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Google Cloud
This course introduces you to the Transformer architecture and the Bidirectional Encoder Representations from Transformers (BERT) model. You learn about the main components of the Transformer architecture, such as the self-attention mechanism, and how it is used to build the BERT model. You also learn about the different tasks that BERT can be used for, such as text classification, question answering, and natural language inference. This course is estimated to take approximately 45 minutes to complete.
Read more
This course introduces you to the Transformer architecture and the Bidirectional Encoder Representations from Transformers (BERT) model. You learn about the main components of the Transformer architecture, such as the self-attention mechanism, and how it is used to build the BERT model. You also learn about the different tasks that BERT can be used for, such as text classification, question answering, and natural language inference. This course is estimated to take approximately 45 minutes to complete.
Read more
This course introduces you to the Transformer architecture and the Bidirectional Encoder Representations from Transformers (BERT) model. You learn about the main components of the Transformer architecture, such as the self-attention mechanism, and how it is used to build the BERT model. You also learn about the different tasks that BERT can be used for, such as text classification, question answering, and natural language inference. This course is estimated to take approximately 45 minutes to complete.
This course introduces you to the Transformer architecture and the Bidirectional Encoder Representations from Transformers (BERT) model. You learn about the main components of the Transformer architecture, such as the self-attention mechanism, and how it is used to build the BERT model. You also learn about the different tasks that BERT can be used for, such as text classification, question answering, and natural language inference. This course is estimated to take approximately 45 minutes to complete.
Register for this course and see more details by visiting:
OpenCourser.com/course/24z5l2/transformer
What's inside
Syllabus
Introduction
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Examines the Transformer architecture and the Bidirectional Encoder Representations from Transformers (BERT) model
Taught by Google Cloud instructors, who are recognized for their work in this field
Develops skills in natural language processing, a core competency in machine learning
Covers topics relevant to text classification, question answering, and natural language inference
Requires no prerequisites, making it accessible to learners with varying backgrounds
Save this course
Save Transformer Models and BERT Model to your list so you can find it easily later:
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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 Transformer Models and BERT Model with these
activities:
Review the concepts of natural language processing
Show steps
Covering the foundations of NLP will lay a strong groundwork for the Transformer and BERT models covered in the course.
Browse courses on
Natural Language Processing
Show steps
-
Revisit the basics of text processing, tokenization, and stemming
-
Review different NLP tasks, such as text classification and question answering
Review Linear Algebra
Show steps
Refresh essential knowledge in linear algebra, a foundational skill for deep learning and NLP.
Browse courses on
Linear Algebra
Show steps
-
Review the basics of linear algebra, including vectors, matrices, and transformations.
-
Practice solving linear equations and matrix operations.
-
Explore the application of linear algebra in NLP, such as topic modeling and natural language inference.
Compile NLP Resources
Show steps
Enhance learning by gathering and organizing a comprehensive collection of NLP resources.
Browse courses on
NLP
Show steps
-
Create a repository or document to store relevant articles, tutorials, and other NLP materials.
-
Categorize and organize the resources based on topics or task areas.
-
Share the compilation with peers or contribute it to an online community.
Six other activities
Expand to see all activities and additional details
Show all nine activities
Solve NLP Practice Problems
Show steps
Strengthen problem-solving skills and deepen understanding of NLP concepts through practical exercises.
Browse courses on
Transformer
Show steps
-
Find online platforms or textbooks that provide NLP practice problems.
-
Allocate time for regular practice, attempting to solve problems independently.
-
Review solutions and identify areas for improvement.
Join NLP Study Group
Show steps
Engage with peers to discuss concepts, exchange ideas, and reinforce understanding.
Browse courses on
Transformer
Show steps
-
Find or create a study group with other students taking the course or interested in NLP.
-
Establish regular meeting times and discuss course materials, assignments, or research papers.
-
Take turns presenting on different topics, facilitating discussions, and providing feedback.
Explore Hugging Face Transformers
Show steps
Enhance understanding of Transformer architectures by utilizing the Hugging Face Transformers library.
Browse courses on
Transformer
Show steps
-
Familiarize yourself with the Hugging Face Transformers library and its functionality.
-
Follow tutorials or documentation to load and use pre-trained Transformer models for various NLP tasks.
-
Fine-tune a pre-trained model on a specific dataset.
Follow Hands-on NLP Tutorial
Show steps
Solidify understanding of the concepts by implementing practical examples of NLP tasks.
Browse courses on
Transformer
Show steps
-
Find a comprehensive tutorial that covers the implementation of Transformer and BERT models for NLP tasks.
-
Set up the required environment and tools for the tutorial.
-
Follow the tutorial step-by-step, implementing and experimenting with the models on provided datasets.
-
Answer the review questions and complete the exercises included in the tutorial to test your understanding.
Build an NLP Project
Show steps
Enhance practical skills and apply the concepts learned in the course by creating a real-world NLP project.
Browse courses on
Transformer
Show steps
-
Identify an NLP task that you're interested in exploring.
-
Gather and prepare the necessary data for your project.
-
Choose an appropriate NLP model, such as Transformer or BERT, and implement it for your task.
-
Train and evaluate your model to optimize its performance.
-
Deploy your model and demonstrate its functionality.
Contribute to TensorFlow NLP Projects
Show steps
Engage with the open-source community, contribute to NLP projects, and expand your knowledge.
Browse courses on
Transformer
Show steps
-
Identify open-source projects related to Transformer models and NLP tasks on platforms like GitHub.
-
Familiarize yourself with the project's codebase and documentation.
-
Identify areas where you can contribute, such as bug fixes, feature improvements, or documentation updates.
-
Submit pull requests with your contributions and actively engage in discussions on the project's repository.
Review the concepts of natural language processing
Show steps
Covering the foundations of NLP will lay a strong groundwork for the Transformer and BERT models covered in the course.
Browse courses on
Natural Language Processing
Show steps
Show steps
Show steps
- Revisit the basics of text processing, tokenization, and stemming
- Review different NLP tasks, such as text classification and question answering
Review Linear Algebra
Show steps
Refresh essential knowledge in linear algebra, a foundational skill for deep learning and NLP.
Browse courses on
Linear Algebra
Show steps
Show steps
Show steps
- Review the basics of linear algebra, including vectors, matrices, and transformations.
- Practice solving linear equations and matrix operations.
- Explore the application of linear algebra in NLP, such as topic modeling and natural language inference.
Compile NLP Resources
Show steps
Enhance learning by gathering and organizing a comprehensive collection of NLP resources.
Browse courses on
NLP
Show steps
Show steps
Show steps
- Create a repository or document to store relevant articles, tutorials, and other NLP materials.
- Categorize and organize the resources based on topics or task areas.
- Share the compilation with peers or contribute it to an online community.
Six other activities
Expand to see all activities and additional details
Show all nine activities
Solve NLP Practice Problems
Show steps
Strengthen problem-solving skills and deepen understanding of NLP concepts through practical exercises.
Browse courses on
Transformer
Show steps
Show steps
Show steps
- Find online platforms or textbooks that provide NLP practice problems.
- Allocate time for regular practice, attempting to solve problems independently.
- Review solutions and identify areas for improvement.
Join NLP Study Group
Show steps
Engage with peers to discuss concepts, exchange ideas, and reinforce understanding.
Browse courses on
Transformer
Show steps
Show steps
Show steps
- Find or create a study group with other students taking the course or interested in NLP.
- Establish regular meeting times and discuss course materials, assignments, or research papers.
- Take turns presenting on different topics, facilitating discussions, and providing feedback.
Explore Hugging Face Transformers
Show steps
Enhance understanding of Transformer architectures by utilizing the Hugging Face Transformers library.
Browse courses on
Transformer
Show steps
Show steps
Show steps
- Familiarize yourself with the Hugging Face Transformers library and its functionality.
- Follow tutorials or documentation to load and use pre-trained Transformer models for various NLP tasks.
- Fine-tune a pre-trained model on a specific dataset.
Follow Hands-on NLP Tutorial
Show steps
Solidify understanding of the concepts by implementing practical examples of NLP tasks.
Browse courses on
Transformer
Show steps
Show steps
Show steps
- Find a comprehensive tutorial that covers the implementation of Transformer and BERT models for NLP tasks.
- Set up the required environment and tools for the tutorial.
- Follow the tutorial step-by-step, implementing and experimenting with the models on provided datasets.
- Answer the review questions and complete the exercises included in the tutorial to test your understanding.
Build an NLP Project
Show steps
Enhance practical skills and apply the concepts learned in the course by creating a real-world NLP project.
Browse courses on
Transformer
Show steps
Show steps
Show steps
- Identify an NLP task that you're interested in exploring.
- Gather and prepare the necessary data for your project.
- Choose an appropriate NLP model, such as Transformer or BERT, and implement it for your task.
- Train and evaluate your model to optimize its performance.
- Deploy your model and demonstrate its functionality.
Contribute to TensorFlow NLP Projects
Show steps
Engage with the open-source community, contribute to NLP projects, and expand your knowledge.
Browse courses on
Transformer
Show steps
Show steps
Show steps
- Identify open-source projects related to Transformer models and NLP tasks on platforms like GitHub.
- Familiarize yourself with the project's codebase and documentation.
- Identify areas where you can contribute, such as bug fixes, feature improvements, or documentation updates.
- Submit pull requests with your contributions and actively engage in discussions on the project's repository.
Career center
Learners who complete Transformer Models and BERT Model will develop knowledge and skills
that may be useful to these careers:
Natural Language Processing Researcher
Natural Language Processing Researcher
Natural Language Processing Researchers conduct research in the field of natural language processing. They have a strong understanding of natural language processing techniques, as well as proficiency in programming and machine learning. Due to the popularity of Transformers and BERT in natural language processing, this course may be useful to your career, as it covers the theoretical foundations of these models.
Deep Learning Engineer
Deep Learning Engineer
Deep Learning Engineers design, develop, and maintain deep learning models. They have a strong understanding of deep learning concepts, as well as proficiency in programming and machine learning. Due to the popularity of Transformers and BERT in deep learning, this course may be useful to your career, as it covers the theoretical foundations of these models.
Artificial Intelligence Engineer
Artificial Intelligence Engineer
Artificial Intelligence Engineers design, develop, and maintain AI systems. They have a strong understanding of AI concepts, as well as proficiency in programming and machine learning. This course may be helpful to you if you work on projects involving natural language processing, as Transformers and BERT are widely used in this field.
Research Scientist
Research Scientist
Research Scientists conduct research in a variety of scientific fields, including computer science, natural language processing, and machine learning. They have a strong understanding of research methods, as well as proficiency in programming and data analysis. This course may be helpful to you if you are interested in researching new natural language processing models, as it covers the theoretical underpinnings of transformer and BERT models.
Natural Language Generation Engineer
Natural Language Generation Engineer
Natural Language Generation Engineers build and maintain natural language generation systems. They have a strong understanding of natural language generation techniques, as well as proficiency in programming and machine learning. Due to the popularity of Transformers and BERT in natural language generation, this course may be useful to your career, as it covers the theoretical foundations of these models.
Speech Recognition Engineer
Speech Recognition Engineer
Speech Recognition Engineers develop and maintain speech recognition systems. They have a strong understanding of speech recognition techniques, as well as proficiency in programming and machine learning. Due to the popularity of Transformers and BERT in speech recognition, this course may be useful to your career, as it covers the theoretical foundations of these models.
Business Intelligence Analyst
Business Intelligence Analyst
Business Intelligence Analysts use data analysis and visualization techniques to help businesses make informed decisions. They have a strong understanding of business intelligence concepts, as well as proficiency in data analysis and visualization tools. This course may be helpful to you if you work on projects involving natural language processing, as Transformers and BERT are widely used in this area.
Machine Learning Scientist
Machine Learning Scientist
Machine Learning Scientists research and develop new machine learning algorithms and techniques. They have a strong understanding of machine learning theory, as well as proficiency in programming and software engineering. This course may be helpful to you if you are interested in working on the theoretical development of natural language processing models, as it covers the foundations of transformer and BERT models.
Computational Linguist
Computational Linguist
Computational Linguists use computational methods to study human language. They have a strong understanding of linguistics, as well as proficiency in programming and machine learning. Due to the popularity of Transformers and BERT in computational linguistics, this course may be useful to your career, as it covers the theoretical foundations of these models.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze financial data and make investment decisions. They have a strong understanding of financial markets, as well as proficiency in programming and machine learning. This course may supplement your knowledge of machine learning models in finance. A deep understanding of Transformer and BERT models can be particularly useful for working with textual financial data.
Natural Language Processing Engineer
Natural Language Processing Engineer
Natural Language Processing Engineers build and maintain NLP models to help computers understand and generate human language. They have a strong understanding of natural language processing techniques, as well as proficiency in programming and machine learning. Due to the popularity of Transformers and BERT in natural language processing, this course may be helpful to your career, as it covers the theoretical underpinnings of these models.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software applications. They have a strong understanding of computer science fundamentals, as well as proficiency in programming languages and software engineering principles. This course may be useful to you if you work on natural language processing projects, as Transformers and BERT are widely used in this field.
Data Analyst
Data Analyst
Data Analysts clean, analyze, and interpret data to help organizations make informed decisions. They use various statistical and machine learning techniques to extract meaningful insights from data. Many Data Analysts work specifically with text data, which makes this course potentially useful. It lays the theoretical groundwork for using Transformer and BERT models in real-world data analysis.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers build, deploy, and maintain machine learning models. They have a strong understanding of the machine learning lifecycle, as well as proficiency in programming and software engineering. Due to the popularity of Transformers and BERT in natural language processing, this course may be helpful to your career, as it introduces the technical foundations of these models.
Data Scientist
Data Scientist
Data Scientists use their strong technical understanding of machine learning, mathematics, and programming to tackle complex data problems. Many Data Scientists work with Transformers and BERT on language-related projects, which is why the material taught in this course may be useful to you. It helps you build a foundation in the theoretical aspects of these models, which can be helpful for working with them later in your Data Science career.
For more career information including salaries, visit:
OpenCourser.com/course/24z5l2/transformer
Reading list
We've selected 15 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
Transformer Models and BERT Model.
Provides a comprehensive introduction to NLP with Python, covering everything from the basics to advanced topics. It is particularly useful for those who want to get started with NLP programming.
Provides a comprehensive introduction to speech and language processing, covering everything from the basics to advanced topics. It is particularly useful for those who want to learn how to develop speech and language processing systems.
Provides a comprehensive introduction to pattern recognition and machine learning, covering everything from the basics to advanced topics. It is particularly useful for those who want to learn how to develop machine learning models.
Save
Provides a comprehensive introduction to deep learning, covering everything from the basics to advanced topics. It is particularly useful for those who want to learn how to develop deep learning models.
Provides a comprehensive introduction to artificial intelligence, covering everything from the basics to advanced topics. It is particularly useful for those who want to learn about the history of AI and its potential future.
Save
Provides a comprehensive introduction to machine learning, covering everything from the basics to advanced topics. It is particularly useful for those who want to learn how to develop machine learning models.
Provides a practical introduction to natural language processing with Python. It valuable resource for anyone who wants to learn how to use Python for NLP tasks.
Provides a comprehensive overview of speech and language processing. It valuable resource for anyone who wants to learn more about this field.
Provides a comprehensive overview of statistical learning. It valuable resource for anyone who wants to learn more about this field.
Provides a comprehensive overview of machine learning. It valuable resource for anyone who wants to learn more about this field.
Save
Provides a comprehensive overview of deep learning. It valuable resource for anyone who wants to learn more about this field.
Provides a comprehensive overview of machine learning. It valuable resource for anyone who wants to learn more about this field.
Provides a comprehensive overview of pattern recognition and machine learning. It valuable resource for anyone who wants to learn more about this field.
Provides a comprehensive overview of machine learning from a probabilistic perspective. It valuable resource for anyone who wants to learn more about this field.
Provides a comprehensive overview of machine learning for text. It valuable resource for anyone who wants to learn more about this field.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/24z5l2/transformer
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Time
23 hours
Level
Introductory
Via
Pluralsight
Instructor
Google Cloud
Language
English
Good to know
Examines the Transformer architecture and the Bidirectional Encoder Representations from Transformers (BERT) model
Taught by Google Cloud instructors, who are recognized for their work in this field
Develops skills in natural language processing, a core competency in machine learning
Covers topics relevant to text classification, question answering, and natural language inference
Requires no prerequisites, making it accessible to learners with varying backgrounds
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