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Google Cloud Training
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/7ng3n7/transformer
What's inside
Syllabus
Transformer Models and BERT Model: Overview
In this module you will 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.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Teaches about the Transformer architecture, which is standard in the machine learning industry
Taught by Google Cloud Training, who are recognized for their work in the machine learning industry
Covers transformer models and BERT, which are highly relevant to machine learning and natural language processing
Develops skills in using BERT for tasks such as text classification and question answering
Explicitly requires learners to come in with extensive background knowledge in machine learning and natural language processing
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 Transformer Architecture
Show steps
Reviewing the Transformer Architecture and the Self-attention Mechanism will help you understand the foundational concepts of the BERT model.
Browse courses on
Transformer Architecture
Show steps
-
Read through the course materials on the Transformer Architecture.
-
Take notes on the key concepts, such as self-attention.
-
Review your notes and try to explain the concepts to yourself.
Review the basics of natural language processing (NLP)
Show steps
Reviewing the basics of NLP will help you better understand the context and applications of the Transformer architecture.
Browse courses on
Natural Language Processing
Show steps
-
Review the course materials on NLP.
-
Read articles or tutorials on NLP.
BERT Question Answering Practice
Show steps
Practicing BERT Question Answering will improve your understanding of how to use BERT to extract information from text.
Browse courses on
Question Answering
Show steps
-
Find a dataset of questions and answers.
-
Use a BERT model to answer the questions and evaluate your results.
-
Repeat the process with different datasets to improve your accuracy.
Three other activities
Expand to see all activities and additional details
Show all six activities
Complete 10 practice exercises on the Transformer architecture
Show steps
Practice exercises can help you to understand the concepts and apply them to real-world scenarios.
Browse courses on
Transformer Architecture
Show steps
-
Review the course materials on the Transformer architecture.
-
Attempt to solve the practice exercises.
-
Check your answers against the provided solutions.
Build a BERT Model
Show steps
Building a BERT model from scratch will provide you with a deeper understanding of its inner workings and how it can be used for various tasks.
Browse courses on
BERT Model
Show steps
-
Find a tutorial on building a BERT model using a framework like TensorFlow or PyTorch.
-
Follow the tutorial step-by-step and build the model.
-
Test the model on a sample dataset and evaluate its performance.
Volunteer at a local organization that uses NLP technology
Show steps
Volunteering will provide you with hands-on experience with NLP and its applications, allowing you to see how the technology is used in the real world.
Browse courses on
NLP Applications
Show steps
-
Research local organizations that use NLP technology.
-
Contact the organizations and inquire about volunteer opportunities.
Review Transformer Architecture
Show steps
Reviewing the Transformer Architecture and the Self-attention Mechanism will help you understand the foundational concepts of the BERT model.
Browse courses on
Transformer Architecture
Show steps
Show steps
Show steps
- Read through the course materials on the Transformer Architecture.
- Take notes on the key concepts, such as self-attention.
- Review your notes and try to explain the concepts to yourself.
Review the basics of natural language processing (NLP)
Show steps
Reviewing the basics of NLP will help you better understand the context and applications of the Transformer architecture.
Browse courses on
Natural Language Processing
Show steps
Show steps
Show steps
- Review the course materials on NLP.
- Read articles or tutorials on NLP.
BERT Question Answering Practice
Show steps
Practicing BERT Question Answering will improve your understanding of how to use BERT to extract information from text.
Browse courses on
Question Answering
Show steps
Show steps
Show steps
- Find a dataset of questions and answers.
- Use a BERT model to answer the questions and evaluate your results.
- Repeat the process with different datasets to improve your accuracy.
Three other activities
Expand to see all activities and additional details
Show all six activities
Complete 10 practice exercises on the Transformer architecture
Show steps
Practice exercises can help you to understand the concepts and apply them to real-world scenarios.
Browse courses on
Transformer Architecture
Show steps
Show steps
Show steps
- Review the course materials on the Transformer architecture.
- Attempt to solve the practice exercises.
- Check your answers against the provided solutions.
Build a BERT Model
Show steps
Building a BERT model from scratch will provide you with a deeper understanding of its inner workings and how it can be used for various tasks.
Browse courses on
BERT Model
Show steps
Show steps
Show steps
- Find a tutorial on building a BERT model using a framework like TensorFlow or PyTorch.
- Follow the tutorial step-by-step and build the model.
- Test the model on a sample dataset and evaluate its performance.
Volunteer at a local organization that uses NLP technology
Show steps
Volunteering will provide you with hands-on experience with NLP and its applications, allowing you to see how the technology is used in the real world.
Browse courses on
NLP Applications
Show steps
Show steps
Show steps
- Research local organizations that use NLP technology.
- Contact the organizations and inquire about volunteer opportunities.
Career center
Learners who complete Transformer Models and BERT Model will develop knowledge and skills
that may be useful to these careers:
Computational Linguist
Computational Linguist
Computational Linguists use computer science and linguistics to study and develop natural language processing technologies. The Transformer Models and BERT Model course covers a key type of machine learning architecture used in Natural Language Processing: the Transformer architecture. Furthermore, this course covers the BERT model which is used for a variety of Natural Language Processing tasks. This course may be useful for building foundational knowledge and skills for this career path, but a master's or PhD is often required.
Natural Language Processing Engineer
Natural Language Processing Engineer
Natural Language Processing Engineers develop and implement software capable of understanding or generating human language. They may focus on one type of task, such as machine translation, text summarization, or question answering. The Transformer Models and BERT Model course covers a key type of machine learning architecture used in Natural Language Processing: the Transformer architecture. Furthermore, this course covers the BERT model which is used for a variety of Natural Language Processing tasks. This course may be useful for building foundational knowledge and skills for this career path. Most roles require a master's degree and a strong background in data science and computer science.
Research Scientist
Research Scientist
Research Scientists conduct research and develop new technologies. They may specialize in a particular field, such as machine learning or artificial intelligence. The Transformer Models and BERT Model course introduces you to a key type of machine learning architecture: the Transformer architecture. This course may be useful in building foundational knowledge for this career path. Candidates typically have a master's or PhD degree in a relevant field.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers develop machine learning algorithms and implement them into software programs. The Transformer Models and BERT Model course introduces you to a key type of machine learning architecture: the Transformer architecture. This course may be useful in building foundational knowledge for this career path. Many top-tier bachelor's programs provide the necessary foundational knowledge for this career, but an overview of Transformer Models and BERT Model, like this course provides, may be helpful in preparing a job candidate for entry-level work.
Data Scientist
Data Scientist
Data Scientists apply machine learning and statistical modeling to extract insights from data. The Transformer Models and BERT Model course introduces you to a key type of machine learning architecture: the Transformer architecture. This course may be useful in building foundational knowledge for this career path. Candidates often have a background in computer science, statistics, or mathematics. Some roles may also require a master's or PhD degree.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems. The Transformer Models and BERT Model course introduces you to a key type of machine learning architecture: the Transformer architecture. This course may be useful in building foundational knowledge for this career path. Most roles require a bachelor's degree in computer science or a related field.
Product Manager
Product Manager
Product Managers are responsible for the development and launch of new products. They may work in a variety of industries, such as software, hardware, or consumer products. The Transformer Models and BERT Model course may be useful in building foundational knowledge for this career path, but a bachelor's or master's degree in business administration or a related field is often required.
Technical Writer
Technical Writer
Technical Writers create and maintain technical documentation, such as user manuals, white papers, and training materials. The Transformer Models and BERT Model course may be useful in building foundational knowledge for this career path, but a bachelor's or master's degree in technical writing or a related field is often required.
Financial Analyst
Financial Analyst
Financial Analysts help companies make investment decisions. They may work in a variety of industries, such as banking, insurance, or asset management. The Transformer Models and BERT Model course may be useful in building foundational knowledge for this career path, but a bachelor's or master's degree in finance or a related field is often required.
Marketing Manager
Marketing Manager
Marketing Managers plan and execute marketing campaigns. They may work in a variety of industries, such as consumer products, software, or healthcare. The Transformer Models and BERT Model course may be useful in building foundational knowledge for this career path, but a bachelor's or master's degree in marketing or a related field is often required.
Project Manager
Project Manager
Project Managers plan, execute, and close projects. They may work in a variety of industries, such as construction, software development, or manufacturing. The Transformer Models and BERT Model course may be useful in building foundational knowledge for this career path, but a bachelor's or master's degree in project management or a related field is often required.
Business Analyst
Business Analyst
Business Analysts help organizations improve their business processes and operations. The Transformer Models and BERT Model course may be useful in building foundational knowledge for this career path, but a bachelor's or master's degree in business administration or a related field is often required.
Human Resources Manager
Human Resources Manager
Human Resources Managers oversee the human resources department of an organization. They may work in a variety of industries, such as manufacturing, retail, or healthcare. The Transformer Models and BERT Model course may be useful in building foundational knowledge for this career path, but a bachelor's or master's degree in human resources or a related field is often required.
Operations Manager
Operations Manager
Operations Managers oversee the day-to-day operations of an organization. They may work in a variety of industries, such as manufacturing, retail, or healthcare. The Transformer Models and BERT Model course may be useful in building foundational knowledge for this career path, but a bachelor's or master's degree in business administration or a related field is often required.
Sales Engineer
Sales Engineer
Sales Engineers help customers choose and implement technical products and services. The Transformer Models and BERT Model course may be useful in building foundational knowledge for this career path, but a bachelor's or master's degree in engineering or a related field is often required.
For more career information including salaries, visit:
OpenCourser.com/course/7ng3n7/transformer
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
Transformer Models and BERT Model.
Save
Provides a comprehensive overview of deep learning, including transformer models.
Provides a comprehensive overview of machine learning, including transformer models.
Save
Provides a comprehensive overview of machine learning, including transformer models.
Provides a comprehensive overview of pattern recognition and machine learning, including transformer models.
Provides a comprehensive overview of information theory, inference, and learning algorithms, including transformer models.
Provides a practical guide to using Python for deep learning, including transformer models.
Provides a practical guide to using R for machine learning, including transformer models.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/7ng3n7/transformer
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Level
Advanced
Via
Coursera
Institution
Google Cloud
Instructor
Google Cloud Training
Language
English
Good to know
Teaches about the Transformer architecture, which is standard in the machine learning industry
Taught by Google Cloud Training, who are recognized for their work in the machine learning industry
Covers transformer models and BERT, which are highly relevant to machine learning and natural language processing
Develops skills in using BERT for tasks such as text classification and question answering
Explicitly requires learners to come in with extensive background knowledge in machine learning and natural language processing
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