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대규모 언어 모델(LLM)이란 무엇이고, LLM을 활용할 수 있는 사용 사례로는 어떤 것이 있으며, 프롬프트 조정을 사용해 LLM 성능을 개선하는 방법을 알아보는 입문용 마이크로 학습 과정입니다. 또한 자체 생성형 AI 앱을 개발하는 데 도움이 되는 Google 도구에 대해서도 다룹니다.
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What's inside
Syllabus
대규모 언어 모델 소개
이 모듈에서는 대규모 언어 모델(LLM)이란 무엇이고, LLM을 활용할 수 있는 사용 사례로는 어떤 것이 있으며, 프롬프트 조정을 사용해 LLM 성능을 개선하는 방법을 알아봅니다. 또한 자체 생성형 AI 앱을 개발하는 데 도움이 되는 Google 도구에 대해서도 다룹니다.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
탐구되는 주제는 거의 모든 산업에서 현재 표준화된 기술입니다
과정의 명확한 목표를 정의하고 이를 달성하는 방법에 대한 전략을 제공합니다
대규모 언어 모델의 특성을 분석하고 그 특성을 활용하여 다양한 분야에서 성능을 향상하는 방법에 대한 깊이 있는 이해를 제공합니다
Google Cloud에서 널리 사용되고 신뢰를 받는 도구의 활용법을 보여줍니다
자체 생성 AI 앱을 만드는 것과 같이 현실적인 상황의 문제점을 해결할 수 있는 실무적인 기술을 개발합니다
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Activities
Be better prepared
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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 - 한국어 with these
activities:
Review basic statistics and linear algebra concepts
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Reviewing these concepts will help provide a foundation for understanding the course material
Browse courses on
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Go over your lecture notes from previous courses in these two topics
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Complete practice problems
Connect with professionals in the field
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Attend industry events and conferences
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Reach out to professionals on LinkedIn
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Ask for informational interviews
Google Cloud 도구 탐구하기
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자체 생성형 AI 앱 개발을 위한 Google 도구를 탐구하여 지식을 확장하여보세요.
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Google Cloud 플랫폼 설명서 및 자습서 검토하기
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특정 도구를 위한 실습 세션 참여하기
Five other activities
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Follow tutorials on natural language processing and machine learning
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Browse courses on
Natural Language Processing
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Find tutorials from reputable sources such as Coursera, edX, or YouTube
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Complete the tutorials at your own pace
-
Take notes and ask questions
Practice using LLM APIs
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This practice will familiarize you with the practical application of LLM technology
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-
Sign up for an LLM platform
-
Explore the available APIs
-
Write code to interact with the APIs
-
Experiment with different prompts and parameters
자료 모음하기
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대규모 언어 모델, 프롬프트 조정 및 관련 도구에 대한 포괄적인 리소스 모음을 만들어 지식을 체계화하고 참조하기 쉽게 만들어보세요.
Show steps
-
연구 논문, 블로그 게시물 및 기타 정보 출처 수집하기
-
자료를 주제별로 구성 및 정리하기
Participate in peer review sessions
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Peer review will provide you with valuable feedback and insights
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-
Find a study group or partner
-
Share your work with your peers
-
Provide constructive feedback
-
Incorporate feedback into your own work
Develop a simple chatbot using an LLM API
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Building a chatbot will provide practical experience in applying LLM technology to solve a real-world problem
Show steps
-
Design the chatbot's functionality
-
Choose an appropriate LLM API
-
Develop the chatbot using the LLM API
-
Test and refine the chatbot
Review basic statistics and linear algebra concepts
Show steps
Reviewing these concepts will help provide a foundation for understanding the course material
Browse courses on
Statistics
Show steps
Show steps
Show steps
- Go over your lecture notes from previous courses in these two topics
- Complete practice problems
Connect with professionals in the field
Show steps
Mentors can provide guidance and support throughout your learning journey
Show steps
Show steps
Show steps
- Attend industry events and conferences
- Reach out to professionals on LinkedIn
- Ask for informational interviews
Google Cloud 도구 탐구하기
Show steps
자체 생성형 AI 앱 개발을 위한 Google 도구를 탐구하여 지식을 확장하여보세요.
Show steps
Show steps
Show steps
- Google Cloud 플랫폼 설명서 및 자습서 검토하기
- 특정 도구를 위한 실습 세션 참여하기
Five other activities
Expand to see all activities and additional details
Show all eight activities
Follow tutorials on natural language processing and machine learning
Show steps
These tutorials will help enhance your understanding of the theoretical concepts behind the course
Browse courses on
Natural Language Processing
Show steps
Show steps
Show steps
- Find tutorials from reputable sources such as Coursera, edX, or YouTube
- Complete the tutorials at your own pace
- Take notes and ask questions
Practice using LLM APIs
Show steps
This practice will familiarize you with the practical application of LLM technology
Show steps
Show steps
Show steps
- Sign up for an LLM platform
- Explore the available APIs
- Write code to interact with the APIs
- Experiment with different prompts and parameters
자료 모음하기
Show steps
대규모 언어 모델, 프롬프트 조정 및 관련 도구에 대한 포괄적인 리소스 모음을 만들어 지식을 체계화하고 참조하기 쉽게 만들어보세요.
Show steps
Show steps
Show steps
- 연구 논문, 블로그 게시물 및 기타 정보 출처 수집하기
- 자료를 주제별로 구성 및 정리하기
Participate in peer review sessions
Show steps
Peer review will provide you with valuable feedback and insights
Show steps
Show steps
Show steps
- Find a study group or partner
- Share your work with your peers
- Provide constructive feedback
- Incorporate feedback into your own work
Develop a simple chatbot using an LLM API
Show steps
Building a chatbot will provide practical experience in applying LLM technology to solve a real-world problem
Show steps
Show steps
Show steps
- Design the chatbot's functionality
- Choose an appropriate LLM API
- Develop the chatbot using the LLM API
- Test and refine the chatbot
Career center
Learners who complete Introduction to Large Language Models - 한국어 will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
As a Data Scientist, you will use your knowledge of large language models (LLMs) to develop innovative solutions to business problems. You will use LLMs to analyze data, identify trends, and make predictions. You will also use LLMs to create natural language processing (NLP) applications, such as chatbots and language translation systems.
Machine Learning Engineer
Machine Learning Engineer
As a Machine Learning Engineer, you will use your knowledge of LLMs to develop and deploy machine learning models. You will use LLMs to improve the accuracy and efficiency of machine learning models. You will also use LLMs to develop new machine learning algorithms.
Natural Language Processing Engineer
Natural Language Processing Engineer
As a Natural Language Processing Engineer, you will use your knowledge of LLMs to develop and deploy NLP applications. You will use LLMs to analyze text data, identify patterns, and extract meaning. You will also use LLMs to develop new NLP algorithms.
Software Engineer
Software Engineer
As a Software Engineer, you will use your knowledge of LLMs to develop and deploy software applications. You will use LLMs to improve the quality and efficiency of software development. You will also use LLMs to develop new software development tools.
Product Manager
Product Manager
As a Product Manager, you will use your knowledge of LLMs to develop and launch new products. You will use LLMs to understand customer needs, identify market opportunities, and develop product roadmaps. You will also use LLMs to create product demos and marketing materials.
Marketing Manager
Marketing Manager
As a Marketing Manager, you will use your knowledge of LLMs to develop and execute marketing campaigns. You will use LLMs to create marketing content, identify target audiences, and measure campaign performance. You will also use LLMs to develop new marketing strategies.
Business Analyst
Business Analyst
As a Business Analyst, you will use your knowledge of LLMs to analyze business processes and identify areas for improvement. You will use LLMs to create data models, develop process maps, and write business requirements documents. You will also use LLMs to present your findings to stakeholders.
Consultant
Consultant
As a Consultant, you will use your knowledge of LLMs to help clients solve business problems. You will use LLMs to analyze data, identify trends, and make recommendations. You will also use LLMs to develop and implement solutions.
Technical Writer
Technical Writer
As a Technical Writer, you will use your knowledge of LLMs to create technical documentation. You will use LLMs to write user guides, white papers, and training materials. You will also use LLMs to create marketing materials and website content.
Teacher
Teacher
As a Teacher, you will use your knowledge of LLMs to teach students about language and literature. You will use LLMs to create lesson plans, develop teaching materials, and assess student learning.
Librarian
Librarian
As a Librarian, you will use your knowledge of LLMs to catalog and organize information. You will use LLMs to create search tools, develop library collections, and provide reference services. You will also use LLMs to teach patrons about information literacy.
Editor
Editor
As an Editor, you will use your knowledge of LLMs to edit and proofread text. You will use LLMs to check for grammar and spelling errors, improve readability, and ensure consistency. You will also use LLMs to develop style guides and editorial policies.
Translator
Translator
As a Translator, you will use your knowledge of LLMs to translate text from one language to another. You will use LLMs to ensure accuracy and fluency. You will also use LLMs to develop translation tools and resources.
Journalist
Journalist
As a Journalist, you will use your knowledge of LLMs to research and write news stories. You will use LLMs to gather information, verify facts, and write articles. You will also use LLMs to develop story ideas and create multimedia content.
Writer
Writer
As a Writer, you will use your knowledge of LLMs to create written content for a variety of purposes. You will use LLMs to write fiction, non-fiction, poetry, and screenplays. You will also use LLMs to develop writing skills and techniques.
For more career information including salaries, visit:
OpenCourser.com/course/drgcde/introduction
Reading list
We've selected 13 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 - 한국어.
트랜스포머를 사용한 자연어 처리에 대한 입문서로서, 트랜스포머 아키텍처의 기본 사항에서 LLM의 구축 및 배포에 이르기까지 다양한 주제를 다룹니다.
생성형 AI의 개념과 기술을 포괄적으로 설명하는 일본어 책으로, LLM의 기본 원리와 응용 분야를 다룹니다.
딥러닝의 기본 원리를 다룹니다. LLM의 학습 프로세스를 이해하는 데 도움이 될 수 있습니다.
자연어 처리의 기본적인 딥 러닝 개념과 기술을 다루는 포괄적인 책으로, LLM의 개발과 배포에 대한 배경 지식을 제공합니다.
다양한 LLM과 그 특성을 소개하는 온라인 리소스로서, 특정 작업에 가장 적합한 모델을 선택하는 데 도움이 됩니다.
Save
AI의 잠재적 영향과 인류의 미래에 대한 사색적 탐구로서, LLM의 역할과 이 기술이 사회에 미치는 영향에 대한 통찰력을 제공합니다.
딥러닝과 파이썬 프로그래밍의 기본 사항을 다룹니다. LLM의 구현을 이해하는 데 도움이 될 수 있습니다.
Save
AI 시스템의 윤리적, 사회적 함의에 대한 조사로서, LLM의 사용과 관련된 잠재적 편견과 차별에 대한 인식을 높이는 데 도움이 됩니다.
자연어 처리 기술의 Python 구현에 대한 종합적인 가이드입니다. LLM의 개발에 도움이 될 수 있습니다.
자연어 처리의 기본 개념과 기술에 대한 포괄적인 교과서입니다. LLM의 배경 지식을 제공하는 데 도움이 될 수 있습니다.
Save
머신 러닝의 역사와 발전에 대한 포괄적인 개관으로서, LLM의 발전과 자연어 처리 분야에 미치는 영향에 대한 배경 지식을 제공합니다.
AI의 기본 원리와 응용 분야에 대한 접근하기 쉬운 소개서로서, LLM의 개념과 이 기술이 다양한 산업에 미치는 영향에 대한 이해를 키우는 데 도움이 됩니다.
Save
기계 학습을 위한 자연어 데이터의 주석에 대한 가이드입니다. LLM을 훈련하기 위한 데이터 준비에 도움이 될 수 있습니다.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/drgcde/introduction
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Level
Introductory
Via
Coursera
Institution
Google Cloud
Instructor
Google Cloud Training
Language
한국어
Transcript
English
Good to know
탐구되는 주제는 거의 모든 산업에서 현재 표준화된 기술입니다
과정의 명확한 목표를 정의하고 이를 달성하는 방법에 대한 전략을 제공합니다
대규모 언어 모델의 특성을 분석하고 그 특성을 활용하여 다양한 분야에서 성능을 향상하는 방법에 대한 깊이 있는 이해를 제공합니다
Google Cloud에서 널리 사용되고 신뢰를 받는 도구의 활용법을 보여줍니다
자체 생성 AI 앱을 만드는 것과 같이 현실적인 상황의 문제점을 해결할 수 있는 실무적인 기술을 개발합니다
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