October 2025
Beginner to intermediate
534 pages
6h 18m
Chinese
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索引
A
- 加速器，人工智能加速器-功耗
- 主动注入，间接提示注入
- 基于适配器的方法，PEFT 技术
- 适配器
- 微调、微调方法
- LoRA,LoRA-QuantizedLoRA
- 合并与连接, 连接
- PEFT技术,PEFT技术-PEFT技术
- 代理，代理-效率
- 人工智能加速器（见加速器）
- 人工智能应用构建（参见应用构建）
- 人工智能应用规划（参见应用规划）
- 人工智能工程（AIE）
- 定义,从基础模型到人工智能工程
- ML 工程与人工智能工程的比较,人工智能工程与 ML 工程-人工智能接口的 比较
- 人工智能工程的兴起,人工智能工程的兴起--从基础模型到人工智能工程
- 人工智能工程架构（参见工程架构）
- AI 工程堆栈（参见工程堆栈）
- 人工智能法官，人工智能作为法官
- (另见人工智能法官）
- 人工智能管道编排（参见管道编排）
- 人工智能系统评估（参见系统评估）
- AI-as -a-judge，人工智能作为法官--哪些模型可以充当法官？
- 局限性，人工智能作为法官的局限性--人工智能作为法官的 偏见
- 偏见，人工智能作为法官的偏见
- 标准模糊性，标准模糊性--标准模糊性
- 不一致性
- 增加成本和延迟，增加成本和延迟
- 模型，哪些模型 可以充当法官？
- 原因,为什么人工智能是法官？
- 基于参考,What Models Can Act as Judges?
- 使用,如何将人工智能用作法官-如何将人工智能用作法官
- 局限性，人工智能作为法官的局限性--人工智能作为法官的 偏见
- 人工智能驱动的数据合成（见人工智能驱动的数据合成）
- AMP（自动混合精度），训练量化
- ANN（近似近邻）,基于嵌入的检索
- Annoy（近似近邻哦耶）,基于嵌入的检索
- 异常检测、与参考数据的相似性测量
- 人类
- API（参见开源模型、模型 API 与）
- 应用构建,Introduction to Building AI Applications with FoundationModels-Summary
- 应用规划,人工智能应用规划-维护
- 工程堆栈,The AI Engineering Stack-AIEngineering Versus Full-Stack Engineering
- 人工智能工程与 ML 工程，人工智能工程与 ML 工程--人工智能接口
- 应用程序开发,应用程序开发-AI接口
- 全栈工程与 人工智能工程, 人工智能工程与全栈工程的比较
- 人工智能堆栈的三个层次,人工智能堆栈的三个层次-人工智能堆栈的三个层次
- 基础模型用例,基础模型用例-工作流自动化
- 编码,编码-编码
- 对话机器人,Conversational Bots
- 数据组织，数据组织
- 教育, 教育
- 图像和视频制作,图像和视频制作
- 信息聚合,信息聚合
- 工作流程自动化,工作流程自动化
- 写作,Writing-Writing
- 人工智能工程的兴起, 人工智能工程的兴起--从基础模型到人工智能工程
- 基础模型到人工智能工程,从基础模型到人工智能工程--从基础模型到人工智能工程
- 应用开发
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O’Reilly covers everything we've got, with content to help us build a world-class technology community, upgrade the capabilities and competencies of our teams, and improve overall team performance as well as their engagement.Julian F.
I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.Addison B.
I’ve been on the O’Reilly platform for more than eight years. I use a couple of learning platforms, but I'm on O'Reilly more than anybody else. When you're there, you start learning. I'm never disappointed.Amir M.
I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.