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Google Cloud Training
Ini adalah kursus pengantar pembelajaran mikro yang membahas definisi model bahasa besar (LLM), kasus penggunaannya, dan cara menggunakan prompt tuning untuk meningkatkan performa LLM. Kursus ini juga membahas beberapa alat Google yang dapat membantu Anda mengembangkan aplikasi Gen AI Anda sendiri.
Register for this course and see more details by visiting:
OpenCourser.com/course/tn7u9n/introduction
What's inside
Syllabus
Pengantar Model Bahasa Besar (LLM)
Modul ini membahas definisi model bahasa besar (LLM), kasus penggunaannya, dan cara menggunakan prompt tuning untuk meningkatkan performa LLM. Kursus ini juga membahas beberapa alat Google yang dapat membantu Anda mengembangkan aplikasi Gen AI Anda sendiri.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Enhances skill sets relevant for developers and Gen AI practitioners alike
Provides a firm understanding of LLM models for various use cases
Supports learners in developing Gen AI applications using Google tools
Save this course
Save Introduction to Large Language Models - Bahasa Indonesia 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 Introduction to Large Language Models - Bahasa Indonesia with these
activities:
Review key NLP concepts
Show steps
Strengthen your foundation by reviewing essential NLP concepts.
Browse courses on
Natural Language Processing
Show steps
-
Review your notes or textbooks from previous NLP courses or tutorials.
-
Take online quizzes or practice exercises to test your understanding.
-
Discuss NLP concepts with classmates or colleagues to clarify your understanding.
Practice programming in Python or R
Show steps
Strengthen your programming skills in preparation for working with LLM.
Browse courses on
Python
Show steps
-
Review basic programming concepts in Python or R.
-
Complete coding exercises or puzzles to practice your skills.
-
Work on a small programming project to apply your knowledge.
Form a study group with classmates
Show steps
Collaborate with peers to enhance your learning experience.
Show steps
-
Reach out to classmates and form a study group of 3-5 people.
-
Schedule regular study sessions to discuss course materials, work on assignments together, and quiz each other.
-
Take turns leading discussions and sharing insights.
Three other activities
Expand to see all activities and additional details
Show all six activities
Practice using Google's NLP API
Show steps
Solidify your understanding of NLP concepts by practicing with Google's NLP API.
Browse courses on
Natural Language Processing
Show steps
-
Set up a Google Cloud Platform account and create a project.
-
Explore the NLP API documentation.
-
Write code to use the API for a specific NLP task, such as text classification or sentiment analysis.
-
Test your code and refine it as needed.
Explore Google's AI Platform
Show steps
Expand your knowledge of Google's AI capabilities by exploring their AI Platform.
Browse courses on
AI Platform
Show steps
-
Visit the AI Platform website and browse the available products and services.
-
Choose a specific product to learn more about, such as AutoML or Vertex AI.
-
Follow the tutorials and documentation for that product to gain hands-on experience.
Develop a Gen AI application prototype
Show steps
Apply your newfound knowledge by creating a prototype of a Gen AI application.
Browse courses on
Generative AI
Show steps
-
Define the problem you want to solve or the opportunity you want to create with Gen AI.
-
Choose a specific Gen AI technique to use, such as text generation or image generation.
-
Develop a prototype of your application using a suitable programming language and framework.
-
Test your prototype and refine it based on feedback.
-
Present your prototype to others for feedback and discussion.
Review key NLP concepts
Show steps
Strengthen your foundation by reviewing essential NLP concepts.
Browse courses on
Natural Language Processing
Show steps
Show steps
Show steps
- Review your notes or textbooks from previous NLP courses or tutorials.
- Take online quizzes or practice exercises to test your understanding.
- Discuss NLP concepts with classmates or colleagues to clarify your understanding.
Practice programming in Python or R
Show steps
Strengthen your programming skills in preparation for working with LLM.
Browse courses on
Python
Show steps
Show steps
Show steps
- Review basic programming concepts in Python or R.
- Complete coding exercises or puzzles to practice your skills.
- Work on a small programming project to apply your knowledge.
Form a study group with classmates
Show steps
Collaborate with peers to enhance your learning experience.
Show steps
Show steps
Show steps
- Reach out to classmates and form a study group of 3-5 people.
- Schedule regular study sessions to discuss course materials, work on assignments together, and quiz each other.
- Take turns leading discussions and sharing insights.
Three other activities
Expand to see all activities and additional details
Show all six activities
Practice using Google's NLP API
Show steps
Solidify your understanding of NLP concepts by practicing with Google's NLP API.
Browse courses on
Natural Language Processing
Show steps
Show steps
Show steps
- Set up a Google Cloud Platform account and create a project.
- Explore the NLP API documentation.
- Write code to use the API for a specific NLP task, such as text classification or sentiment analysis.
- Test your code and refine it as needed.
Explore Google's AI Platform
Show steps
Expand your knowledge of Google's AI capabilities by exploring their AI Platform.
Browse courses on
AI Platform
Show steps
Show steps
Show steps
- Visit the AI Platform website and browse the available products and services.
- Choose a specific product to learn more about, such as AutoML or Vertex AI.
- Follow the tutorials and documentation for that product to gain hands-on experience.
Develop a Gen AI application prototype
Show steps
Apply your newfound knowledge by creating a prototype of a Gen AI application.
Browse courses on
Generative AI
Show steps
Show steps
Show steps
- Define the problem you want to solve or the opportunity you want to create with Gen AI.
- Choose a specific Gen AI technique to use, such as text generation or image generation.
- Develop a prototype of your application using a suitable programming language and framework.
- Test your prototype and refine it based on feedback.
- Present your prototype to others for feedback and discussion.
Career center
Learners who complete Introduction to Large Language Models - Bahasa Indonesia will develop knowledge and skills
that may be useful to these careers:
Natural Language Processing Engineer
Natural Language Processing Engineer
Natural Language Processing Engineers develop and apply techniques to enable computers to understand and generate human language. This course is highly relevant as it delves into the fundamentals of LLMs, which are at the forefront of NLP research. By gaining expertise in LLMs, NLP Engineers can create more sophisticated and effective language-based applications.
Computational Linguist
Computational Linguist
Computational Linguists use computational techniques to study and model human language. This course can be highly beneficial as it provides insights into the theoretical underpinnings of LLMs and their applications in language-related tasks. By understanding the linguistic principles behind LLMs, Computational Linguists can develop more robust and accurate language models.
Data Scientist
Data Scientist
Data Scientists use scientific methods to extract knowledge and insights from data. This course can be helpful as it introduces the concepts of LLMs and their applications in data science. By understanding how to use LLMs effectively, Data Scientists can automate certain aspects of their work, such as feature engineering and data exploration, leading to more efficient and accurate data analysis.
AI Researcher
AI Researcher
AI Researchers explore the theoretical and practical aspects of artificial intelligence. This course can be useful as it offers an introduction to the field of LLMs and their potential impact on various industries. By understanding the capabilities and limitations of LLMs, AI Researchers can stay at the forefront of AI advancements and contribute to the development of innovative AI solutions.
Content Writer
Content Writer
Content Writers create and maintain written content for websites, blogs, and other platforms. This course can be beneficial as it provides an introduction to LLMs and their applications in content writing. By understanding how to use LLMs effectively, Content Writers can automate certain aspects of their work, such as generating content ideas or creating summaries of existing content. This can lead to increased productivity and improved content quality.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems. This course may be helpful as it provides an introduction to LLMs and their potential applications in software development. By understanding how LLMs can be integrated into software systems, Software Engineers can create more intelligent and user-friendly applications.
Customer Success Manager
Customer Success Manager
Customer Success Managers ensure that customers are satisfied with a company's products or services. This course can be beneficial as it provides an understanding of how LLMs can be used to automate certain aspects of customer support and improve customer satisfaction. By leveraging the power of LLMs, Customer Success Managers can provide more efficient and personalized support to customers.
Business Analyst
Business Analyst
Business Analysts analyze business processes and systems to identify opportunities for improvement. This course can be helpful as it introduces the applications of LLMs in business analysis. By understanding how LLMs can be used to automate certain tasks and extract insights from data, Business Analysts can improve the efficiency and accuracy of their work.
Project Manager
Project Manager
Project Managers plan, execute, and close projects. This course may be helpful as it provides an understanding of how LLMs can be used to improve project management processes. By leveraging the insights from LLMs, Project Managers can make data-driven decisions and improve the efficiency and success of their projects.
Data Analyst
Data Analyst
Data Analysts collect, clean, and analyze data from various sources to extract meaningful insights and trends. This course can be helpful as it provides an understanding of how to use prompt tuning to improve the performance of LLMs, which can enhance the accuracy and efficiency of data analysis tasks. By leveraging the power of LLMs, Data Analysts can automate certain aspects of their work, freeing up time for more complex and strategic analysis.
Marketing Specialist
Marketing Specialist
Marketing Specialists develop and implement marketing campaigns. This course may be helpful as it provides an understanding of how LLMs can be used to enhance marketing strategies. By leveraging the insights from LLMs, Marketing Specialists can create more targeted and effective campaigns.
Salesperson
Salesperson
Salespeople sell products or services to customers. This course may be useful as it provides an introduction to LLMs and their applications in sales. By understanding how LLMs can be used to automate certain tasks and extract insights from data, Salespeople can improve the efficiency and accuracy of their work.
Product Manager
Product Manager
Product Managers oversee the development and launch of products. This course can be useful as it provides an understanding of how LLMs can be used to enhance product development. By leveraging the insights from LLMs, Product Managers can make data-driven decisions and create products that better meet the needs of users.
Technical Writer
Technical Writer
Technical Writers create and maintain technical documentation. This course may be useful as it provides an understanding of how LLMs can be used to automate certain aspects of technical writing, such as generating documentation from code or creating summaries of technical documents. By leveraging the power of LLMs, Technical Writers can improve the quality and efficiency of their work.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design, build, and maintain machine learning models. This course can be useful as it provides an introduction to the field of LLMs and their applications. By understanding how LLMs work and how to use them effectively, Machine Learning Engineers can develop more accurate and efficient models.
For more career information including salaries, visit:
OpenCourser.com/course/tn7u9n/introduction
Reading list
We've selected eight 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
Introduction to Large Language Models - Bahasa Indonesia.
Provides a comprehensive overview of language models, including LLMs. It covers the history, theory, and applications of language models, making it a valuable resource for understanding the broader context of LLMs.
Provides a comprehensive overview of deep learning techniques for NLP. It covers the theory and implementation of these techniques, including those used in LLMs, making it a valuable resource for understanding the technical foundations of LLMs.
This classic textbook provides a comprehensive overview of speech and language processing, including the fundamentals of NLP. It covers the topics that underlie LLMs, making it a valuable resource for understanding the theoretical foundations of these models.
Explores ethical and societal implications of advanced AI systems, including LLM. It raises important questions about the responsible development and deployment of LLM and their potential impact on society.
Save
Serves as a comprehensive reference on deep learning, the underlying technology behind LLM. It provides a solid foundation for understanding the theoretical concepts and practical applications of deep learning.
Provides a comprehensive overview of natural language understanding, including techniques and models for tasks such as text classification, question answering, and machine translation. It offers a broader perspective on LLM and their applications.
Provides a comprehensive introduction to statistical learning, including supervised and unsupervised learning methods. It offers a strong foundation for understanding the statistical principles underlying LLM.
Provides a comprehensive introduction to machine learning from a probabilistic perspective, covering fundamental concepts and algorithms. It offers a valuable foundation for understanding the theoretical underpinnings of LLM.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/tn7u9n/introduction
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Level
Introductory
Via
Coursera
Institution
Google Cloud
Instructor
Google Cloud Training
Language
English
Transcript
Bahasa Indonesia
Good to know
Enhances skill sets relevant for developers and Gen AI practitioners alike
Provides a firm understanding of LLM models for various use cases
Supports learners in developing Gen AI applications using Google tools
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