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Introduction to Large Language Models - 한국어

Google Cloud Training

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

Traffic lights

Read about what's good
what should give you pause
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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Reviews summary

대규모 언어 모델 한국어 입문

학습자들은 이 과정이 대규모 언어 모델(LLM)에 대한 명확하고 간결한 입문서라고 말합니다. 초보자에게 매우 적합하며, 복잡한 개념을 쉽게 이해할 수 있도록 설명하는 점을 높이 평가합니다. 특히 자체 생성형 AI 앱 개발에 필요한 Google 도구에 대한 소개가 실용적이며 유용하다고 언급됩니다. 과정이 마이크로 학습 형식으로 구성되어 짧은 시간 안에 핵심 지식을 습득할 수 있어 바쁜 학습자들에게 큰 장점으로 작용합니다. 다만, 이미 LLM에 대한 기본 지식이 있는 학습자들에게는 내용이 다소 기초적이며 심화 학습의 기회가 부족할 수 있다는 의견도 있습니다.
마이크로 학습 형식으로 짧은 시간 안에 핵심 지식을 효과적으로 습득합니다.
"바쁜 직장인에게 최적화된 강의입니다. 핵심만 짧고 굵게 배울 수 있어요."
"길지 않은 시간 안에 LLM의 본질을 파악할 수 있어서 좋았습니다."
"마이크로 학습이라 부담 없이 시작할 수 있었고, 예상보다 내용이 알찼습니다."
Google의 생성형 AI 개발 도구들을 소개하여 실용적인 가이드라인을 제공합니다.
"Google Cloud 도구에 대한 소개가 특히 유용했어요. 바로 적용해볼 수 있겠습니다."
"저는 Vertex AI에 관심이 많았는데, 과정이 잘 다뤄주어서 만족합니다."
"생성형 AI 앱 개발에 필요한 Google 도구들을 전반적으로 훑어볼 수 있었습니다."
LLM 개념과 활용 사례를 명확하고 쉽게 설명하여 이해를 돕습니다.
"저는 LLM에 대해 전혀 몰랐는데, 이 강의로 기본 개념을 잘 잡았습니다."
"매우 이해하기 쉬운 방식으로 설명해 주어서 좋았습니다. 복잡한 내용을 잘 요약했어요."
"LLM 초급자에게 강력히 추천합니다. 길지 않게 핵심만 다루네요."
기초적인 내용 위주로 구성되어 심층적인 학습이나 Hands-on 경험이 부족합니다.
"LLM에 대해 이미 아는 내용이 많다면 다소 기초적이라 느낄 수 있습니다."
"저는 좀 더 기술적인 세부 사항을 기대했는데, 입문 수준에 머무른 점이 아쉬워요."
"실제 코딩이나 hands-on 부분이 부족해서 추가 학습이 필요할 것 같습니다."

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 - 한국어 with these activities:
Review basic statistics and linear algebra concepts
Reviewing these concepts will help provide a foundation for understanding the course material
Browse courses on Statistics
Show steps
  • Go over your lecture notes from previous courses in these two topics
  • Complete practice problems
Connect with professionals in the field
Mentors can provide guidance and support throughout your learning journey
Show steps
  • Attend industry events and conferences
  • Reach out to professionals on LinkedIn
  • Ask for informational interviews
Google Cloud 도구 탐구하기
자체 생성형 AI 앱 개발을 위한 Google 도구를 탐구하여 지식을 확장하여보세요.
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
These tutorials will help enhance your understanding of the theoretical concepts behind the course
Browse courses on Natural Language Processing
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
This practice will familiarize you with the practical application of LLM technology
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
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
Peer review will provide you with valuable feedback and insights
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
Building a chatbot will provide practical experience in applying LLM technology to solve a real-world problem
Browse courses on LLM Applications
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
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.
See salaries and explore the career path for Data Scientist
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.
See salaries and explore the career path for Machine Learning 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.
See salaries and explore the career path for Natural Language Processing 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.
See salaries and explore the career path for Software Engineer
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.
See salaries and explore the career path for Product 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.
See salaries and explore the career path for Marketing Manager
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.
See salaries and explore the career path for Business Analyst
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.
See salaries and explore the career path for Consultant
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.
See salaries and explore the career path for Technical Writer
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.
See salaries and explore the career path for Teacher
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.
See salaries and explore the career path for Librarian
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.
See salaries and explore the career path for Editor
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.
See salaries and explore the career path for Translator
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.
See salaries and explore the career path for Journalist
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.
See salaries and explore the career path for Writer

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.
Natural Language Processing with Transformers
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트랜스포머를 사용한 자연어 처리에 대한 입문서로서, 트랜스포머 아키텍처의 기본 사항에서 LLM의 구축 및 배포에 이르기까지 다양한 주제를 다룹니다.
(中文) Natural Language Processing with Transformers...
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深層学習教科書 ディープラーニング G検定（ジェネラリスト）公式テキスト 第2版
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생성형 AI의 개념과 기술을 포괄적으로 설명하는 일본어 책으로, LLM의 기본 원리와 응용 분야를 다룹니다.
(日本語) 深層学習教科書 ディープラーニング G検定(ジェネラリスト)公式テキスト 第2版
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Dive into Deep Learning
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딥러닝의 기본 원리를 다룹니다. LLM의 학습 프로세스를 이해하는 데 도움이 될 수 있습니다.
(English) Dive into Deep Learning
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Cross-Lingual Word Embeddings
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자연어 처리의 기본적인 딥 러닝 개념과 기술을 다루는 포괄적인 책으로, LLM의 개발과 배포에 대한 배경 지식을 제공합니다.
(English) Cross-Lingual Word Embeddings (Synthesis Lectures...
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Deep Learning for Coders with fastai and PyTorch
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다양한 LLM과 그 특성을 소개하는 온라인 리소스로서, 특정 작업에 가장 적합한 모델을 선택하는 데 도움이 됩니다.
(English) Deep Learning for Coders with Fastai and PyTorch:...
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(English) Deep Learning for Coders with fastai and PyTorch:...
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Quantum Supremacy
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AI의 잠재적 영향과 인류의 미래에 대한 사색적 탐구로서, LLM의 역할과 이 기술이 사회에 미치는 영향에 대한 통찰력을 제공합니다.
(English) Quantum Supremacy: How the Quantum Computer...
Hardcover
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(English) Quantum Supremacy: How the Quantum Computer...
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Deep Learning with TensorFlow and Keras
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딥러닝과 파이썬 프로그래밍의 기본 사항을 다룹니다. LLM의 구현을 이해하는 데 도움이 될 수 있습니다.
(English) Deep Learning with TensorFlow and Keras - Third...
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(English) Deep Learning with TensorFlow 2 and Keras - Second...
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(English) Deep Learning with TensorFlow and Keras: Build and...
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(English) Deep Learning with TensorFlow 2 and Keras:...
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The Shame Machine
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AI 시스템의 윤리적, 사회적 함의에 대한 조사로서, LLM의 사용과 관련된 잠재적 편견과 차별에 대한 인식을 높이는 데 도움이 됩니다.
The Shame Machine
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The Shame Machine
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Natural Language Processing with Python
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자연어 처리 기술의 Python 구현에 대한 종합적인 가이드입니다. LLM의 개발에 도움이 될 수 있습니다.
(English) Natural Language Processing with Python: Analyzing...
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(English) Natural Language Processing with Python: Analyzing...
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Speech and Language Processing. An Introduction to...
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자연어 처리의 기본 개념과 기술에 대한 포괄적인 교과서입니다. LLM의 배경 지식을 제공하는 데 도움이 될 수 있습니다.
(English) Speech and Language Processing An Introduction to...
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The Master Algorithm
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머신 러닝의 역사와 발전에 대한 포괄적인 개관으로서, LLM의 발전과 자연어 처리 분야에 미치는 영향에 대한 배경 지식을 제공합니다.
(English) The Master Algorithm: How the Quest for the...
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(English) The Master Algorithm: How the Quest for the...
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(Türkçe) Master algoritma : yapay ögrenme hayatimizi nasil...
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(English) Master algorithm (Korean Edition)
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In Defence of Principles
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AI의 기본 원리와 응용 분야에 대한 접근하기 쉬운 소개서로서, LLM의 개념과 이 기술이 다양한 산업에 미치는 영향에 대한 이해를 키우는 데 도움이 됩니다.
(English) In Defence of Principles: NGOs and Human Rights in...
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Active Learning
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기계 학습을 위한 자연어 데이터의 주석에 대한 가이드입니다. LLM을 훈련하기 위한 데이터 준비에 도움이 될 수 있습니다.
Active Learning
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Active Learning
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Level
Introductory
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Institution
Google Cloud
Instructor
Google Cloud Training
Language
한국어
Transcript
English
Traffic lights
Read about what's good
what should give you pause
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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