighting the advantages of sequential data representation for text prediction
Explores the concept of Long-Short Term Memory (LSTM) units as a solution to vanishing gradient issues, making them particularly useful for Named Entity Recognition (NER) tasks
Utilizes real-world data from Kaggle for hands-on experience in building an LSTM-based NER system, fostering practical application skills
Introduces Siamese networks, a specialized type of neural network architecture for comparing question similarity in a given corpus, emphasizing their utility in identifying duplicates
Provides hands-on experience in building a Siamese network for question similarity identification using a dataset from Quora, reinforcing practical implementation skills
Taught by renowned experts in NLP, machine learning, and deep learning, including Younes Bensouda Mourri and Łukasz Kaiser, ensuring high-quality instruction and industry-relevant insights
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Reviews summary
Innovative nlp for advanced learners
learners say this is a series of courses, specializing in Natural Language Processing with Sequence Models, from Deeplearning.ai, taught by professors from Stanford University. Overall, the course is well received by students and this course in particular is largely positive. The course is composed of short, concise videos, labs, and programming assignments due weekly. The course is well received for using the Trax Framework for Deep Learning to build cool models that solve real world NLP problems. Students report that the assignments are challenging yet helpful, and the course is a great value for the price. There are some complaints that the videos are too short and some assignments are difficult to complete, however, the majority of students recommend this course as a great way to learn about NLP with Sequence Models.
This course is a great value for the price. Learners report that they learn a lot from the course, and they appreciate the opportunity to learn from experts in the field of NLP.
"Excellent class with great assignments with real use case of NLP"
"This is one of the best course on NLP with Deep learning technique. "
"Great course, with clear explanations and well designed assignments. "
The programming assignments in this course are challenging, but they are also very helpful. Learners report that they learn a lot by completing the assignments, and they appreciate the opportunity to apply their new knowledge to real-world problems.
"Great Course as usual with Deeplearning.ai!"
"I enjoyed it, and I had a great intro to Trax"
"Great compact ad very useful course curriculum."
This course is taught using the Trax Framework for Deep Learning. While Trax is not as popular as other frameworks like TensorFlow or PyTorch, learners appreciate using it because it is easy to use and deploy. Trax is also a very powerful framework, allowing learners to build complex models with ease.
"This course very informative on NLP is actual helping me do my next project."
"This course is much more difficult than the 2 previous ones in the series. Not because of the way instructor transferring but in the knowledge itself. Totally worth taking this course"
"I am now confusing by too many Deep learning framework. Also the content is somehow repeated with the Deep learning specialization."
Most students found that the course did not have adequate depth.
"Compared to prior deepLearning Ai courses. the lecturers were very robotic and un natural. The explanations were much less clear and less effort was made to explain the intuitons behind formulas. "
"This course is good for practical knowledge with really good projects but it lags in the theoretical part you must be familiar with the concepts to get the most out of this course."
"I think it should be better if we use TensorFlow 2.x or Pytorch instead of Trax, which is seldom used in other places."
Multiple students noted that the video lectures and course as a whole were too short.
"This course is too short in my opinion."
"Videos are annoyingly short and provide little depth."
"The videos are very short to convey the ideas behind the methodology."
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 Sequence Models with these
activities:
Assemble a resource collection on NLP tools and datasets
Show steps
Curate a comprehensive repository of resources for ongoing NLP exploration.
Browse courses on
Natural Language Processing
Show steps
-
Identify relevant NLP tools and datasets
-
Categorize and organize the resources
Read 'Natural Language Processing with Python'
Show steps
Gain a comprehensive understanding of NLP fundamentals and Python-based implementations.
View
Natural Language Processing with Python:...
on Amazon
Show steps
-
Read chapters on core NLP concepts
-
Work through code examples and exercises
Participate in weekly study group discussions
Show steps
Foster collaboration, enhance understanding, and identify areas for improvement.
Browse courses on
Natural Language Processing
Show steps
-
Prepare for the discussion by reviewing materials
-
Actively participate in discussions
-
Share own insights and perspectives
Four other activities
Expand to see all activities and additional details
Show all seven activities
Follow Tensorflow tutorials on LSTM and Siamese networks
Show steps
Enhance practical skills in implementing advanced NLP models using Tensorflow.
Browse courses on
LSTM
Show steps
-
Complete the LSTM tutorial
-
Complete the Siamese network tutorial
Solve NLP coding challenges on LeetCode
Show steps
Sharpen problem-solving skills and deepen understanding of NLP algorithms.
Browse courses on
Natural Language Processing
Show steps
-
Select LeetCode problems tagged with 'NLP'
-
Attempt to solve the problems
-
Review solutions and discuss with peers
Build a named entity recognition (NER) tool
Show steps
Strengthen understanding of NER concepts and practical implementation in NLP.
Browse courses on
Named Entity Recognition
Show steps
-
Gather and preprocess training data
-
Select and train a suitable NER model
-
Evaluate model performance on test data
-
Deploy the tool for practical use
Develop a sentiment analysis application
Show steps
Integrate NLP techniques with software engineering to create a practical real-world application.
Browse courses on
Sentiment Analysis
Show steps
-
Design the application architecture
-
Implement the NLP model
-
Develop the user interface
-
Deploy the application
Assemble a resource collection on NLP tools and datasets
Show steps
Curate a comprehensive repository of resources for ongoing NLP exploration.
Browse courses on
Natural Language Processing
Show steps
Show steps
Show steps
- Identify relevant NLP tools and datasets
- Categorize and organize the resources
Read 'Natural Language Processing with Python'
Show steps
Gain a comprehensive understanding of NLP fundamentals and Python-based implementations.
View
Natural Language Processing with Python:...
on Amazon
Show steps
Show steps
Show steps
- Read chapters on core NLP concepts
- Work through code examples and exercises
Participate in weekly study group discussions
Show steps
Foster collaboration, enhance understanding, and identify areas for improvement.
Browse courses on
Natural Language Processing
Show steps
Show steps
Show steps
- Prepare for the discussion by reviewing materials
- Actively participate in discussions
- Share own insights and perspectives
Four other activities
Expand to see all activities and additional details
Show all seven activities
Follow Tensorflow tutorials on LSTM and Siamese networks
Show steps
Enhance practical skills in implementing advanced NLP models using Tensorflow.
Browse courses on
LSTM
Show steps
Show steps
Show steps
- Complete the LSTM tutorial
- Complete the Siamese network tutorial
Solve NLP coding challenges on LeetCode
Show steps
Sharpen problem-solving skills and deepen understanding of NLP algorithms.
Browse courses on
Natural Language Processing
Show steps
Show steps
Show steps
- Select LeetCode problems tagged with 'NLP'
- Attempt to solve the problems
- Review solutions and discuss with peers
Build a named entity recognition (NER) tool
Show steps
Strengthen understanding of NER concepts and practical implementation in NLP.
Browse courses on
Named Entity Recognition
Show steps
Show steps
Show steps
- Gather and preprocess training data
- Select and train a suitable NER model
- Evaluate model performance on test data
- Deploy the tool for practical use
Develop a sentiment analysis application
Show steps
Integrate NLP techniques with software engineering to create a practical real-world application.
Browse courses on
Sentiment Analysis
Show steps
Show steps
Show steps
- Design the application architecture
- Implement the NLP model
- Develop the user interface
- Deploy the application
Career center
Learners who complete Natural Language Processing with Sequence Models will develop knowledge and skills
that may be useful to these careers:
Natural Language Processing Engineer
Natural Language Processing Engineer
Natural Language Processing Engineers work on a range of applications, including chatbots, machine translation, and search, teaching your system the fundamentals of Natural Language Processing. It will teach you how to use sequence models to perform sentiment analysis, generate text, perform named entity recognition, and compare questions for duplicates, skills which an NLP Engineer finds crucial in their day to day operations in the workplace.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design, develop, and deploy machine learning models, covering the same theoretical and applied knowledge you will be taught in this course. The course will provide the skills to design, train and evaluate NLP models for a range of tasks.
Data Scientist
Data Scientist
Data Scientists use machine learning to extract insights from data, much like the NLP models built in this course. You will be immersed in the same theories and best practices for building, training and evaluating models, which gives you a solid foundation for a future career in Data Science.
Software Engineer
Software Engineer
Software Engineers develop and maintain software systems, many of which incorporate NLP. The course will teach you about the underlying principles of NLP and how to apply them to real-world problems. This will give you a strong foundation to develop NLP-based software systems in your future career as a Software Engineer.
Research Scientist
Research Scientist
Research Scientists conduct research in a variety of fields, including NLP. This course will introduce you to the fundamental concepts and techniques of NLP, providing you with a solid foundation for further research in this field.
Computational Linguist
Computational Linguist
Computational Linguists use computers to analyze and understand human language, including NLP techniques which you will learn in this course. The knowledge and skills you gain will help you develop computational tools for a variety of NLP tasks, such as machine translation and information extraction.
Business Analyst
Business Analyst
Business Analysts use data to help businesses make better decisions, which often involves NLP. This course will teach you how to use NLP techniques to extract insights from text data, which will be valuable in your future career as a Business Analyst.
Product Manager
Product Manager
Product Managers develop and manage software products, many of which incorporate NLP. This course will give you a solid understanding of NLP concepts and techniques, which will be helpful in your future career as a Product Manager.
Technical Writer
Technical Writer
Technical Writers create documentation for software and other technical products, often using NLP techniques. The course will provide you with a good understanding of NLP, which will help you write clear and concise documentation for technical products.
UX Designer
UX Designer
UX Designers design user interfaces for software and other products, often using NLP techniques. This course will teach you about the basics of NLP, which will help you design user interfaces that are easy to use and understand.
Content Strategist
Content Strategist
Content Strategists develop and manage content for websites and other digital platforms, often using NLP techniques. This course will teach you about the basics of NLP, which will help you create content that is engaging and informative.
Digital Marketer
Digital Marketer
Digital Marketers use digital channels to promote products and services, often using NLP techniques. This course will teach you about the basics of NLP, which will help you create effective digital marketing campaigns.
Customer Success Manager
Customer Success Manager
Customer Success Managers help customers get the most out of a company's products and services, often using NLP techniques. This course will teach you about the basics of NLP, which will help you provide better support to customers.
Sales Engineer
Sales Engineer
Sales Engineers help customers understand and buy a company's products and services, often using NLP techniques. This course will teach you about the basics of NLP, which will help you better understand customer needs and close more deals.
Recruiter
Recruiter
Recruiters find and hire talent for companies, often using NLP techniques. This course will teach you about the basics of NLP, which will help you find the best candidates for your open positions.
For more career information including salaries, visit:
OpenCourser.com/course/ik5n4x/natural
Reading list
We've selected 13 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
Natural Language Processing with Sequence Models.
Save
Provides a comprehensive overview of deep learning, covering the fundamental concepts, algorithms, and applications. It is an excellent resource for learners who are new to the field or for those who want to deepen their understanding.
Provides a practical introduction to natural language processing, covering a wide range of topics including text classification, information retrieval, and machine translation. It valuable resource for learners who want to apply NLP techniques to real-world problems.
Provides a comprehensive overview of speech and language processing, covering topics such as phonetics, phonology, syntax, semantics, and pragmatics. It is an excellent resource for learners who want to gain a deep understanding of the field.
Provides a comprehensive overview of probabilistic graphical models, covering topics such as Bayesian networks, Markov random fields, and Kalman filters. It is an excellent resource for learners who want to gain a deep understanding of the field.
Provides a comprehensive overview of information theory, inference, and learning algorithms. It is an excellent resource for learners who want to gain a deep understanding of the field.
Provides a comprehensive overview of pattern recognition and machine learning, covering a wide range of topics including supervised learning, unsupervised learning, and deep learning. It is an excellent resource for learners who want to gain a strong foundation in the field.
Provides a comprehensive overview of machine learning from a probabilistic perspective, covering topics such as Bayesian inference, Gaussian processes, and Markov chain Monte Carlo. It is an excellent resource for learners who want to gain a deep understanding of the field.
Provides a comprehensive overview of speech recognition, covering topics such as acoustic modeling, language modeling, and decoding. It is an excellent resource for learners who want to gain a deep understanding of the field.
Provides a comprehensive overview of the Natural Language Toolkit (NLTK), a Python library for natural language processing. It is an excellent resource for learners who want to gain a deep understanding of the library and its applications.
Provides a comprehensive overview of scikit-learn, a Python library for machine learning. It is an excellent resource for learners who want to gain a deep understanding of the library and its applications.
Provides a comprehensive overview of TensorFlow, a deep learning framework. It is an excellent resource for learners who want to gain a deep understanding of the framework and its applications.
Provides a comprehensive overview of PyTorch, a deep learning framework. It is an excellent resource for learners who want to gain a deep understanding of the framework and its applications.
Provides a comprehensive overview of Keras, a deep learning library. It is an excellent resource for learners who want to gain a deep understanding of the library and its applications.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/ik5n4x/natural
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Effort
At the rate of 5 hours a week, it typically takes 4 weeks to complete one Course.
Level
Intermediate
Via
Coursera
Institution
DeepLearning.AI
Instructors
Younes Bensouda Mourri
Łukasz Kaiser
Eddy Shyu
Language
English
Transcripts
Español
Français
Português
Deutsch
Italiano
中文
العربية
한국어
日本語
हिंदी
Nederlands
Svenska
Pусский
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Polski
Ελληνικά
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پښتو
آذربایجان دیلی
This course belongs to a
collection
called
Natural Language Processing. It's comprised of four courses:
- Natural Language Processing with Attention Models
- Natural Language Processing with Classification and Vector Spaces
- Natural Language Processing with Probabilistic Models
- Natural Language Processing with Sequence Models (viewing)
Good to know
Introduces Recurrent Neural Networks (RNNs) for Language Modeling, highlighting the advantages of sequential data representation for text prediction
Explores the concept of Long-Short Term Memory (LSTM) units as a solution to vanishing gradient issues, making them particularly useful for Named Entity Recognition (NER) tasks
Utilizes real-world data from Kaggle for hands-on experience in building an LSTM-based NER system, fostering practical application skills
Introduces Siamese networks, a specialized type of neural network architecture for comparing question similarity in a given corpus, emphasizing their utility in identifying duplicates
Provides hands-on experience in building a Siamese network for question similarity identification using a dataset from Quora, reinforcing practical implementation skills
Taught by renowned experts in NLP, machine learning, and deep learning, including Younes Bensouda Mourri and Łukasz Kaiser, ensuring high-quality instruction and industry-relevant insights
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