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Google Cloud Training
Take Udacity's free Cloud Transformer Models and BERT Course by Google and learn about the main components of the Transformer architecture and how it is used to build the BERT model.
Prerequisite details
To optimize your success in this program, we've created a list of prerequisites and recommendations to help you prepare for the curriculum. Prior to enrolling, you should have the following knowledge:
- Machine learning model implementation
- TensorFlow
- Machine learning frameworks in Python
- Intermediate Python
- Attention mechanisms
- PyTorch
Read more
Take Udacity's free Cloud Transformer Models and BERT Course by Google and learn about the main components of the Transformer architecture and how it is used to build the BERT model.
Prerequisite details
To optimize your success in this program, we've created a list of prerequisites and recommendations to help you prepare for the curriculum. Prior to enrolling, you should have the following knowledge:
- Machine learning model implementation
- TensorFlow
- Machine learning frameworks in Python
- Intermediate Python
- Attention mechanisms
- PyTorch
Read more
Take Udacity's free Cloud Transformer Models and BERT Course by Google and learn about the main components of the Transformer architecture and how it is used to build the BERT model.
Prerequisite details
To optimize your success in this program, we've created a list of prerequisites and recommendations to help you prepare for the curriculum. Prior to enrolling, you should have the following knowledge:
- Machine learning model implementation
- TensorFlow
- Machine learning frameworks in Python
- Intermediate Python
- Attention mechanisms
- PyTorch
You will also need to be able to communicate fluently and professionally in written and spoken English.
Register for this course and see more details by visiting:
OpenCourser.com/course/c2x4xz/transformer
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UDACITY40
What's inside
Syllabus
This course introduces you to the Transformer architecture and the Bidirectional Encoder Representations from Transformers (BERT) model.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Builds a strong foundation for learners with no prior machine learning experience
Teaches the latest deep learning architectures used in industry
Taught by Google Cloud Training, a recognized expert in the field
Introduces Transformer architecture, which is foundational for many deep learning applications
Prerequisite knowledge required: ML model implementation, TensorFlow, Python, attention mechanisms
Good fluency in English is required
Save this course
Save Transformer Models and BERT Model with Google Cloud 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 Google Cloud with these
activities:
Review Python Intermediate concepts
Show steps
Review the basic programming concepts of Python which are foundational to this course.
Show steps
-
Review the data types, variables, and operators
-
Practice control flow statements
-
Start working on a hands-on Python project
Review prerequisite materials on TensorFlow and Python
Show steps
Enhance understanding of key concepts and ensure a strong foundation for the course by revisiting prerequisite materials.
Browse courses on
TensorFlow
Show steps
-
Review TensorFlow tutorials and documentation
-
Complete practice exercises using TensorFlow
-
Review Python basics and intermediate concepts
Weekly Study Group
Show steps
Enhance your learning experience by discussing course concepts with your peers.
Show steps
-
Form a study group with 2-3 classmates
-
Meet regularly to discuss course material
-
Work together on practice problems and assignments
-
Share resources and insights with each other
20 other activities
Expand to see all activities and additional details
Show all 23 activities
Form a study group for BERT
Show steps
Forming a study group will allow you to discuss BERT with other students and learn from each other.
Browse courses on
BERT
Show steps
-
Find other students who are interested in BERT.
-
Meet regularly to discuss BERT.
-
Work together on BERT projects.
Watch tutorial on BERT architecture
Show steps
Watching a tutorial will familiarize you with the basics of the Transformer architecture and BERT.
Browse courses on
BERT
Show steps
-
Find a tutorial on the Transformer architecture and BERT.
-
Watch the tutorial and take notes on the key concepts.
Explore the official TensorFlow documentation
Show steps
Become familiar with the foundational concepts of TensorFlow by reading through the official documentation and tutorials.
Browse courses on
TensorFlow
Show steps
-
Visit the TensorFlow website
-
Read through the getting started guide
-
Explore the API reference
-
Follow along with the tutorials
TensorFlow Tutorial
Show steps
Familiarize yourself with the basics of TensorFlow, the framework used in this course.
Browse courses on
TensorFlow
Show steps
-
Start the TensorFlow tutorial
-
Complete the first three sections of the tutorial
-
Try out the examples provided in the tutorial
Solve Transformer and BERT practice problems
Show steps
Test your understanding of the core concepts of Transformer and BERT models by completing practice problems.
Browse courses on
Transformer Architecture
Show steps
-
Attempt the practice problems provided at the end of each lesson
-
Solve additional problems from online resources
Start a project involving NLP and Transformers
Show steps
Gain practical experience by initiating a project that incorporates NLP and Transformers.
Browse courses on
Transformer Architecture
Show steps
-
Define the scope and objectives of the project
-
Gather the necessary resources and data
-
Implement Transformer or BERT models within the project
-
Evaluate the results and refine the project
Attend a workshop on BERT
Show steps
Attending a workshop will allow you to learn from experts in the field of BERT and ask questions.
Browse courses on
BERT
Show steps
-
Find a workshop on BERT.
-
Register for the workshop.
-
Attend the workshop and take notes.
BERT Model Practice
Show steps
Develop a deeper understanding of the BERT model and its applications.
Browse courses on
BERT Model
Show steps
-
Work through the practice drills provided in the course
-
Experiment with different hyperparameters
-
Implement your own BERT model
Explore advanced Transformer and BERT tutorials
Show steps
Expand your knowledge by exploring advanced tutorials on Transformer and BERT models.
Browse courses on
Transformer Architecture
Show steps
-
Identify reputable sources for advanced tutorials
-
Follow the tutorials and complete the exercises
-
Share your learnings with others
Code implementation of BERT model
Show steps
Coding the BERT model will help you understand the inner workings of the model and how to apply it to your own projects.
Browse courses on
BERT
Show steps
-
Set up your development environment.
-
Find a dataset to train your BERT model on.
-
Code the BERT model using TensorFlow.
-
Train and evaluate your BERT model.
Review the Deep Learning book
Show steps
Establish a foundational understanding of deep learning concepts to enhance comprehension of Transformer architecture and BERT model.
View
Deep Learning
on Amazon
Show steps
-
Read chapters 1-4 of the book
-
Complete the exercises at the end of each chapter
-
Summarize the key concepts covered in each chapter
Transformer Architecture Presentation
Show steps
Demonstrate your understanding of the Transformer architecture by creating a presentation.
Browse courses on
Transformer Architecture
Show steps
-
Choose a specific aspect of the Transformer architecture to focus on
-
Research and gather information on the topic
-
Create a presentation that explains the topic clearly and concisely
-
Present your findings to your classmates
-
Answer questions and discuss your findings with your classmates
Build a Transformer or BERT-based project
Show steps
Apply your knowledge by building a project that utilizes the concepts of Transformer and BERT models.
Browse courses on
Transformer Architecture
Show steps
-
Identify a problem or use case that can be solved using Transformers or BERT
-
Design and develop the project
-
Evaluate the performance of your project
Attend a workshop on Transformer and BERT
Show steps
Gain exposure to industry best practices and expert insights on Transformer and BERT models, expanding knowledge beyond the course content.
Browse courses on
Transformer Architecture
Show steps
-
Research and identify relevant Transformer and BERT workshops
-
Attend the workshop and actively participate in discussions
-
Follow up with the workshop organizers or speakers for further clarification
Write a blog post about BERT
Show steps
Writing a blog post will help you synthesize your understanding of BERT and share your knowledge with others.
Browse courses on
BERT
Show steps
-
Choose a topic for your blog post.
-
Research your topic and gather information.
-
Write your blog post.
-
Edit and proofread your blog post.
-
Publish your blog post.
Natural Language Processing Project
Show steps
Apply the concepts learned in this course to a real-world Natural Language Processing project.
Browse courses on
Natural Language Processing
Show steps
-
Identify a problem or task that you would like to solve using Natural Language Processing
-
Gather and prepare the necessary data
-
Choose and train a suitable machine learning model
-
Evaluate the performance of your model
-
Deploy your model and make it available to others
Follow the Transformer and BERT tutorials
Show steps
Gain practical experience in implementing Transformer and BERT models, reinforcing theoretical understanding.
Browse courses on
Transformer Architecture
Show steps
-
Complete the Transformer tutorial from Hugging Face
-
Complete the BERT tutorial from Google AI
-
Build a simple NLP application using a pre-trained BERT model
Write a blog post or article on Transformer and BERT
Show steps
Enhance understanding and communication skills by summarizing and explaining Transformer and BERT concepts for a broader audience.
Browse courses on
Transformer Architecture
Show steps
-
Choose a specific aspect of Transformer or BERT to focus on
-
Research and gather information from reliable sources
-
Write a clear and concise blog post or article explaining the topic
-
Share the article with others and encourage feedback
Practice Transformer and BERT exercises
Show steps
Enhance problem-solving skills and deepen understanding of Transformer and BERT by completing practice exercises.
Browse courses on
Transformer Architecture
Show steps
-
Solve coding challenges related to Transformer and BERT
-
Participate in online coding competitions focusing on NLP
Develop a Transformer or BERT-based project
Show steps
Apply Transformer and BERT knowledge to solve real-world NLP problems, consolidating learning and fostering creativity.
Browse courses on
Transformer Architecture
Show steps
-
Identify a suitable NLP problem
-
Design and implement a Transformer or BERT model for the problem
-
Evaluate the model's performance
-
Write a project report summarizing the findings
Review Python Intermediate concepts
Show steps
Review the basic programming concepts of Python which are foundational to this course.
Show steps
Show steps
Show steps
- Review the data types, variables, and operators
- Practice control flow statements
- Start working on a hands-on Python project
Review prerequisite materials on TensorFlow and Python
Show steps
Enhance understanding of key concepts and ensure a strong foundation for the course by revisiting prerequisite materials.
Browse courses on
TensorFlow
Show steps
Show steps
Show steps
- Review TensorFlow tutorials and documentation
- Complete practice exercises using TensorFlow
- Review Python basics and intermediate concepts
Weekly Study Group
Show steps
Enhance your learning experience by discussing course concepts with your peers.
Show steps
Show steps
Show steps
- Form a study group with 2-3 classmates
- Meet regularly to discuss course material
- Work together on practice problems and assignments
- Share resources and insights with each other
20 other activities
Expand to see all activities and additional details
Show all 23 activities
Form a study group for BERT
Show steps
Forming a study group will allow you to discuss BERT with other students and learn from each other.
Browse courses on
BERT
Show steps
Show steps
Show steps
- Find other students who are interested in BERT.
- Meet regularly to discuss BERT.
- Work together on BERT projects.
Watch tutorial on BERT architecture
Show steps
Watching a tutorial will familiarize you with the basics of the Transformer architecture and BERT.
Browse courses on
BERT
Show steps
Show steps
Show steps
- Find a tutorial on the Transformer architecture and BERT.
- Watch the tutorial and take notes on the key concepts.
Explore the official TensorFlow documentation
Show steps
Become familiar with the foundational concepts of TensorFlow by reading through the official documentation and tutorials.
Browse courses on
TensorFlow
Show steps
Show steps
Show steps
- Visit the TensorFlow website
- Read through the getting started guide
- Explore the API reference
- Follow along with the tutorials
TensorFlow Tutorial
Show steps
Familiarize yourself with the basics of TensorFlow, the framework used in this course.
Browse courses on
TensorFlow
Show steps
Show steps
Show steps
- Start the TensorFlow tutorial
- Complete the first three sections of the tutorial
- Try out the examples provided in the tutorial
Solve Transformer and BERT practice problems
Show steps
Test your understanding of the core concepts of Transformer and BERT models by completing practice problems.
Browse courses on
Transformer Architecture
Show steps
Show steps
Show steps
- Attempt the practice problems provided at the end of each lesson
- Solve additional problems from online resources
Start a project involving NLP and Transformers
Show steps
Gain practical experience by initiating a project that incorporates NLP and Transformers.
Browse courses on
Transformer Architecture
Show steps
Show steps
Show steps
- Define the scope and objectives of the project
- Gather the necessary resources and data
- Implement Transformer or BERT models within the project
- Evaluate the results and refine the project
Attend a workshop on BERT
Show steps
Attending a workshop will allow you to learn from experts in the field of BERT and ask questions.
Browse courses on
BERT
Show steps
Show steps
Show steps
- Find a workshop on BERT.
- Register for the workshop.
- Attend the workshop and take notes.
BERT Model Practice
Show steps
Develop a deeper understanding of the BERT model and its applications.
Browse courses on
BERT Model
Show steps
Show steps
Show steps
- Work through the practice drills provided in the course
- Experiment with different hyperparameters
- Implement your own BERT model
Explore advanced Transformer and BERT tutorials
Show steps
Expand your knowledge by exploring advanced tutorials on Transformer and BERT models.
Browse courses on
Transformer Architecture
Show steps
Show steps
Show steps
- Identify reputable sources for advanced tutorials
- Follow the tutorials and complete the exercises
- Share your learnings with others
Code implementation of BERT model
Show steps
Coding the BERT model will help you understand the inner workings of the model and how to apply it to your own projects.
Browse courses on
BERT
Show steps
Show steps
Show steps
- Set up your development environment.
- Find a dataset to train your BERT model on.
- Code the BERT model using TensorFlow.
- Train and evaluate your BERT model.
Review the Deep Learning book
Show steps
Establish a foundational understanding of deep learning concepts to enhance comprehension of Transformer architecture and BERT model.
View
Deep Learning
on Amazon
Show steps
Show steps
Show steps
- Read chapters 1-4 of the book
- Complete the exercises at the end of each chapter
- Summarize the key concepts covered in each chapter
Transformer Architecture Presentation
Show steps
Demonstrate your understanding of the Transformer architecture by creating a presentation.
Browse courses on
Transformer Architecture
Show steps
Show steps
Show steps
- Choose a specific aspect of the Transformer architecture to focus on
- Research and gather information on the topic
- Create a presentation that explains the topic clearly and concisely
- Present your findings to your classmates
- Answer questions and discuss your findings with your classmates
Build a Transformer or BERT-based project
Show steps
Apply your knowledge by building a project that utilizes the concepts of Transformer and BERT models.
Browse courses on
Transformer Architecture
Show steps
Show steps
Show steps
- Identify a problem or use case that can be solved using Transformers or BERT
- Design and develop the project
- Evaluate the performance of your project
Attend a workshop on Transformer and BERT
Show steps
Gain exposure to industry best practices and expert insights on Transformer and BERT models, expanding knowledge beyond the course content.
Browse courses on
Transformer Architecture
Show steps
Show steps
Show steps
- Research and identify relevant Transformer and BERT workshops
- Attend the workshop and actively participate in discussions
- Follow up with the workshop organizers or speakers for further clarification
Write a blog post about BERT
Show steps
Writing a blog post will help you synthesize your understanding of BERT and share your knowledge with others.
Browse courses on
BERT
Show steps
Show steps
Show steps
- Choose a topic for your blog post.
- Research your topic and gather information.
- Write your blog post.
- Edit and proofread your blog post.
- Publish your blog post.
Natural Language Processing Project
Show steps
Apply the concepts learned in this course to a real-world Natural Language Processing project.
Browse courses on
Natural Language Processing
Show steps
Show steps
Show steps
- Identify a problem or task that you would like to solve using Natural Language Processing
- Gather and prepare the necessary data
- Choose and train a suitable machine learning model
- Evaluate the performance of your model
- Deploy your model and make it available to others
Follow the Transformer and BERT tutorials
Show steps
Gain practical experience in implementing Transformer and BERT models, reinforcing theoretical understanding.
Browse courses on
Transformer Architecture
Show steps
Show steps
Show steps
- Complete the Transformer tutorial from Hugging Face
- Complete the BERT tutorial from Google AI
- Build a simple NLP application using a pre-trained BERT model
Write a blog post or article on Transformer and BERT
Show steps
Enhance understanding and communication skills by summarizing and explaining Transformer and BERT concepts for a broader audience.
Browse courses on
Transformer Architecture
Show steps
Show steps
Show steps
- Choose a specific aspect of Transformer or BERT to focus on
- Research and gather information from reliable sources
- Write a clear and concise blog post or article explaining the topic
- Share the article with others and encourage feedback
Practice Transformer and BERT exercises
Show steps
Enhance problem-solving skills and deepen understanding of Transformer and BERT by completing practice exercises.
Browse courses on
Transformer Architecture
Show steps
Show steps
Show steps
- Solve coding challenges related to Transformer and BERT
- Participate in online coding competitions focusing on NLP
Develop a Transformer or BERT-based project
Show steps
Apply Transformer and BERT knowledge to solve real-world NLP problems, consolidating learning and fostering creativity.
Browse courses on
Transformer Architecture
Show steps
Show steps
Show steps
- Identify a suitable NLP problem
- Design and implement a Transformer or BERT model for the problem
- Evaluate the model's performance
- Write a project report summarizing the findings
Career center
Learners who complete Transformer Models and BERT Model with Google Cloud will develop knowledge and skills
that may be useful to these careers:
Deep Learning Architect
Deep Learning Architect
Deep Learning Architects design and develop deep learning systems for complex problems. They work in a variety of industries, including finance, healthcare, manufacturing, and transportation. This course may be useful to Deep Learning Architects who want to learn more about the Transformer architecture and the BERT model.
Deep Learning Engineer
Deep Learning Engineer
Deep Learning Engineers design, develop, and maintain deep learning models. They work in a variety of industries, including finance, healthcare, manufacturing, and transportation. This course may be useful to Deep Learning Engineers who want to learn more about the Transformer architecture and the BERT model.
Machine Learning Researcher
Machine Learning Researcher
Machine Learning Researchers develop new machine learning algorithms and models to solve real-world problems. They work in a variety of industries, including finance, healthcare, manufacturing, and transportation. This course may be useful to Machine Learning Researchers who want to learn more about the Transformer architecture and the BERT model.
Machine Learning Architect
Machine Learning Architect
Machine Learning Architects design and develop machine learning systems for complex problems. They work in a variety of industries, including finance, healthcare, manufacturing, and transportation. This course may be useful to Machine Learning Architects who want to learn more about the Transformer architecture and the BERT model.
Natural Language Processing Researcher
Natural Language Processing Researcher
Natural Language Processing Researchers develop new natural language processing algorithms and models to understand and generate human language. They work in a variety of industries, including finance, healthcare, manufacturing, and transportation. This course may be useful to Natural Language Processing Researchers who want to learn more about the Transformer architecture and the BERT model.
Natural Language Understanding Engineer
Natural Language Understanding Engineer
Natural Language Understanding Engineers design, develop, and maintain natural language understanding systems. They work in a variety of industries, including finance, healthcare, manufacturing, and transportation. This course may be useful to Natural Language Understanding Engineers who want to learn more about the Transformer architecture and the BERT model.
Speech Recognition Engineer
Speech Recognition Engineer
Speech Recognition Engineers design, develop, and maintain speech recognition systems. They work in a variety of industries, including finance, healthcare, manufacturing, and transportation. This course may be useful to Speech Recognition Engineers who want to learn more about the Transformer architecture and the BERT model.
Computer Vision Engineer
Computer Vision Engineer
Computer Vision Engineers design, develop, and maintain computer vision systems. They work in a variety of industries, including finance, healthcare, manufacturing, and transportation. This course may be useful to Computer Vision Engineers who want to learn more about the Transformer architecture and the BERT model.
Artificial Intelligence Engineer
Artificial Intelligence Engineer
Artificial Intelligence Engineers design, develop, and maintain artificial intelligence systems. They work in a variety of industries, including finance, healthcare, manufacturing, and transportation. This course may be useful to Artificial Intelligence Engineers who want to learn more about the Transformer architecture and the BERT model.
Natural Language Processing Engineer
Natural Language Processing Engineer
Natural Language Processing Engineers design, develop, and maintain software systems that can understand and generate human language. They work in a variety of industries, including finance, healthcare, manufacturing, and transportation. This course may be useful to Natural Language Processing Engineers who want to learn more about the Transformer architecture and the BERT model.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers research, design, develop, and deploy machine learning algorithms and models to solve real-world problems. They work in a variety of industries, including finance, healthcare, manufacturing, and transportation. This course may be useful to Machine Learning Engineers who want to learn more about the Transformer architecture and the BERT model.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems. They work in a variety of industries, including finance, healthcare, manufacturing, and transportation. This course may be useful to Software Engineers who want to learn more about the Transformer architecture and the BERT model.
Data Analyst
Data Analyst
Data Analysts collect, clean, and analyze data to identify trends and patterns. They work in a variety of industries, including finance, healthcare, manufacturing, and transportation. This course may be useful to Data Analysts who want to learn more about the Transformer architecture and the BERT model.
Research Scientist
Research Scientist
Research Scientists conduct research in a variety of scientific fields, including computer science, physics, biology, and chemistry. This course may be useful to Research Scientists who want to learn more about the Transformer architecture and the BERT model.
Data Scientist
Data Scientist
Data Scientists use scientific methods, processes, algorithms, and systems to extract knowledge and insights from data in various forms, both structured and unstructured. This course may be useful to Data Scientists who want to learn more about the Transformer architecture and the BERT model.
For more career information including salaries, visit:
OpenCourser.com/course/c2x4xz/transformer
Reading list
We've selected nine 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 with Google Cloud.
Provides a comprehensive overview of Bayesian reasoning and machine learning. It valuable resource for those who want to learn more about the theory and practice of Bayesian reasoning and machine learning.
Provides a comprehensive overview of deep learning techniques for natural language processing. It valuable resource for those who want to learn more about the theory and practice of deep learning models for natural language processing.
Provides a comprehensive overview of natural language processing techniques in Python. It valuable resource for those who want to learn more about the theory and practice of natural language processing.
Provides a comprehensive overview of speech and language processing. It valuable resource for those who want to learn more about the theory and practice of speech and language processing.
Save
Provides a comprehensive overview of machine learning concepts and techniques. It valuable resource for those who want to learn more about the theory and practice of machine learning.
Save
Provides a comprehensive overview of deep learning concepts and techniques. It valuable resource for those who want to learn more about the theory and practice of deep learning.
Provides a comprehensive overview of reinforcement learning concepts and techniques. It valuable resource for those who want to learn more about the theory and practice of reinforcement learning.
Provides a comprehensive overview of probabilistic graphical models. It valuable resource for those who want to learn more about the theory and practice of probabilistic graphical models.
Provides a practical introduction to natural language processing. It valuable resource for those who want to learn more about the theory and practice of natural language processing.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/c2x4xz/transformer
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Via
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Instructor
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Language
English
Good to know
Builds a strong foundation for learners with no prior machine learning experience
Teaches the latest deep learning architectures used in industry
Taught by Google Cloud Training, a recognized expert in the field
Introduces Transformer architecture, which is foundational for many deep learning applications
Prerequisite knowledge required: ML model implementation, TensorFlow, Python, attention mechanisms
Good fluency in English is required
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