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Kursus ini memperkenalkan Anda pada arsitektur Transformer dan model Representasi Encoder Dua Arah dari Transformer (Bidirectional Encoder Representations from Transformers atau BERT). Anda akan belajar tentang komponen utama arsitektur Transformer, seperti mekanisme self-attention, dan cara penggunaannya untuk membangun model BERT. Anda juga akan belajar tentang berbagai tugas yang dapat memanfaatkan BERT, seperti klasifikasi teks, menjawab pertanyaan, dan inferensi natural language. Kursus ini diperkirakan memakan waktu sekitar 45 menit untuk menyelesaikannya.
Register for this course and see more details by visiting:
OpenCourser.com/course/h1ptq0/transformer
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What's inside
Syllabus
Model Transformer dan BERT: Ringkasan
Dalam modul ini Anda akan belajar tentang komponen utama arsitektur Transformer, seperti mekanisme self-attention, dan cara penggunaannya untuk membangun model BERT. Anda juga akan belajar tentang berbagai tugas yang dapat memanfaatkan BERT, seperti klasifikasi teks, menjawab pertanyaan, dan inferensi natural language.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
May be too technical for non-specialist audiences
Beginners will highly benefit from this introductory course
Taught by a team of experienced experts in the field
Offers a strong foundation in the fundamentals covered in this course
Save this course
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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 - Bahasa Indonesia with these
activities:
Review Natural Language Processing Concepts
Show steps
Review the core concepts and techniques in natural language processing to strengthen the foundation.
Show steps
-
Identify the key concepts in NLP
-
Review the different NLP tasks and their applications
-
Explore the state-of-the-art NLP models and algorithms
BERT Resource Collection
Show steps
Gather and compile useful resources related to BERT, such as articles, tutorials, and code repositories.
Show steps
-
Browse various sources to identify relevant resources
-
Organize the resources into a structured and accessible format
-
Share the collection with other learners or interested parties
Organize Course Materials
Show steps
Organize and study the course materials, including notes, assignments, and quizzes.
Show steps
-
Gather all course materials
-
Review materials and identify key concepts
-
Create a study plan
One other activity
Expand to see all activities and additional details
Show all four activities
BERT Architecture Diagram
Show steps
Create a visual representation of the BERT architecture, including its components and their relationships.
Browse courses on
Transformer Architecture
Show steps
-
Identify the key components of the BERT architecture
-
Arrange the components in a logical and visually appealing manner
-
Label the components and their connections
Review Natural Language Processing Concepts
Show steps
Review the core concepts and techniques in natural language processing to strengthen the foundation.
Show steps
Show steps
Show steps
- Identify the key concepts in NLP
- Review the different NLP tasks and their applications
- Explore the state-of-the-art NLP models and algorithms
BERT Resource Collection
Show steps
Gather and compile useful resources related to BERT, such as articles, tutorials, and code repositories.
Show steps
Show steps
Show steps
- Browse various sources to identify relevant resources
- Organize the resources into a structured and accessible format
- Share the collection with other learners or interested parties
Organize Course Materials
Show steps
Organize and study the course materials, including notes, assignments, and quizzes.
Show steps
Show steps
Show steps
- Gather all course materials
- Review materials and identify key concepts
- Create a study plan
One other activity
Expand to see all activities and additional details
Show all four activities
BERT Architecture Diagram
Show steps
Create a visual representation of the BERT architecture, including its components and their relationships.
Browse courses on
Transformer Architecture
Show steps
Show steps
Show steps
- Identify the key components of the BERT architecture
- Arrange the components in a logical and visually appealing manner
- Label the components and their connections
Career center
Learners who complete Transformer Models and BERT Model - Bahasa Indonesia will develop knowledge and skills
that may be useful to these careers:
Natural Language Processing Engineer
Natural Language Processing Engineer
As a Natural Language Processing Engineer, you will build and use machine learning models, like those based on Transformer Models and BERT Model, to help computers understand, interpret, and work with text and other language data. This course will help you build a foundation in the architectures and principles behind these models, allowing you to better understand how they can be applied to your projects in the future.
Machine Learning Engineer
Machine Learning Engineer
As a Machine Learning Engineer, you will play a central role in designing, building, deploying and maintaining artificial intelligence and data science applications. This course may be useful for you as, increasingly, Machine Learning Engineers are expected to be able to apply their skills to natural language processing tasks.
Data Scientist
Data Scientist
As a Data Scientist, you will work with complex datasets to identify trends and patterns, and often use these to build machine learning models. This course may be useful for you as more and more of these models, including those used in natural language processing, leverage the techniques covered in the course.
Software Engineer
Software Engineer
As a Software Engineer, you will design, build, and maintain software systems. This course may be useful for you if you work with large or complex text-based datasets as part of your projects.
Computational Linguist
Computational Linguist
As a Computational Linguist, you will study the structure and meaning of natural languages, which means studying how computers can understand and produce human language. This course may be useful for you as models built on Transformer Models and BERT Model are having a transformative effect on computational linguistics.
Linguist
Linguist
As a Linguist, you will study languages, typically focusing on a particular language or language family. This course may be useful for you if you want to incorporate more technical skills into your work, or if you find yourself increasingly working with computational linguistics tools and methods.
Technical Writer
Technical Writer
As a Technical Writer, you will create user guides, manuals, and other documentation that helps people understand and use technical products and services. This course may be useful for you as you will need to be able to clearly explain technical information and trends to a broad range of audiences.
Content Writer
Content Writer
As a Content Writer, you will write articles, blog posts, and other content that appears online. Increasingly, content writers are expected to be able to work with content-related ML models and platforms, which this course will give you a great overview for.
Product Manager
Product Manager
As a Product Manager, you will be responsible for the development, launch, and ongoing management of products. This course may be useful for you if you work on products that involve natural language processing and machine learning.
Project Manager
Project Manager
As a Project Manager, you will plan, execute, and close out projects. This course may be useful for you if you manage projects that involve natural language processing and machine learning.
Sales Manager
Sales Manager
As a Sales Manager, you will lead and manage a team of sales professionals. This course may be useful for you if you work with clients who use natural language processing and machine learning products or services.
Business Analyst
Business Analyst
As a Business Analyst, you will identify and analyze business needs, and develop solutions to improve business processes. This course may be useful for you if you work with natural language processing and machine learning technologies.
Marketing Manager
Marketing Manager
As a Marketing Manager, you will research, plan, and manage marketing campaigns for products and services. This course may be useful for you if you work on campaigns that include natural language processing and machine learning applications.
Science Writer
Science Writer
As a Science Writer, you will specialize in writing about scientific and technical topics, often for a non-specialist audience. This course may be useful for you as natural language processing models such as Transformer Models and BERT Model are receiving a lot of attention in this field.
Quality Assurance Analyst
Quality Assurance Analyst
As a Quality Assurance Analyst, you will ensure that software products meet quality standards. This course may be useful for you if you work with natural language processing and machine learning technologies.
For more career information including salaries, visit:
OpenCourser.com/course/h1ptq0/transformer
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
Transformer Models and BERT Model - Bahasa Indonesia.
Provides a comprehensive overview of the cognitive science of language. It covers a wide range of topics, including the nature of language, the relationship between language and thought, and the acquisition of language.
Provides a comprehensive overview of the sociolinguistics of language. It covers a wide range of topics, including the relationship between language and culture, the role of language in social interaction, and the acquisition of language.
Provides a comprehensive overview of natural language processing techniques using Python. It covers a wide range of topics, including text preprocessing, machine learning, and deep learning.
Provides a comprehensive overview of speech and language processing. It covers a wide range of topics, including speech recognition, natural language understanding, and natural language generation.
Provides a comprehensive overview of the statistical foundations of natural language processing. It covers a wide range of topics, including probability theory, information theory, and machine learning.
Provides a comprehensive overview of computational linguistics. It covers a wide range of topics, including natural language processing, machine learning, and artificial intelligence.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/h1ptq0/transformer
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Level
Advanced
Via
Coursera
Institution
Google Cloud
Instructor
Google Cloud Training
Language
English
Transcript
Bahasa Indonesia
Good to know
May be too technical for non-specialist audiences
Beginners will highly benefit from this introductory course
Taught by a team of experienced experts in the field
Offers a strong foundation in the fundamentals covered in this course
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