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16:00:00","2026-07-13 16:00:00","2026-06-16 16:00:00","ongoing","private","2026-04-16 08:00:00",{"slug":26,"title":27,"logo":7,"liveUrl":14,"prize":28,"sponsor":7,"description":29,"organizer":18,"startTime":30,"endTime":31,"registrationDeadline":32,"status":33,"dashboardVisibility":23,"createdAt":34},"autumn2025","2025秋季启元人工智能大赛","150,000","“2025秋季启元人工智能大赛”是由启元实验室主办的面向全国人工智能领域开发者、科研人员及高校学生的权威赛事。大赛旨在促进与培养具备工程实践能力和创新能力的优秀人工智能领域开发者、推广国产软硬件技术与生态、助力国家人工智能产业高质量发展。","2025-11-08 08:00:00","2026-01-13 08:00:00","2025-12-30 08:00:00","ended","2025-11-03 08:00:00",{"slug":36,"title":37,"logo":7,"liveUrl":14,"prize":38,"sponsor":7,"description":39,"organizer":18,"startTime":40,"endTime":41,"registrationDeadline":42,"status":33,"dashboardVisibility":23,"createdAt":43},"summer2025","启元人工智能大赛(系统方向)","80,000","启元人工智能大赛由启元实验室主办，其中系统赛道基于启元实验室九源软件栈的相关开源项目，通过拉动开源社区力量，一方面促进国产智能计算生态的发展，另一方面也为人工智能系统方向的学生及从业者提供一个实践平台。","2025-07-08 08:00:00","2025-08-25 08:00:00","2025-07-22 08:00:00","2025-06-22 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08:00:00","2026-10-19 08:00:00","public","2026-10-12 08:00:00",{"id":63,"slug":64,"title":65,"logo":66,"publicLiveEmbedUrl":7,"description":67,"introduction":57,"organizer":18,"coOrganizer":5,"sponsor":68,"startTime":69,"endTime":70,"status":33,"dashboardVisibility":23,"createdAt":71,"updatedAt":72},"019af3f3-e06a-7245-aa8d-37dc99a69c73","winter2025","2025年冬季InfiniTensor大模型与人工智能系统训练营","https:\u002F\u002Foss.infinitensor.com\u002Fprod\u002Fuploads\u002F2025-12\u002F019afc69-b7ca-7805-91fb-605429671c01.png?Expires=1792752826&OSSAccessKeyId=LTAI5t9R6uzvTa69CT92cFvV&Signature=tM28gi%2FKPywJvel7AqVcxFGnKTg%3D","由启元实验室发起的InfiniTensor大模型与人工智能系统训练营，面向开源社区、高校及产业界伙伴开放。活动以「普及AI全栈知识技术、培养工程实践与创新型人才、推广国产软硬件技术生态」为核心目标，助力国家人工智能产业高质量发展。 \n","新华三技术有限公司","2025-12-25 08:00:00","2026-03-27 08:00:00","2025-12-06 08:00:00","2026-10-22 20:24:30",{"id":74,"slug":36,"title":75,"logo":76,"publicLiveEmbedUrl":7,"description":77,"introduction":57,"organizer":18,"coOrganizer":5,"sponsor":78,"startTime":79,"endTime":80,"status":81,"dashboardVisibility":23,"createdAt":82,"updatedAt":83},"01980342-208c-7803-b3f3-19e1d31960ba","2025年夏季InfiniTensor大模型与人工智能系统训练营","https:\u002F\u002Foss.infinitensor.com\u002Fprod\u002Fuploads\u002F2025-07\u002F0198045e-28fe-78f0-95fe-23835ce58395.jpg?Expires=1792752826&OSSAccessKeyId=LTAI5t9R6uzvTa69CT92cFvV&Signature=RSwSsL0Rw0AFxM%2Bz65pGHzs2YLw%3D","InfiniTensor大模型与人工智能系统训练营由启元实验室及清华大学 AI 系统方向团队发起，面向开源社区、高校和产业界，普及人工智能各方面知识和技术，培养相关人才。训练营联合开源项目开发者、学术界大牛、工业界资深工程师参与授课与项目指导。课程采用「线上直播授课 + 在线答疑 + 项目实践辅导 」的方式进行，确保每位同学都能获得充分成长。训练营全程免费，通过视频、直播课、习题和项目实践，在人工智能飞速发展的背景下，为学员提供一窥深层技术的机会。","北京积算科技有限公司","2025-07-14 08:00:00","2025-09-30 08:00:00","upcoming","2025-07-13 08:00:00","2026-10-22 20:24:58",{"id":85,"slug":86,"title":87,"logo":88,"publicLiveEmbedUrl":7,"description":89,"introduction":57,"organizer":18,"coOrganizer":5,"sponsor":5,"startTime":90,"endTime":91,"status":81,"dashboardVisibility":23,"createdAt":92,"updatedAt":93},"01980cf9-a7d8-7796-ae86-0bbe05334add","winter2024","2024年冬季InfiniTensor大模型与人工智能系统训练营","https:\u002F\u002Ftesting-cdn.infinitensor.com\u002Fuploads\u002F2025-07\u002F01981269-ce23-7624-a287-9f07dd0d85f6.jpg","InfiniTensor大模型与人工智能系统训练营由清华大学发起，面向开源社区、高校和产业界，普及人工智能各方面知识和技术，培养相关人才。训练营全程免费，通过视频、直播课、习题和项目实践，在人工智能飞速发展的背景下，为学员提供一窥深层技术的机会。报名学员请阅读“导学”课程页的训练营介绍。","2025-01-05 08:00:00","2025-03-22 08:00:00","2025-07-15 08:00:00","2026-10-22 20:24:39",{"id":95,"slug":96,"title":97,"logo":98,"publicLiveEmbedUrl":7,"description":99,"introduction":57,"organizer":18,"coOrganizer":5,"sponsor":5,"startTime":100,"endTime":101,"status":81,"dashboardVisibility":23,"createdAt":92,"updatedAt":102},"01980c20-363a-7dc3-8f5a-58a68e08dcc4","summer2024","2024年夏季InfiniTensor大模型与人工智能系统训练营","https:\u002F\u002Ftesting-cdn.infinitensor.com\u002Fuploads\u002F2025-07\u002F01981269-fbfa-788a-afcd-17fa9be74ff9.jpg","InfiniTensor大模型与人工智能系统训练营由清华大学发起，面向开源社区和高校，普及人工智能各方面知识和技术，培养相关人才。训练营全程免费，通过课程、习题和项目实践，在人工智能飞速发展的背景下，为学员提供一窥深层技术的机会。","2024-07-20 08:00:00","2024-09-29 08:00:00","2026-10-22 20:24:49",[44],{"currentItem":105,"processedData":125},{"img":106,"keywords":107,"treeType":44,"description":108,"showStatus":44,"updateTime":109,"type":110,"title":111,"content":112,"nextArticle":113,"createTime":109,"sortOrder":117,"canonicalPath":118,"id":119,"contentType":120,"prevArticle":121},"https:\u002F\u002Foss.infinitensor.com\u002Fprod\u002Fimages\u002FKnowledge_Base\u002FKB_08_fm.jpg?Expires=1792752826&OSSAccessKeyId=LTAI5t9R6uzvTa69CT92cFvV&Signature=%2BnOF%2FbgwqieE7CkhOyZvAT3t53A%3D","InfiniTensor,冬季训练营,推理引擎,AI模型基础,推理原理","讲解 AI 模型导论与推理基础原理，建立推理引擎方向的概念体系与实践认知。","2026-10-22 20:38:11",2,"InfiniTensor 冬季训练营推理引擎方向第一课《AI 模型导论与推理基础原理》","![](https:\u002F\u002Foss.infinitensor.com\u002Fprod\u002Fimages\u002FKnowledge_Base\u002FKB_08_zw1.jpg?Expires=1792752826&OSSAccessKeyId=LTAI5t9R6uzvTa69CT92cFvV&Signature=%2BjVhVlMeJKdeHx3hx9CIc7Wyai0%3D)\r\n\r\n\r\n\r\n2026 年 1 月 12 日，2025 冬季 InfiniTensor 大模型与人工智能系统训练营推理引擎方向第一课《AI 模型导论与推理基础原理》正式开讲。\r\n\r\n本节课面向零基础学员，介绍人工智能模型的基本概念与推理基础原理，为后续推理系统与训练方向奠定理论基础。\r\n\r\n### 智能的本质与AI模型基础\r\n#### 1. 什么是智能？\r\n- 定义：针对不同情景给出合理输出反应\r\n- 生物实验案例：草履虫应激性实验\r\n- AI应用实例：图像识别、AlphaGo、聊天机器人\r\n\r\n![](https:\u002F\u002Foss.infinitensor.com\u002Fprod\u002Fimages\u002FKnowledge_Base\u002FKB_08_zw2.jpg?Expires=1792752826&OSSAccessKeyId=LTAI5t9R6uzvTa69CT92cFvV&Signature=vcKYq1ITu12IFEGIWHTT9ECuvww%3D)\r\n\r\n#### 2. 人工智能的基本概念\r\n- 图灵测试原理\r\n- AI模型的黑盒子比喻\r\n- 函数表示法：输入情景→输出反应\r\n\r\n![](https:\u002F\u002Foss.infinitensor.com\u002Fprod\u002Fimages\u002FKnowledge_Base\u002FKB_08_zw3.jpg?Expires=1792752826&OSSAccessKeyId=LTAI5t9R6uzvTa69CT92cFvV&Signature=rsOKxckpeu5GlA%2BWZQZEHFdMBhY%3D)\r\n\r\n\r\n### 人工智能发展历程\r\n#### 1. 符号主义（上世纪50年代）\r\n- 核心思想：智能是对符号的处理\r\n- 专家系统架构：知识库+推理机\r\n- 成功案例：MYCIN医疗诊断系统\r\n- 局限性分析\r\n\r\n![](https:\u002F\u002Foss.infinitensor.com\u002Fprod\u002Fimages\u002FKnowledge_Base\u002FKB_08_zw4.jpg?Expires=1792752826&OSSAccessKeyId=LTAI5t9R6uzvTa69CT92cFvV&Signature=F4OWqjb2r6nJd2B9PTPdpm7IrcE%3D)\r\n\r\n\r\n#### 2. 联结主义与感知器\r\n- 核心思想：智能源于人脑神经元结构\r\n- 感知器原理：模拟生物神经元\r\n- 线性计算与非线性计算结合\r\n\r\n![](https:\u002F\u002Foss.infinitensor.com\u002Fprod\u002Fimages\u002FKnowledge_Base\u002FKB_08_zw5.jpg?Expires=1792752826&OSSAccessKeyId=LTAI5t9R6uzvTa69CT92cFvV&Signature=lZInRdSfzdqK8nly4fZ5diSXwkI%3D)\r\n\r\n\r\n### 感知器与神经网络基础\r\n1. 感知器工作原理\r\n- 特征权重分配原理\r\n- 阈值判断机制\r\n- 实例分析：水果识别系统\r\n\r\n2. 感知器的优势与局限\r\n- 数值表示的灵活性\r\n- 参数可调性\r\n- 异或问题：线性不可分性\r\n\r\n### 多层感知器（MLP）\r\n#### 1. 解决线性不可分问题\r\n- 多层结构原理\r\n- 异或问题的多层解决方案\r\n- 区域划分策略\r\n#### 2. 万能近似定理\r\n- 三层神经网络近似任意复杂决策边界\r\n- 输入维度与输出种类的关系\r\n- 深度神经网络的形成原理\r\n\r\n![](https:\u002F\u002Foss.infinitensor.com\u002Fprod\u002Fimages\u002FKnowledge_Base\u002FKB_08_zw6.jpg?Expires=1792752826&OSSAccessKeyId=LTAI5t9R6uzvTa69CT92cFvV&Signature=5%2BuMPv0Q0WA%2B5Gmk2pAQENbppzo%3D)\r\n\r\n### 神经网络训练原理\r\n#### 1. 损失函数与参数优化\r\n- 损失函数的作用\r\n- 梯度下降法原理\r\n- 高维空间优化比喻\r\n\r\n#### 2. 激活函数\r\n- 符号函数的局限性\r\n- 常用激活函数类型\r\n- 连续性与可导性要求\r\n\r\n![](https:\u002F\u002Foss.infinitensor.com\u002Fprod\u002Fimages\u002FKnowledge_Base\u002FKB_08_zw7.jpg?Expires=1792752826&OSSAccessKeyId=LTAI5t9R6uzvTa69CT92cFvV&Signature=sMF1EIBMf4IhNcyBgD82GScAmgs%3D)\r\n\r\n\r\n### 特殊神经网络结构\r\n#### 1. 卷积神经网络（CNN）\r\n- 特征提取效率优化\r\n- 图像识别中的应用\r\n\r\n#### 2. 残差连接\r\n- 信息损失问题\r\n- 跨层连接原理\r\n\r\n#### 3. 层归一化\r\n- 解决内部协变量偏移\r\n- LayerNorm与RMSNorm\r\n- 加速模型收敛并提高训练稳定性\r\n\r\n![](https:\u002F\u002Foss.infinitensor.com\u002Fprod\u002Fimages\u002FKnowledge_Base\u002FKB_08_zw8.jpg?Expires=1792752826&OSSAccessKeyId=LTAI5t9R6uzvTa69CT92cFvV&Signature=bpDkvbJH6XTiiYA8nT8jZBv7O%2F0%3D)\r\n\r\n\r\n\r\n### 核心计算算子与硬件支持\r\n#### 1. 矩阵乘法\r\n- 神经网络中的核心计算\r\n- 并行计算特性\r\n#### 2. 神经网络与智能芯片\r\n- 专用硬件优化\r\n- 主流芯片介绍（A100、昇腾等）\r\n\r\n### AI模型的任务类型分类\r\n#### 1. 输入类型\r\n- 向量输入\r\n- 矩阵输入\r\n- 序列输入\r\n#### 2. 输出任务类型\r\n- 回归任务（regression）：气温预测、概率\r\n- 分类任务（classification）：图像识别\r\n- 生成任务（generation）：文本翻译、图片生成\r\n\r\n![](https:\u002F\u002Foss.infinitensor.com\u002Fprod\u002Fimages\u002FKnowledge_Base\u002FKB_08_zw9.jpg?Expires=1792752826&OSSAccessKeyId=LTAI5t9R6uzvTa69CT92cFvV&Signature=yAGAWR%2BBoH4b5oNowd%2Fphcc7uNU%3D)\r\n\r\n\r\n\r\n### 总结与展望\r\n这节课搭建了理解AI模型的理论基石，接下来我们将深入大模型内部，学习如何优化其推理性能。\r\n\r\n![](https:\u002F\u002Foss.infinitensor.com\u002Fprod\u002Fimages\u002FKnowledge_Base\u002FKB_01_zw6.jpg?Expires=1792752826&OSSAccessKeyId=LTAI5t9R6uzvTa69CT92cFvV&Signature=epXM94790JRyVZK5KPiIf95EZZQ%3D)\r\n\r\n\r\n### 💡 学习贴士\r\n更多细节及完整课程内容，请观看直播或查看课程回放。\r\n\r\n链接：https:\u002F\u002Fwww.infinitensor.com\u002Fcamp\u002Fwinter2025\r\n\r\n### 📚 训练营报名持续进行中\r\n扫码报名与加入群聊，或点击 **「阅读原文」** 直达官网报名\r\n\r\n\r\n![](https:\u002F\u002Foss.infinitensor.com\u002Fprod\u002Fimages\u002FKnowledge_Base\u002FKB_01_zw7.jpg?Expires=1792752826&OSSAccessKeyId=LTAI5t9R6uzvTa69CT92cFvV&Signature=T6W1BlHdoBa2bd0P%2B0OABUI5sTc%3D)",{"publishTime":7,"createTime":109,"canonicalPath":114,"id":115,"title":116},"\u002Fstudy-material\u002Fstudy-7","study-7","InfiniTensor 冬季训练营 CUDA 编程方向第一课《并行编程导论与 CUDA 入门》",101,"\u002Fstudy-material\u002Fstudy-8","study-8","markdown",{"publishTime":7,"createTime":109,"canonicalPath":122,"id":123,"title":124},"\u002Fstudy-material\u002Fstudy-9","study-9","InfiniTensor冬季训练营AI编译器方向第一课《AI编译器概述》",{"htmlContent":126,"headings":127},"\u003Cp>\u003Cimg src=\"https:\u002F\u002Foss.infinitensor.com\u002Fprod\u002Fimages\u002FKnowledge_Base\u002FKB_08_zw1.jpg?Expires=1792752826&OSSAccessKeyId=LTAI5t9R6uzvTa69CT92cFvV&Signature=%2BjVhVlMeJKdeHx3hx9CIc7Wyai0%3D\" alt=\"\">\u003C\u002Fp>\n\u003Cp>2026 年 1 月 12 日，2025 冬季 InfiniTensor 大模型与人工智能系统训练营推理引擎方向第一课《AI 模型导论与推理基础原理》正式开讲。\u003C\u002Fp>\n\u003Cp>本节课面向零基础学员，介绍人工智能模型的基本概念与推理基础原理，为后续推理系统与训练方向奠定理论基础。\u003C\u002Fp>\n\u003Ch3 id=\"智能的本质与ai模型基础\">\u003Ca class=\"anchor\" href=\"#智能的本质与ai模型基础\" aria-label=\"跳转到标题：智能的本质与AI模型基础\">\u003Cspan class=\"octicon octicon-link\">\u003C\u002Fspan>\u003C\u002Fa>智能的本质与AI模型基础\u003C\u002Fh3>\u003Ch4 id=\"1-什么是智能\">\u003Ca class=\"anchor\" href=\"#1-什么是智能\" aria-label=\"跳转到标题：1. 什么是智能？\">\u003Cspan class=\"octicon octicon-link\">\u003C\u002Fspan>\u003C\u002Fa>1. 什么是智能？\u003C\u002Fh4>\u003Cul>\n\u003Cli>定义：针对不同情景给出合理输出反应\u003C\u002Fli>\n\u003Cli>生物实验案例：草履虫应激性实验\u003C\u002Fli>\n\u003Cli>AI应用实例：图像识别、AlphaGo、聊天机器人\u003C\u002Fli>\n\u003C\u002Ful>\n\u003Cp>\u003Cimg src=\"https:\u002F\u002Foss.infinitensor.com\u002Fprod\u002Fimages\u002FKnowledge_Base\u002FKB_08_zw2.jpg?Expires=1792752826&OSSAccessKeyId=LTAI5t9R6uzvTa69CT92cFvV&Signature=vcKYq1ITu12IFEGIWHTT9ECuvww%3D\" alt=\"\">\u003C\u002Fp>\n\u003Ch4 id=\"2-人工智能的基本概念\">\u003Ca class=\"anchor\" href=\"#2-人工智能的基本概念\" aria-label=\"跳转到标题：2. 人工智能的基本概念\">\u003Cspan class=\"octicon octicon-link\">\u003C\u002Fspan>\u003C\u002Fa>2. 人工智能的基本概念\u003C\u002Fh4>\u003Cul>\n\u003Cli>图灵测试原理\u003C\u002Fli>\n\u003Cli>AI模型的黑盒子比喻\u003C\u002Fli>\n\u003Cli>函数表示法：输入情景→输出反应\u003C\u002Fli>\n\u003C\u002Ful>\n\u003Cp>\u003Cimg src=\"https:\u002F\u002Foss.infinitensor.com\u002Fprod\u002Fimages\u002FKnowledge_Base\u002FKB_08_zw3.jpg?Expires=1792752826&OSSAccessKeyId=LTAI5t9R6uzvTa69CT92cFvV&Signature=rsOKxckpeu5GlA%2BWZQZEHFdMBhY%3D\" alt=\"\">\u003C\u002Fp>\n\u003Ch3 id=\"人工智能发展历程\">\u003Ca class=\"anchor\" href=\"#人工智能发展历程\" aria-label=\"跳转到标题：人工智能发展历程\">\u003Cspan class=\"octicon octicon-link\">\u003C\u002Fspan>\u003C\u002Fa>人工智能发展历程\u003C\u002Fh3>\u003Ch4 id=\"1-符号主义-上世纪50年代\">\u003Ca class=\"anchor\" href=\"#1-符号主义-上世纪50年代\" aria-label=\"跳转到标题：1. 符号主义（上世纪50年代）\">\u003Cspan class=\"octicon octicon-link\">\u003C\u002Fspan>\u003C\u002Fa>1. 符号主义（上世纪50年代）\u003C\u002Fh4>\u003Cul>\n\u003Cli>核心思想：智能是对符号的处理\u003C\u002Fli>\n\u003Cli>专家系统架构：知识库+推理机\u003C\u002Fli>\n\u003Cli>成功案例：MYCIN医疗诊断系统\u003C\u002Fli>\n\u003Cli>局限性分析\u003C\u002Fli>\n\u003C\u002Ful>\n\u003Cp>\u003Cimg src=\"https:\u002F\u002Foss.infinitensor.com\u002Fprod\u002Fimages\u002FKnowledge_Base\u002FKB_08_zw4.jpg?Expires=1792752826&OSSAccessKeyId=LTAI5t9R6uzvTa69CT92cFvV&Signature=F4OWqjb2r6nJd2B9PTPdpm7IrcE%3D\" alt=\"\">\u003C\u002Fp>\n\u003Ch4 id=\"2-联结主义与感知器\">\u003Ca class=\"anchor\" href=\"#2-联结主义与感知器\" aria-label=\"跳转到标题：2. 联结主义与感知器\">\u003Cspan class=\"octicon octicon-link\">\u003C\u002Fspan>\u003C\u002Fa>2. 联结主义与感知器\u003C\u002Fh4>\u003Cul>\n\u003Cli>核心思想：智能源于人脑神经元结构\u003C\u002Fli>\n\u003Cli>感知器原理：模拟生物神经元\u003C\u002Fli>\n\u003Cli>线性计算与非线性计算结合\u003C\u002Fli>\n\u003C\u002Ful>\n\u003Cp>\u003Cimg src=\"https:\u002F\u002Foss.infinitensor.com\u002Fprod\u002Fimages\u002FKnowledge_Base\u002FKB_08_zw5.jpg?Expires=1792752826&OSSAccessKeyId=LTAI5t9R6uzvTa69CT92cFvV&Signature=lZInRdSfzdqK8nly4fZ5diSXwkI%3D\" alt=\"\">\u003C\u002Fp>\n\u003Ch3 id=\"感知器与神经网络基础\">\u003Ca class=\"anchor\" href=\"#感知器与神经网络基础\" aria-label=\"跳转到标题：感知器与神经网络基础\">\u003Cspan class=\"octicon octicon-link\">\u003C\u002Fspan>\u003C\u002Fa>感知器与神经网络基础\u003C\u002Fh3>\u003Col>\n\u003Cli>感知器工作原理\u003C\u002Fli>\n\u003C\u002Fol>\n\u003Cul>\n\u003Cli>特征权重分配原理\u003C\u002Fli>\n\u003Cli>阈值判断机制\u003C\u002Fli>\n\u003Cli>实例分析：水果识别系统\u003C\u002Fli>\n\u003C\u002Ful>\n\u003Col start=\"2\">\n\u003Cli>感知器的优势与局限\u003C\u002Fli>\n\u003C\u002Fol>\n\u003Cul>\n\u003Cli>数值表示的灵活性\u003C\u002Fli>\n\u003Cli>参数可调性\u003C\u002Fli>\n\u003Cli>异或问题：线性不可分性\u003C\u002Fli>\n\u003C\u002Ful>\n\u003Ch3 id=\"多层感知器-mlp\">\u003Ca class=\"anchor\" href=\"#多层感知器-mlp\" aria-label=\"跳转到标题：多层感知器（MLP）\">\u003Cspan class=\"octicon octicon-link\">\u003C\u002Fspan>\u003C\u002Fa>多层感知器（MLP）\u003C\u002Fh3>\u003Ch4 id=\"1-解决线性不可分问题\">\u003Ca class=\"anchor\" href=\"#1-解决线性不可分问题\" aria-label=\"跳转到标题：1. 解决线性不可分问题\">\u003Cspan class=\"octicon octicon-link\">\u003C\u002Fspan>\u003C\u002Fa>1. 解决线性不可分问题\u003C\u002Fh4>\u003Cul>\n\u003Cli>多层结构原理\u003C\u002Fli>\n\u003Cli>异或问题的多层解决方案\u003C\u002Fli>\n\u003Cli>区域划分策略\u003C\u002Fli>\n\u003C\u002Ful>\n\u003Ch4 id=\"2-万能近似定理\">\u003Ca class=\"anchor\" href=\"#2-万能近似定理\" aria-label=\"跳转到标题：2. 万能近似定理\">\u003Cspan class=\"octicon octicon-link\">\u003C\u002Fspan>\u003C\u002Fa>2. 万能近似定理\u003C\u002Fh4>\u003Cul>\n\u003Cli>三层神经网络近似任意复杂决策边界\u003C\u002Fli>\n\u003Cli>输入维度与输出种类的关系\u003C\u002Fli>\n\u003Cli>深度神经网络的形成原理\u003C\u002Fli>\n\u003C\u002Ful>\n\u003Cp>\u003Cimg src=\"https:\u002F\u002Foss.infinitensor.com\u002Fprod\u002Fimages\u002FKnowledge_Base\u002FKB_08_zw6.jpg?Expires=1792752826&OSSAccessKeyId=LTAI5t9R6uzvTa69CT92cFvV&Signature=5%2BuMPv0Q0WA%2B5Gmk2pAQENbppzo%3D\" alt=\"\">\u003C\u002Fp>\n\u003Ch3 id=\"神经网络训练原理\">\u003Ca class=\"anchor\" href=\"#神经网络训练原理\" aria-label=\"跳转到标题：神经网络训练原理\">\u003Cspan class=\"octicon octicon-link\">\u003C\u002Fspan>\u003C\u002Fa>神经网络训练原理\u003C\u002Fh3>\u003Ch4 id=\"1-损失函数与参数优化\">\u003Ca class=\"anchor\" href=\"#1-损失函数与参数优化\" aria-label=\"跳转到标题：1. 损失函数与参数优化\">\u003Cspan class=\"octicon octicon-link\">\u003C\u002Fspan>\u003C\u002Fa>1. 损失函数与参数优化\u003C\u002Fh4>\u003Cul>\n\u003Cli>损失函数的作用\u003C\u002Fli>\n\u003Cli>梯度下降法原理\u003C\u002Fli>\n\u003Cli>高维空间优化比喻\u003C\u002Fli>\n\u003C\u002Ful>\n\u003Ch4 id=\"2-激活函数\">\u003Ca class=\"anchor\" href=\"#2-激活函数\" aria-label=\"跳转到标题：2. 激活函数\">\u003Cspan class=\"octicon octicon-link\">\u003C\u002Fspan>\u003C\u002Fa>2. 激活函数\u003C\u002Fh4>\u003Cul>\n\u003Cli>符号函数的局限性\u003C\u002Fli>\n\u003Cli>常用激活函数类型\u003C\u002Fli>\n\u003Cli>连续性与可导性要求\u003C\u002Fli>\n\u003C\u002Ful>\n\u003Cp>\u003Cimg src=\"https:\u002F\u002Foss.infinitensor.com\u002Fprod\u002Fimages\u002FKnowledge_Base\u002FKB_08_zw7.jpg?Expires=1792752826&OSSAccessKeyId=LTAI5t9R6uzvTa69CT92cFvV&Signature=sMF1EIBMf4IhNcyBgD82GScAmgs%3D\" alt=\"\">\u003C\u002Fp>\n\u003Ch3 id=\"特殊神经网络结构\">\u003Ca class=\"anchor\" href=\"#特殊神经网络结构\" aria-label=\"跳转到标题：特殊神经网络结构\">\u003Cspan class=\"octicon octicon-link\">\u003C\u002Fspan>\u003C\u002Fa>特殊神经网络结构\u003C\u002Fh3>\u003Ch4 id=\"1-卷积神经网络-cnn\">\u003Ca class=\"anchor\" href=\"#1-卷积神经网络-cnn\" aria-label=\"跳转到标题：1. 卷积神经网络（CNN）\">\u003Cspan class=\"octicon octicon-link\">\u003C\u002Fspan>\u003C\u002Fa>1. 卷积神经网络（CNN）\u003C\u002Fh4>\u003Cul>\n\u003Cli>特征提取效率优化\u003C\u002Fli>\n\u003Cli>图像识别中的应用\u003C\u002Fli>\n\u003C\u002Ful>\n\u003Ch4 id=\"2-残差连接\">\u003Ca class=\"anchor\" href=\"#2-残差连接\" aria-label=\"跳转到标题：2. 残差连接\">\u003Cspan class=\"octicon octicon-link\">\u003C\u002Fspan>\u003C\u002Fa>2. 残差连接\u003C\u002Fh4>\u003Cul>\n\u003Cli>信息损失问题\u003C\u002Fli>\n\u003Cli>跨层连接原理\u003C\u002Fli>\n\u003C\u002Ful>\n\u003Ch4 id=\"3-层归一化\">\u003Ca class=\"anchor\" href=\"#3-层归一化\" aria-label=\"跳转到标题：3. 层归一化\">\u003Cspan class=\"octicon octicon-link\">\u003C\u002Fspan>\u003C\u002Fa>3. 层归一化\u003C\u002Fh4>\u003Cul>\n\u003Cli>解决内部协变量偏移\u003C\u002Fli>\n\u003Cli>LayerNorm与RMSNorm\u003C\u002Fli>\n\u003Cli>加速模型收敛并提高训练稳定性\u003C\u002Fli>\n\u003C\u002Ful>\n\u003Cp>\u003Cimg src=\"https:\u002F\u002Foss.infinitensor.com\u002Fprod\u002Fimages\u002FKnowledge_Base\u002FKB_08_zw8.jpg?Expires=1792752826&OSSAccessKeyId=LTAI5t9R6uzvTa69CT92cFvV&Signature=bpDkvbJH6XTiiYA8nT8jZBv7O%2F0%3D\" alt=\"\">\u003C\u002Fp>\n\u003Ch3 id=\"核心计算算子与硬件支持\">\u003Ca class=\"anchor\" href=\"#核心计算算子与硬件支持\" aria-label=\"跳转到标题：核心计算算子与硬件支持\">\u003Cspan class=\"octicon octicon-link\">\u003C\u002Fspan>\u003C\u002Fa>核心计算算子与硬件支持\u003C\u002Fh3>\u003Ch4 id=\"1-矩阵乘法\">\u003Ca class=\"anchor\" href=\"#1-矩阵乘法\" aria-label=\"跳转到标题：1. 矩阵乘法\">\u003Cspan class=\"octicon octicon-link\">\u003C\u002Fspan>\u003C\u002Fa>1. 矩阵乘法\u003C\u002Fh4>\u003Cul>\n\u003Cli>神经网络中的核心计算\u003C\u002Fli>\n\u003Cli>并行计算特性\u003C\u002Fli>\n\u003C\u002Ful>\n\u003Ch4 id=\"2-神经网络与智能芯片\">\u003Ca class=\"anchor\" href=\"#2-神经网络与智能芯片\" aria-label=\"跳转到标题：2. 神经网络与智能芯片\">\u003Cspan class=\"octicon octicon-link\">\u003C\u002Fspan>\u003C\u002Fa>2. 神经网络与智能芯片\u003C\u002Fh4>\u003Cul>\n\u003Cli>专用硬件优化\u003C\u002Fli>\n\u003Cli>主流芯片介绍（A100、昇腾等）\u003C\u002Fli>\n\u003C\u002Ful>\n\u003Ch3 id=\"ai模型的任务类型分类\">\u003Ca class=\"anchor\" href=\"#ai模型的任务类型分类\" aria-label=\"跳转到标题：AI模型的任务类型分类\">\u003Cspan class=\"octicon octicon-link\">\u003C\u002Fspan>\u003C\u002Fa>AI模型的任务类型分类\u003C\u002Fh3>\u003Ch4 id=\"1-输入类型\">\u003Ca class=\"anchor\" href=\"#1-输入类型\" aria-label=\"跳转到标题：1. 输入类型\">\u003Cspan class=\"octicon octicon-link\">\u003C\u002Fspan>\u003C\u002Fa>1. 输入类型\u003C\u002Fh4>\u003Cul>\n\u003Cli>向量输入\u003C\u002Fli>\n\u003Cli>矩阵输入\u003C\u002Fli>\n\u003Cli>序列输入\u003C\u002Fli>\n\u003C\u002Ful>\n\u003Ch4 id=\"2-输出任务类型\">\u003Ca class=\"anchor\" href=\"#2-输出任务类型\" aria-label=\"跳转到标题：2. 输出任务类型\">\u003Cspan class=\"octicon octicon-link\">\u003C\u002Fspan>\u003C\u002Fa>2. 输出任务类型\u003C\u002Fh4>\u003Cul>\n\u003Cli>回归任务（regression）：气温预测、概率\u003C\u002Fli>\n\u003Cli>分类任务（classification）：图像识别\u003C\u002Fli>\n\u003Cli>生成任务（generation）：文本翻译、图片生成\u003C\u002Fli>\n\u003C\u002Ful>\n\u003Cp>\u003Cimg src=\"https:\u002F\u002Foss.infinitensor.com\u002Fprod\u002Fimages\u002FKnowledge_Base\u002FKB_08_zw9.jpg?Expires=1792752826&OSSAccessKeyId=LTAI5t9R6uzvTa69CT92cFvV&Signature=yAGAWR%2BBoH4b5oNowd%2Fphcc7uNU%3D\" alt=\"\">\u003C\u002Fp>\n\u003Ch3 id=\"总结与展望\">\u003Ca class=\"anchor\" href=\"#总结与展望\" aria-label=\"跳转到标题：总结与展望\">\u003Cspan class=\"octicon octicon-link\">\u003C\u002Fspan>\u003C\u002Fa>总结与展望\u003C\u002Fh3>\u003Cp>这节课搭建了理解AI模型的理论基石，接下来我们将深入大模型内部，学习如何优化其推理性能。\u003C\u002Fp>\n\u003Cp>\u003Cimg src=\"https:\u002F\u002Foss.infinitensor.com\u002Fprod\u002Fimages\u002FKnowledge_Base\u002FKB_01_zw6.jpg?Expires=1792752826&OSSAccessKeyId=LTAI5t9R6uzvTa69CT92cFvV&Signature=epXM94790JRyVZK5KPiIf95EZZQ%3D\" alt=\"\">\u003C\u002Fp>\n\u003Ch3 id=\"学习贴士\">\u003Ca class=\"anchor\" href=\"#学习贴士\" aria-label=\"跳转到标题：💡 学习贴士\">\u003Cspan class=\"octicon octicon-link\">\u003C\u002Fspan>\u003C\u002Fa>💡 学习贴士\u003C\u002Fh3>\u003Cp>更多细节及完整课程内容，请观看直播或查看课程回放。\u003C\u002Fp>\n\u003Cp>链接：\u003Ca href=\"https:\u002F\u002Fwww.infinitensor.com\u002Fcamp\u002Fwinter2025\">https:\u002F\u002Fwww.infinitensor.com\u002Fcamp\u002Fwinter2025\u003C\u002Fa>\u003C\u002Fp>\n\u003Ch3 id=\"训练营报名持续进行中\">\u003Ca class=\"anchor\" href=\"#训练营报名持续进行中\" aria-label=\"跳转到标题：📚 训练营报名持续进行中\">\u003Cspan class=\"octicon octicon-link\">\u003C\u002Fspan>\u003C\u002Fa>📚 训练营报名持续进行中\u003C\u002Fh3>\u003Cp>扫码报名与加入群聊，或点击 \u003Cstrong>「阅读原文」\u003C\u002Fstrong> 直达官网报名\u003C\u002Fp>\n\u003Cp>\u003Cimg src=\"https:\u002F\u002Foss.infinitensor.com\u002Fprod\u002Fimages\u002FKnowledge_Base\u002FKB_01_zw7.jpg?Expires=1792752826&OSSAccessKeyId=LTAI5t9R6uzvTa69CT92cFvV&Signature=T6W1BlHdoBa2bd0P%2B0OABUI5sTc%3D\" alt=\"\">\u003C\u002Fp>\n",[128,131,135,138,140,143,146,148,151,154,157,159,162,165,167,170,173,176,178,181,184,187,190,193,195,198],{"id":129,"text":130,"level":9},"智能的本质与ai模型基础","智能的本质与AI模型基础",{"id":132,"text":133,"level":134},"1-什么是智能","1. 什么是智能？",4,{"id":136,"text":137,"level":134},"2-人工智能的基本概念","2. 人工智能的基本概念",{"id":139,"text":139,"level":9},"人工智能发展历程",{"id":141,"text":142,"level":134},"1-符号主义-上世纪50年代","1. 符号主义（上世纪50年代）",{"id":144,"text":145,"level":134},"2-联结主义与感知器","2. 联结主义与感知器",{"id":147,"text":147,"level":9},"感知器与神经网络基础",{"id":149,"text":150,"level":9},"多层感知器-mlp","多层感知器（MLP）",{"id":152,"text":153,"level":134},"1-解决线性不可分问题","1. 解决线性不可分问题",{"id":155,"text":156,"level":134},"2-万能近似定理","2. 万能近似定理",{"id":158,"text":158,"level":9},"神经网络训练原理",{"id":160,"text":161,"level":134},"1-损失函数与参数优化","1. 损失函数与参数优化",{"id":163,"text":164,"level":134},"2-激活函数","2. 激活函数",{"id":166,"text":166,"level":9},"特殊神经网络结构",{"id":168,"text":169,"level":134},"1-卷积神经网络-cnn","1. 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