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Google Cloud
This course will introduce you to the attention mechanism, a powerful technique that allows neural networks to focus on specific parts of an input sequence. You will learn how attention works, and how it can be used to improve the performance of a variety of machine learning tasks, including machine translation, text summarization, and question answering. This course is estimated to take approximately 45 minutes to complete.
Read more
This course will introduce you to the attention mechanism, a powerful technique that allows neural networks to focus on specific parts of an input sequence. You will learn how attention works, and how it can be used to improve the performance of a variety of machine learning tasks, including machine translation, text summarization, and question answering. This course is estimated to take approximately 45 minutes to complete.
Read more
This course will introduce you to the attention mechanism, a powerful technique that allows neural networks to focus on specific parts of an input sequence. You will learn how attention works, and how it can be used to improve the performance of a variety of machine learning tasks, including machine translation, text summarization, and question answering. This course is estimated to take approximately 45 minutes to complete.
This course will introduce you to the attention mechanism, a powerful technique that allows neural networks to focus on specific parts of an input sequence. You will learn how attention works, and how it can be used to improve the performance of a variety of machine learning tasks, including machine translation, text summarization, and question answering. This course is estimated to take approximately 45 minutes to complete.
Register for this course and see more details by visiting:
OpenCourser.com/course/vtw4av/attention
What's inside
Syllabus
Introduction
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Suitable for learners who want to enhance their understanding of neural network architectures
Suitable for learners with some experience in machine learning or deep learning
Suitable for learners interested in improving the performance of machine learning models
Suitable for learners who want to stay up-to-date with the latest developments in natural language processing
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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 Attention Mechanism with these
activities:
Review Transformer neural networks
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Review Transformer neural networks to refresh your knowledge and strengthen your foundational understanding.
Show steps
-
Read through course materials on Transformer neural networks
-
Complete practice exercises and quizzes on Transformer neural networks
Follow tutorials on attention mechanisms
Show steps
Seek out tutorials to enhance your understanding of attention mechanisms and how they are used in machine learning tasks.
Browse courses on
Attention Mechanism
Show steps
-
Identify tutorials that cover attention mechanisms in detail
-
Follow the tutorials and complete the associated exercises
Practice using attention mechanisms in machine learning tasks
Show steps
Engage in practice drills to reinforce your understanding of attention mechanisms and their application in various machine learning tasks.
Browse courses on
Attention Mechanism
Show steps
-
Find datasets and code examples related to attention mechanisms in machine learning
-
Implement attention mechanisms in your own machine learning projects
Three other activities
Expand to see all activities and additional details
Show all six activities
Create a presentation on attention mechanisms
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Develop a presentation to solidify your understanding of attention mechanisms and their applications, fostering deeper engagement with the concept
Browse courses on
Attention Mechanism
Show steps
-
Research and gather information on attention mechanisms
-
Organize and structure your presentation content
-
Design and create visual aids to support your presentation
-
Practice delivering your presentation
Participate in Kaggle competitions on attention mechanisms
Show steps
Engage in Kaggle competitions to test your skills in applying attention mechanisms to real-world machine learning problems.
Browse courses on
Deep Learning
Show steps
-
Identify Kaggle competitions that involve attention mechanisms
-
Study the competition guidelines and data
-
Develop and implement your machine learning solution using attention mechanisms
-
Submit your solution and track your progress
Contribute to open-source projects on attention mechanisms
Show steps
Engage with the broader machine learning community by contributing to open-source projects focused on attention mechanisms.
Browse courses on
Attention Mechanism
Show steps
-
Identify open-source projects related to attention mechanisms
-
Review the project documentation and identify areas where you can contribute
-
Fork the project and make your changes
-
Submit a pull request to the project maintainers
Review Transformer neural networks
Show steps
Review Transformer neural networks to refresh your knowledge and strengthen your foundational understanding.
Show steps
Show steps
Show steps
- Read through course materials on Transformer neural networks
- Complete practice exercises and quizzes on Transformer neural networks
Follow tutorials on attention mechanisms
Show steps
Seek out tutorials to enhance your understanding of attention mechanisms and how they are used in machine learning tasks.
Browse courses on
Attention Mechanism
Show steps
Show steps
Show steps
- Identify tutorials that cover attention mechanisms in detail
- Follow the tutorials and complete the associated exercises
Practice using attention mechanisms in machine learning tasks
Show steps
Engage in practice drills to reinforce your understanding of attention mechanisms and their application in various machine learning tasks.
Browse courses on
Attention Mechanism
Show steps
Show steps
Show steps
- Find datasets and code examples related to attention mechanisms in machine learning
- Implement attention mechanisms in your own machine learning projects
Three other activities
Expand to see all activities and additional details
Show all six activities
Create a presentation on attention mechanisms
Show steps
Develop a presentation to solidify your understanding of attention mechanisms and their applications, fostering deeper engagement with the concept
Browse courses on
Attention Mechanism
Show steps
Show steps
Show steps
- Research and gather information on attention mechanisms
- Organize and structure your presentation content
- Design and create visual aids to support your presentation
- Practice delivering your presentation
Participate in Kaggle competitions on attention mechanisms
Show steps
Engage in Kaggle competitions to test your skills in applying attention mechanisms to real-world machine learning problems.
Browse courses on
Deep Learning
Show steps
Show steps
Show steps
- Identify Kaggle competitions that involve attention mechanisms
- Study the competition guidelines and data
- Develop and implement your machine learning solution using attention mechanisms
- Submit your solution and track your progress
Contribute to open-source projects on attention mechanisms
Show steps
Engage with the broader machine learning community by contributing to open-source projects focused on attention mechanisms.
Browse courses on
Attention Mechanism
Show steps
Show steps
Show steps
- Identify open-source projects related to attention mechanisms
- Review the project documentation and identify areas where you can contribute
- Fork the project and make your changes
- Submit a pull request to the project maintainers
Career center
Learners who complete Attention Mechanism will develop knowledge and skills
that may be useful to these careers:
Computational Linguist
Computational Linguist
Computational Linguists combine natural language processing with linguistics to develop new NLP solutions. This course will help build a foundation in the concepts underpinning NLP technology, particularly relating to how models focus on specific parts of an input sequence. This is a foundational skill for understanding and improving the latest in NLP technology.
Natural Language Processing Researcher
Natural Language Processing Researcher
Natural Language Processing Researchers develop new NLP theories and techniques. This course will help build a foundation in the concepts underpinning NLP technology, particularly relating to how models focus on specific parts of an input sequence. This is a foundational skill for understanding and improving the latest in NLP technology.
Natural Language Processing Engineer
Natural Language Processing Engineer
Natural Language Processing Engineers build and test software that can understand and complete tasks involving human language. This course will help build a foundation in the concepts underpinning NLP technology, particularly relating to how models focus on specific parts of an input sequence. This is a foundational skill for understanding and improving the latest in NLP technology.
Question Answering Researcher
Question Answering Researcher
Question Answering Researchers develop and test algorithms that allow computers to answer questions from text. This course will help build a foundation in the concepts underpinning NLP technology, particularly relating to how models focus on specific parts of an input sequence. This is a foundational skill for understanding and improving the latest in NLP technology.
Machine Translation Engineer
Machine Translation Engineer
Machine Translation Engineers design and build software that can translate text from one language to another. This course will help build a foundation in the concepts underpinning NLP technology, particularly relating to how models focus on specific parts of an input sequence. This is a foundational skill for understanding and improving the latest in NLP technology.
Software Engineer (NLP)
Software Engineer (NLP)
Software Engineers who specialize in NLP work on the development of NLP-based products. This course will help build a foundation in the concepts underpinning NLP technology, particularly relating to how models focus on specific parts of an input sequence. This is a foundational skill for understanding and improving the latest in NLP technology.
Document Summarizer
Document Summarizer
Document Summarizers write summaries of documents. This course will help build a foundation in the concepts underpinning NLP technology, particularly relating to how models focus on specific parts of an input sequence. This is a foundational skill for understanding and improving the latest in NLP technology.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design, build, and maintain machine learning models. This course will help build a foundation in the concepts underpinning NLP technology, particularly relating to how models focus on specific parts of an input sequence. This is a foundational skill for understanding and improving the latest in NLP technology.
Knowledge Engineer
Knowledge Engineer
Knowledge Engineers design and build knowledge bases for use in AI systems. This course will help build a foundation in the concepts underpinning NLP technology, particularly relating to how models focus on specific parts of an input sequence. This is a foundational skill for understanding and improving the latest in NLP technology.
Text Summarizer
Text Summarizer
Text Summarizers condense large amounts of text into shorter, more concise summaries. This course will help build a foundation in the concepts underpinning NLP technology, particularly relating to how models focus on specific parts of an input sequence. This is a foundational skill for understanding and improving the latest in NLP technology. Those aiming to summarize text would find particular utility in the course's section on text summarization.
Research Scientist (NLP)
Research Scientist (NLP)
Research Scientists are responsible for developing new NLP theories and techniques. This course will help build a foundation in the concepts underpinning NLP technology, particularly relating to how models focus on specific parts of an input sequence. This is a foundational skill for understanding and improving the latest in NLP technology.
Speech Recognition Engineer
Speech Recognition Engineer
Speech Recognition Engineers develop and maintain software that allows computers to recognize human speech. This course will help build a foundation in the concepts underpinning NLP technology, particularly relating to how models focus on specific parts of an input sequence. This is a foundational skill for understanding and improving the latest in NLP technology.
Artificial Intelligence Engineer
Artificial Intelligence Engineer
Artificial Intelligence Engineers design and build AI systems. This course will help build a foundation in the concepts underpinning NLP technology, particularly relating to how models focus on specific parts of an input sequence. This is a foundational skill for understanding and improving the latest in NLP technology.
Data Scientist
Data Scientist
Data Scientists use data to solve business problems. This course will help build a foundation in the concepts underpinning NLP technology, particularly relating to how models focus on specific parts of an input sequence. This is a foundational skill for understanding and improving the latest in NLP technology.
Data Analyst
Data Analyst
Data Analysts collect, analyze, and interpret data to help businesses make decisions. This course will help build a foundation in the concepts underpinning NLP technology, particularly relating to how models focus on specific parts of an input sequence. This is a foundational skill for understanding and improving the latest in NLP technology.
For more career information including salaries, visit:
OpenCourser.com/course/vtw4av/attention
Reading list
We've selected six 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
Attention Mechanism.
Save
Comprehensive introduction to deep learning. It covers a wide range of topics, including neural networks, convolutional neural networks, recurrent neural networks, and generative models.
Provides an overview of natural language processing with deep learning. It covers a wide range of topics, including word embeddings, sequence models, and attention mechanisms.
Provides an overview of neural machine translation. It covers a wide range of topics, including the different types of neural machine translation models and their applications.
Provides an overview of natural language generation. It covers a wide range of topics, including the different types of natural language generation models and their applications.
Save
Provides an overview of computer vision. It covers a wide range of topics, including the different types of computer vision models and their applications.
Provides an overview of speech and language processing. It covers a wide range of topics, including the different types of speech and language processing models and their applications.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/vtw4av/attention
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Time
5 hours
Level
Intermediate
Via
Pluralsight
Instructor
Google Cloud
Language
English
Good to know
Suitable for learners who want to enhance their understanding of neural network architectures
Suitable for learners with some experience in machine learning or deep learning
Suitable for learners interested in improving the performance of machine learning models
Suitable for learners who want to stay up-to-date with the latest developments in natural language processing
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