成为会员时间：2026
Google DeepMind: 02 Represent Your Language Data
Earned Mar 21, 2026 EDT
打造您的首个 Gemini Enterprise 应用
Earned Mar 16, 2026 EDT
企业智能体及其应用场景
Earned Mar 16, 2026 EDT
智能体基础知识
Earned Mar 15, 2026 EDT
Google DeepMind：训练小语言模型
Earned Mar 14, 2026 EDT
AI 智能体简介
Earned Mar 12, 2026 EDT
Google DeepMind: 01 Build Your Own Small Language Model
Earned Mar 10, 2026 EDT
In this Google DeepMind course you will learn how to prepare text data for language models to process. You will investigate the tools and techniques used to prepare, structure, and represent text data for language models, with a focus on tokenization and embeddings. You will be encouraged to think critically about the decisions behind data preparation, and what biases within the data may be introduced into models. You will analyze trade-offs, learn how to work with vectors and matrices, how meaning is represented in language models. Finally, you will practice designing a dataset ethically using the Data Cards process, ensuring transparency, accountability, and respect for community values in AI development.
打造您的首个 Gemini Enterprise 应用，赢得技能徽章！将各种数据源连接到您的应用中，构建强大、统一的搜索和分析引擎。掌握进阶能力，如：深度研究型智能体、多智能体协同构思，以及用于进行聚焦式分析的 NotebookLM。
了解 AI 智能体如何为业务带来实际影响。您将把智能体类型关联到您的关键绩效指标 (KPI)，并探索能够解决实际瓶颈的用例。然后，您将了解 Gemini Enterprise 如何帮助您构建和编排合适的智能体，其范围涵盖从无代码到高代码的各种解决方案。
本课程将介绍 AI 智能体的基础知识，并探讨智能体在现实世界中的价值所在。它为想要从自主、目标导向行为的角度理解 AI 系统的开发者、架构师和技术决策者奠定了基础。
完成本课程，赢取 Google DeepMind：训练小语言模型高级技能徽章，展示您在以下方面的技能： 提出实际的语言模型研究问题；构建一个简单的词元化器；制作用于训练 Transformer 语言模型的数据集；运行小语言模型的训练循环。 注册免费订阅，即可免费访问此实验。每月可免费获得 35 个点数！
大致了解 AI 智能体的概念。了解 AI 智能体如何通过自主行动和推理来解决复杂问题。您将探索技术架构（模型、工具和编排），该架构使智能体能够学习、规划，并为您实现目标。
In this Google DeepMind course, you will learn the fundamentals of language models and gain a high-level understanding of the machine learning development pipeline. You will consider the strengths and limitations of traditional n-gram models and advanced transformer models. Practical coding labs will enable you to develop insights into how machine learning models work and how they can be used to generate text and identify patterns in language. Through real-world case studies, you will build an understanding around how research engineers operate. Drawing on these insights you will identify problems that you wish to tackle in your own community and consider how to leverage the power of machine learning responsibly to address these problems within a global and local context.