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这是一节入门级微学习课程，探讨什么是大型语言模型 (LLM)、适合的应用场景以及如何使用提示调整来提升 LLM 性能。该课程还介绍了可以帮助您开发自己的 Gen AI 应用的各种 Google 工具。
Register for this course and see more details by visiting:
OpenCourser.com/course/o8ldxp/introduction
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What's inside
Syllabus
大型语言模型简介
本单元探讨什么是大型语言模型 (LLM)、适合的应用场景以及如何使用提示调整来提升 LLM 性能。该课程还介绍了可以帮助您开发自己的 Gen AI 应用的各种 Google 工具。
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
深入了解 Gen AI， 可帮助学生提升在不断发展的领域中的竞争力。
利用 Google 工具，学生可以开发自己的生成式人工智能应用，这为他们提供了宝贵的实践经验。
通过提示调整来提升 LLM 性能，可以让学生掌握一项关键技能，从而优化其 Gen AI 应用。
Save this course
Save Introduction to Large Language Models - 简体中文 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 - 简体中文 with these
activities:
复习大型语言模型的基本知识
Show steps
复习大型语言模型的基础知识将帮助你对本课程的主题建立坚实的基础。
Browse courses on
LLM
Show steps
-
阅读课程简介和目标。
-
复习与自然语言处理相关的前提知识。
-
了解大型语言模型的优点和局限性。
复习自然语言处理的基础知识
Show steps
复习自然语言处理的基础知识可以帮助你为学习 LLM 打下坚实的基础。
Browse courses on
NLP
Show steps
-
复习自然语言处理的教科书或在线课程
-
完成自然语言处理练习题
整理课程材料
Show steps
整理课程材料可以帮助你更有效地学习和复习。
Show steps
-
收集课程讲义、作业和其他材料
-
整理材料并创建学习指南
Five other activities
Expand to see all activities and additional details
Show all eight activities
使用 Google 提供的 Colab 教程探索大型语言模型
Show steps
这些教程提供动手实践，让你可以探索大型语言模型的功能。
Browse courses on
Google Colab
Show steps
-
注册 Google Colab 帐户。
-
选择一个 Colab 教程并按照说明操作。
-
运行代码并观察输出。
使用 Google Cloud 的提示调整教程
Show steps
通过本教程，你可以学习如何使用 Google Cloud 的提示调整技术来提升 LLM 的性能。
Show steps
-
注册 Google Cloud Trial 帐户。
-
遵循教程中的分步说明。
-
在给定的数据集上实验不同的提示调整技术。
完成 LLM 提示调整练习
Show steps
LLM 提示调整练习可以帮助你提高使用 LLM 的效率。
Browse courses on
LLM
Show steps
-
查找 LLM 提示调整练习
-
完成练习并检查你的答案
撰写有关 LLM 的博客文章
Show steps
撰写有关 LLM 的博客文章可以帮助你巩固对 LLM 的理解并提高你的写作技能。
Browse courses on
LLM
Show steps
-
选择一个 LLM 相关的主题
-
研究主题并收集信息
-
撰写博客文章并进行编辑
构建一个使用 LLM 的应用程序
Show steps
构建一个使用 LLM 的应用程序可以帮助你将你的 LLM 知识付诸实践并创建一个有价值的工具。
Show steps
-
确定一个应用程序创意
-
选择一个 LLM 并学习如何使用它
-
构建应用程序并使其投入使用
复习大型语言模型的基本知识
Show steps
复习大型语言模型的基础知识将帮助你对本课程的主题建立坚实的基础。
Browse courses on
LLM
Show steps
Show steps
Show steps
- 阅读课程简介和目标。
- 复习与自然语言处理相关的前提知识。
- 了解大型语言模型的优点和局限性。
复习自然语言处理的基础知识
Show steps
复习自然语言处理的基础知识可以帮助你为学习 LLM 打下坚实的基础。
Browse courses on
NLP
Show steps
Show steps
Show steps
- 复习自然语言处理的教科书或在线课程
- 完成自然语言处理练习题
整理课程材料
Show steps
整理课程材料可以帮助你更有效地学习和复习。
Show steps
Show steps
Show steps
- 收集课程讲义、作业和其他材料
- 整理材料并创建学习指南
Five other activities
Expand to see all activities and additional details
Show all eight activities
使用 Google 提供的 Colab 教程探索大型语言模型
Show steps
这些教程提供动手实践，让你可以探索大型语言模型的功能。
Browse courses on
Google Colab
Show steps
Show steps
Show steps
- 注册 Google Colab 帐户。
- 选择一个 Colab 教程并按照说明操作。
- 运行代码并观察输出。
使用 Google Cloud 的提示调整教程
Show steps
通过本教程，你可以学习如何使用 Google Cloud 的提示调整技术来提升 LLM 的性能。
Show steps
Show steps
Show steps
- 注册 Google Cloud Trial 帐户。
- 遵循教程中的分步说明。
- 在给定的数据集上实验不同的提示调整技术。
完成 LLM 提示调整练习
Show steps
LLM 提示调整练习可以帮助你提高使用 LLM 的效率。
Browse courses on
LLM
Show steps
Show steps
Show steps
- 查找 LLM 提示调整练习
- 完成练习并检查你的答案
撰写有关 LLM 的博客文章
Show steps
撰写有关 LLM 的博客文章可以帮助你巩固对 LLM 的理解并提高你的写作技能。
Browse courses on
LLM
Show steps
Show steps
Show steps
- 选择一个 LLM 相关的主题
- 研究主题并收集信息
- 撰写博客文章并进行编辑
构建一个使用 LLM 的应用程序
Show steps
构建一个使用 LLM 的应用程序可以帮助你将你的 LLM 知识付诸实践并创建一个有价值的工具。
Show steps
Show steps
Show steps
- 确定一个应用程序创意
- 选择一个 LLM 并学习如何使用它
- 构建应用程序并使其投入使用
Career center
Learners who complete Introduction to Large Language Models - 简体中文 will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers are responsible for designing, developing, and optimizing machine learning systems. This course can help individuals interested in Machine Learning Engineering build a foundation in large language models, which are heavily leveraged in this field. The course also covers various tools and resources provided by Google that can help Machine Learning Engineers jumpstart their work on generative AI applications.
Natural Language Processing Engineer
Natural Language Processing Engineer
Natural Language Processing Engineers are responsible for developing and maintaining software that can understand and generate human language. This course may help aspiring Natural Language Processing Engineers build a foundation in large language models, which are at the core of this field. The course provides practical tips on how to optimize large language models through prompt engineering, which can prove particularly valuable for Natural Language Processing Engineers.
Computational Linguist
Computational Linguist
Computational Linguists are responsible for studying the relationship between natural language and computation. This course can help aspiring Computational Linguists develop a strong foundation in large language models. The course also provides practical tips on how to use large language models for tasks such as text classification and generation, which can be beneficial for Computational Linguists.
Artificial Intelligence Researcher
Artificial Intelligence Researcher
Artificial Intelligence Researchers are responsible for developing new and innovative artificial intelligence algorithms and applications. This course may be useful for entry-level Artificial Intelligence Researchers who are interested in exploring large language models. The course provides an overview of the fundamental concepts and applications of large language models, which can be helpful for researchers in this field.
Software Engineer
Software Engineer
Software Engineers are responsible for designing, developing, and maintaining software applications. This course may be useful for Software Engineers who are interested in building generative AI applications. The course provides an overview of the latest advancements in large language models and shares best practices for prompt engineering, which can help Software Engineers write efficient and effective prompts.
Product Manager
Product Manager
Product Managers are responsible for defining and managing the development of software products. This course may be useful for Product Managers who are interested in incorporating large language models into their products. The course provides practical tips on how to identify use cases for large language models and how to design products that leverage these technologies effectively.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts are responsible for developing and implementing quantitative models to analyze financial data. This course may be useful for Quantitative Analysts who are interested in using large language models to improve their models. The course provides practical tips on how to use large language models for tasks such as time series forecasting and risk analysis, which can be beneficial for Quantitative Analysts.
Content Creator
Content Creator
Content Creators are responsible for creating and publishing content for various platforms and audiences. This course may be useful for Content Creators who are interested in using large language models to create high-quality and engaging content. The course also covers Google tools that can help automate parts of the content creation process using large language models, which can improve efficiency.
Marketing Manager
Marketing Manager
Marketing Managers are responsible for planning and executing marketing campaigns. This course may be useful for Marketing Managers who are interested in using large language models to improve the effectiveness of their campaigns. The course provides practical tips on how to use large language models for tasks such as content creation and marketing analytics, which can help Marketing Managers achieve better results.
Business Analyst
Business Analyst
Business Analysts are responsible for analyzing business processes and identifying opportunities for improvement. This course can help aspiring Business Analysts build a foundation in large language models, which can be useful for tasks such as market research and competitive analysis. The course also shares case studies of real-world applications of large language models in a business context.
Data Scientist
Data Scientist
Data Scientists are responsible for collecting, cleaning, and safeguarding data according to industry standards. This course can help aspiring Data Scientists build a foundation in large language models, which may allow them to explore and analyze complex datasets more efficiently. The course also covers applications of large language models that could prove useful in this role.
UX Designer
UX Designer
UX Designers are responsible for designing and evaluating user experiences for software products. This course may be useful for UX Designers who are interested in using large language models to improve the user experience of their products. The course provides practical tips on how to use large language models for tasks such as user research and prototyping, which can improve the quality of user experience.
Consultant
Consultant
Consultants are responsible for providing advice and guidance to businesses and organizations. This course may be useful for Consultants who are interested in advising their clients on how to use large language models. The course provides practical tips on how to identify use cases for large language models and how to develop a strategy for implementing these technologies.
Data Analyst
Data Analyst
Data Analysts are responsible for analyzing business and industry-related data. They help organizations make informed decisions. This course may help entry-level Data Analysts understand how large language models are created and applied to different use cases, which could allow them to perform exploratory data analysis more efficiently. The course also covers prompt engineering techniques which Data Analysts may find useful in building efficient queries.
Entrepreneur
Entrepreneur
Entrepreneurs are responsible for starting and running their own businesses. This course may be useful for Entrepreneurs who are interested in using large language models in their business. The course provides practical tips on how to identify use cases for large language models and how to develop a strategy for implementing these technologies.
For more career information including salaries, visit:
OpenCourser.com/course/o8ldxp/introduction
Reading list
We've selected seven 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 - 简体中文.
Save
本书是人工智能领域的经典教科书，提供了人工智能的基础知识和前沿进展，对于理解大语言模型所依赖的人工智能原理有帮助。
这本书提供了使用 Transformer 模型进行机器学习的实用指南，它涵盖了模型训练、微调和部署。
这本书提供了使用神经网络进行自然语言处理的全面介绍，它涵盖了基础知识、模型架构和应用程序。
这本书提供了深入的自然语言处理教程，它涵盖了从基础知识到高级模型和应用程序的所有内容。
这本书提供了对循环神经网络的全面介绍，它涵盖了基本概念、模型架构和训练技术。
Save
这本书提供了对深度学习的全面介绍，它涵盖了基础知识、模型架构和训练技术。
这本书提供了对自然语言处理的全面介绍，它涵盖了基础知识、工具和应用程序。
For more information about how these books relate to this course, visit:
OpenCourser.com/course/o8ldxp/introduction
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Level
Introductory
Via
Coursera
Institution
Google Cloud
Instructor
Google Cloud Training
Language
中文
Transcript
English
Good to know
深入了解 Gen AI， 可帮助学生提升在不断发展的领域中的竞争力。
利用 Google 工具，学生可以开发自己的生成式人工智能应用，这为他们提供了宝贵的实践经验。
通过提示调整来提升 LLM 性能，可以让学生掌握一项关键技能，从而优化其 Gen AI 应用。
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