{
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 "metadata": {
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   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "name": "python"
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   "project_id": "c0b185e7-d7e3-48b6-a605-8f1acdfe243c",
   "notebook_id": "a9c291b4c33b49d2aa7737b6215853d6",
   "destination": "L2",
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  },
  "title": "CLS 05 · 观察与迁移：响应说明什么，新任务怎样检验"
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  {
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   },
   "source": "# CLS 05 · 观察与迁移：响应说明什么，新任务怎样检验",
   "id": "7b481966245c4c298950bb8d21a58113"
  },
  {
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   "source": [
    "[返回第二讲 · 真实响应与遮挡](https://codingai-lec02.pages.dev/#/features-are-learned-responses/0) · [第二讲导读](https://codingai-lec02.pages.dev/course/guide.html) · [问卷作业 ↗](https://tally.so/r/VLroVv)（外链可右键在新标签页打开）\n",
    "\n",
    "这本材料用于第二讲 B 段的**观察与干预**：固定已训练 CNN，先描述响应在哪里，再用匹配的遮挡对照检查指定分数怎样变化。阅读保存图无需重新训练。\n",
    "\n",
    "**先读第 1–2 节，再做一个检查**：到 [LAB 08 · 从真实响应到遮挡对照](https://codingai-lec02.pages.dev/course/lab08.html) 固定一个输入与目标类别，比较同尺寸遮挡。记录原图、遮挡位置、同一目标分数与差值；最后解释观察支持了什么。\n",
    "\n",
    "当前对象名称中的“迁移”保留。第 3 节通往第三讲的独立材料：第二讲不把迁移实验混进响应图的证据链。册末参考进一步区分理论感受野、梯度敏感性与有限遮挡，按讨论需要阅读。"
   ],
   "id": "4652e6b7afbb4c31b0411a75ab0d7bda"
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deepnote": {
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     "type": "text-cell-h2",
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    "html_anchor": "cell-3"
   },
   "source": "## 1. 同一个通道，面对不同数字在哪里响应？",
   "id": "52cb29e9b8474ac1a3535d06d0876083"
  },
  {
   "cell_type": "markdown",
   "metadata": {
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    "html_anchor": "cell-4"
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   "source": [
    "**课件与图解**：[真实响应里，哪些位置和通道不同？](https://codingai-lec02.pages.dev/#/features-are-learned-responses/0) · [同一通道的完整参考](https://codingai-lec02.pages.dev/course/reference/observing-learned-hierarchy.html)\n",
    "\n",
    "**先横着读。** 固定第一层 c0，依次看数字 7、2、1：较强响应分别出现在哪里？先写一条共同现象，再找一个不完全相同的地方。\n",
    "\n",
    "![三个固定MNIST输入及同层c0真实响应，标出零基峰值坐标，层内固定色标](data:image/webp;base64,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)\n",
    "\n",
    "图中金色小方框只标**该响应图的峰值地址**。L1 c0 与 L2 c0 编号相同，却是不同层的不同通道；不能把纵向两格当成同一个 filter 的成长过程，也不能根据三例就把它命名为“数字7检测器”。\n",
    "\n",
    "**把“在哪里”落实成坐标。** 坐标均为左上原点、零基 (行,列)，并列最大值取行优先的第一个：\n",
    "\n",
    "| 固定输入 | L1 c0 峰值；(行,列) | L2 c0 峰值；(行,列) |\n",
    "|---|---|---|\n",
    "| test 0：7 | 2.2682；(24,12) | 4.1919；(11,6) |\n",
    "| test 1：2 | 2.2885；(18,9) | 4.8896；(10,8) |\n",
    "| test 2：1 | 1.9922；(19,13) | 3.4817；(10,6) |\n",
    "\n",
    "L1 的位置 (r,c) 最多读取原图从 (r−1,c−1) 开始的 3×3 范围；L2 则从 (2r−3,2c−3) 开始，范围 8×8。超出图像部分来自补零。于是 test 0 两个峰值的理论支持区分别为 **[23,11,26,14]** 与 **[19,9,27,17]**，次序为 [top,left,bottom,right]，下边、右边不包含。支持范围说明结构允许依赖哪些位置，不意味着每个像素当前都实际贡献，更不是因果解释。\n",
    "\n",
    "**读图条件。** 同一既有未增强 CNN，官方 test 0/1/2 在查看响应前固定，标签依次为 7/2/1；输入 uint8/255，范围 [0,1]。L1 为 ReLU 后、pool1 前 8×28×28；L2 为 ReLU 后、pool2 前 16×14×14。每层跨三个输入的所有通道统一 Blues 色标：L1 [0,2.3630995750427246]，L2 [0,8.801921844482422]，nearest 显示。层内可比颜色，跨层不可直接比深浅。三例恰好预测正确也不构成代表性性能评价。\n",
    "\n",
    "**先预测与核对**：先写观察句，再手算 test 0 的两个支持区。LAB 08 可以换层、换通道，复用检查点重新前向核对；无需重新训练。接着问：如果改变响应较高的区域，指定类别分数一定会变吗？\n",
    "\n",
    "*本图直接重排已保存的 `responses-v8/full-values.npz`，无生成响应值；模型身份与来源见本册末尾及[响应清单](https://codingai-lec02.pages.dev/responses-v8/manifest.json)。*"
   ],
   "id": "7af1df60df394d12a9bb51f6260c5504"
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deepnote": {
     "block_id": "bf98d6fda85346c3abc72867983f7ac2",
     "type": "text-cell-h2",
     "source_block_id": null,
     "logical_key": null
    },
    "html_anchor": "cell-5"
   },
   "source": "## 2. 遮挡后分数变化，需要怎样的对照？",
   "id": "bf98d6fda85346c3abc72867983f7ac2"
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deepnote": {
     "block_id": "f63aadc9de7c4f51828904cd97de8b64",
     "type": "markdown",
     "source_block_id": null,
     "logical_key": null
    },
    "html_anchor": "cell-6"
   },
   "source": [
    "**按需参考**：[遮住自行车后分数变化，还要和什么比较？](https://codingai-lec02.pages.dev/course/reference/occlusion-needs-a-control.html)\n",
    "\n",
    "先把两个问题分开：响应图告诉我们哪里亮，遮挡对照检验替换某区域后指定分数怎样变。不能从“亮”直接跳到“这个区域决定分类”。\n",
    "\n",
    "![观察固定层通道的响应；干预固定模型与目标分数，匹配面积填充，只改遮挡位置并比较相对原图的差值](https://codingai-lec02.pages.dev/course/visuals/deepnote-final/foundations-observe-intervene.png)\n",
    "\n",
    "**读图后先预测**：如果两处遮挡都让目标分数下降，能否只凭下降把其中一处叫作目标概念？先写出还需要比较什么，再看下面的实验设计。\n",
    "\n",
    "遮挡对照页 换到自行车分类情境，展示尚未运行的实验设计。固定模型和目标类别，记录原图的同一类别分数；用相同面积、相同填充值的块，分别遮住自行车、墙面和其他背景，比较各自的分数差。三处结果都保持待测，情境图上的遮挡不是已经得到的模型解释。\n",
    "\n",
    "| 保持条件 | 改变条件 | 要记录 |\n",
    "|---|---|---|\n",
    "| 模型、预处理、目标类别 | 遮挡位置 | 原分数与各次遮挡后的同一分数 |\n",
    "| 遮挡面积、填充方式 | 自行车/墙面/其他背景 | 对照间分数差 |\n",
    "\n",
    "不要遮挡后改读“新的最高类别分数”，否则比较对象已经变化。一次下降描述的是该替换干预下指定输出的变化；区域大小、填充值与输入分布改变都可能影响结论。第一轮先固定条件，之后再逐项检查稳健性，不能从一张遮挡图推出网络完整的因果理解。\n",
    "\n",
    "**先预测与核对**：在纸上写一个最小对照方案，圈出目标类别、面积和填充三项；预测对象区与背景区可能出现的两种结果，不补造数值。随后在 [LAB 08 · 从真实响应到遮挡对照](https://codingai-lec02.pages.dev/course/lab08.html) 对内置数字样本执行同尺寸遮挡对照，实际记录一个固定目标分数的变化。这是数字模型上的可运行练习，不是自行车分类器的结果；不要把上一节的理论支持框直接改名为遮挡证据。完成干预检查后，回到第二讲的结构问题：局部测量怎样通过层叠和连接形成整体判断？跨任务使用这些表示的独立问题归第三讲。"
   ],
   "id": "f63aadc9de7c4f51828904cd97de8b64"
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deepnote": {
     "block_id": "d3b774fb4e3e4e86bf8842921901e0a3",
     "type": "text-cell-h2",
     "source_block_id": null,
     "logical_key": null
    },
    "html_anchor": "cell-7"
   },
   "source": "## 3. 跨讲入口：迁移部分已归第三讲",
   "id": "d3b774fb4e3e4e86bf8842921901e0a3"
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deepnote": {
     "block_id": "1cb5fb29f2db4410a178391e1691e2b4",
     "type": "markdown",
     "source_block_id": null,
     "logical_key": null
    },
    "html_anchor": "cell-8"
   },
   "source": [
    "已训练表示能否帮助新的任务，是第三讲的开场问题；有监督学到的表示也可以迁移，自监督只是另一条学习途径。\n",
    "\n",
    "- [第三讲 CLS 01 · 从可迁移权重到可比较的表示](https://codingai-lec03.pages.dev/course/c01-transfer.html)：沿冻结骨干、只训练读出和更多参数更新，读清“复用什么、改什么”。\n",
    "- [第三讲 LAB 09 · 用验证结果选择迁移范围](https://codingai-lec03.pages.dev/course/lab09-transfer.html)：用独立数字二分类实验选择更新范围，保留验证选择与最终测试的分工。\n",
    "- [第三讲导读](https://codingai-lec03.pages.dev/course/guide.html)：查看现有材料与后续空间、时间及多模态内容。\n",
    "\n",
    "这些材料中的模型、数据和任务按各自实验说明读取，不把本册三张 MNIST 响应图直接当作迁移性能证据。完成本册观察／干预后，也可以先返回第二讲结构与成本主线。"
   ],
   "id": "1cb5fb29f2db4410a178391e1691e2b4"
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deepnote": {
     "block_id": "55b77ee582a741d9860d566ebd92e301",
     "type": "text-cell-h2",
     "source_block_id": null,
     "logical_key": null
    },
    "html_anchor": "cell-9"
   },
   "source": "## 把本段主线连起来",
   "id": "55b77ee582a741d9860d566ebd92e301"
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deepnote": {
     "block_id": "210beaf2c4d14eda8e770f1570a12caf",
     "type": "markdown",
     "source_block_id": null,
     "logical_key": null
    },
    "html_anchor": "cell-10"
   },
   "source": "请把观察与干预分别写成一个完整句子：我固定了什么对象，改变了什么条件，观察了什么结果，这能支持什么、还不能说明什么。响应峰值不是类别专用性，理论支持区不是因果贡献，遮挡差值需要匹配的负对照。\n\n**本课响应的来源记录**：`teaching/labs-v2/results/cnn-plain-state.pt`，未增强 CNN，最小干净验证交叉熵选择 epoch 6；SHA-256 为 `ba8b738cc67d4ec3cd83d75a5dbe8a70ec548377335ba0abaaa7c23cd14eeba3`。两层响应页/同通道响应页 的补图仅做前向，没有重新训练；不声称逐像素复现缺失的旧 `small-cnn-state.pt` 位图。完整测试原图 9533/10000、右移 2px 8464/10000 与既有记录一致。输入文件校验、全部通道和色标见[响应清单](https://codingai-lec02.pages.dev/responses-v8/manifest.json)。\n\n\n完整前向与干预到 LAB08。迁移评价到第三讲，第二讲继续检查连接和运算成本。",
   "id": "210beaf2c4d14eda8e770f1570a12caf"
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deepnote": {
     "block_id": "de23942cbd984e0ba22322fba35fe1b8",
     "type": "text-cell-h2",
     "source_block_id": null,
     "logical_key": null
    },
    "html_anchor": "cell-11"
   },
   "source": "## 课堂核对：先预测，再运行",
   "id": "de23942cbd984e0ba22322fba35fe1b8"
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deepnote": {
     "block_id": "dbd23cf925b845cb9d7ad5d1fff3e0a5",
     "type": "markdown",
     "source_block_id": null,
     "logical_key": null
    },
    "html_anchor": "cell-12"
   },
   "source": "下面只读取内嵌的已保存 c0 摘录，核对理论支持区坐标与层内色标。它不重新推理、训练或生成性能数字。先独立算 test 0 的两个支持框，再运行核对；真实前向和遮挡到 LAB08。",
   "id": "dbd23cf925b845cb9d7ad5d1fff3e0a5"
  },
  {
   "cell_type": "code",
   "metadata": {
    "deepnote": {
     "block_id": "a7400afeaec3434881dc30ae4f3f27b5",
     "type": "code",
     "source_block_id": null,
     "logical_key": null
    },
    "html_anchor": "cell-13"
   },
   "source": "# 以下records是已保存的实测摘录；本代码核对坐标，不重新前向或训练。\nfrom math import isclose\nrecords = [{'test_index': 0, 'label': 7, 'layer': 'relu1', 'peak': 2.2682111263275146, 'row_col': [24, 12], 'rf': [23, 11, 26, 14]}, {'test_index': 0, 'label': 7, 'layer': 'relu2', 'peak': 4.19194221496582, 'row_col': [11, 6], 'rf': [19, 9, 27, 17]}, {'test_index': 1, 'label': 2, 'layer': 'relu1', 'peak': 2.288503408432007, 'row_col': [18, 9], 'rf': [17, 8, 20, 11]}, {'test_index': 1, 'label': 2, 'layer': 'relu2', 'peak': 4.889586448669434, 'row_col': [10, 8], 'rf': [17, 13, 25, 21]}, {'test_index': 2, 'label': 1, 'layer': 'relu1', 'peak': 1.9921979904174805, 'row_col': [19, 13], 'rf': [18, 12, 21, 15]}, {'test_index': 2, 'label': 1, 'layer': 'relu2', 'peak': 3.4817421436309814, 'row_col': [10, 6], 'rf': [17, 9, 25, 17]}]\ncolor_max = {'relu1': 2.3630995750427246, 'relu2': 8.801921844482422}\n\ndef support_box(layer, row, col):\n    if layer == 'relu1':\n        top, left, size = row-1, col-1, 3\n    elif layer == 'relu2':\n        top, left, size = 2*row-3, 2*col-3, 8\n    else:\n        raise ValueError(layer)\n    return [top, left, top+size, left+size]  # bottom/right不包含\n\nfor rec in records:\n    row, col = rec['row_col']\n    box = support_box(rec['layer'], row, col)\n    assert box == rec['rf']\n    assert 0 <= rec['peak'] <= color_max[rec['layer']]\n    print('test', rec['test_index'], rec['layer'], 'c0峰值位置=', (row, col), '理论支持=', box)\n\n# 边界位置：超出28x28部分来自padding；裁切后的实际像素范围可能更小。\nraw = support_box('relu2', 0, 0)\nclipped = [max(0, raw[0]), max(0, raw[1]), min(28, raw[2]), min(28, raw[3])]\nassert raw == [-3, -3, 5, 5] and clipped == [0, 0, 5, 5]\nprint('构造的边界地址:', raw, '可见输入像素:', clipped)\nprint('支持范围由结构计算；它不是遮挡结果，也不是因果归因。')\n",
   "id": "a7400afeaec3434881dc30ae4f3f27b5",
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deepnote": {
     "block_id": "2a47e8dc8b9a4569ba42534f8fc649ff",
     "type": "markdown",
     "source_block_id": null,
     "logical_key": null
    },
    "html_anchor": "cell-14"
   },
   "source": "记录三件事：一条从图中观察到的共同现象；一条不能由三例推出的结论；一个同面积、同填充的遮挡对照。请让 AI 实现你提出的一个改动，再核对模型、输入、shape 和输出身份，最后解释它是否支持预测。",
   "id": "2a47e8dc8b9a4569ba42534f8fc649ff"
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deepnote": {
     "block_id": "97324ec7dbac4d01b1690bcc10d68f44",
     "type": "text-cell-h2",
     "source_block_id": null,
     "logical_key": "f3c6f89148be47c7971812b41b954f7d"
    },
    "html_anchor": "cell-15"
   },
   "source": "## 参考：响应、理论依赖与有限遮挡是三种问题",
   "id": "97324ec7dbac4d01b1690bcc10d68f44"
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deepnote": {
     "block_id": "7f21cf2211714ccabec761678300cc8d",
     "type": "markdown",
     "source_block_id": "795f5acf260a41ba9de61fac6066f385",
     "logical_key": "7cbf2b8bbe824d55bb5dd3d9f0b824bc"
    },
    "html_anchor": "cell-16"
   },
   "source": "最强响应样本问的是某个单元偏好什么输入；反演问的是哪些输入仍能产生相近表示，帮助观察保留的信息；遮挡问的是替换某个输入区域后，指定输出怎样改变。这些方法的优化目标、干预条件和可支持结论不同，一幅直观图不能把它们合并成“网络在想什么”。\n\nZeiler 与 Fergus 的架构诊断利用首层特征与重建观察发现局部测量、采样可能存在问题，再调整核大小与 stride，并用训练及识别结果检验。因为不只改动一个因素，这不能被讲成只改变 stride 的单因素消融。\n\n设计遮挡实验时，先固定模型及版本、输入预处理、目标类别与目标分数，再固定遮挡面积、填充方式和位置选择。比较目标区域与匹配面积的背景区域，并检查填充自身是否产生强烈分布外信号。主讲自行车遮挡页提出的是实验问题，没有已测出的分数可供引用。\n\n若用已有 LAB 02 的 CNN，应该选择它确实接受的灰度数字输入；不能把未提供的自行车分类模型假装成已经可运行。本节任务是让实验对象与结论对齐。\n\n**先判断**：遮住一个区域后猫的概率下降，能否说该区域就是“猫概念所在的位置”？\n\n**核对提示**：不能。概率还依赖其他类别分数，遮挡可能引入异常纹理，网络也可能利用相关背景。至少比较 logits 与概率、匹配面积对照及多张样本，再界定观察范围。\n\n**来源与继续阅读**\n\n- [Zeiler & Fergus · Visualizing and Understanding Convolutional Networks (2014)](https://arxiv.org/abs/1311.2901)\n- [Mahendran & Vedaldi · Understanding Deep Image Representations by Inverting Them (2015)](https://arxiv.org/abs/1412.0035)\n- [Adebayo et al. · Sanity Checks for Saliency Maps (2018)](https://papers.neurips.cc/paper/8160-sanity-checks-for-saliency-maps.pdf)\n- [Zhang · Making CNNs Shift-Invariant Again (2019)](https://proceedings.mlr.press/v97/zhang19a.html)",
   "id": "7f21cf2211714ccabec761678300cc8d"
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deepnote": {
     "block_id": "b7e435ff2bde4b0e9199a7328d49a8f5",
     "type": "markdown",
     "source_block_id": "3e2c1c5fd2824e2fa547e5a5dd5ba56f",
     "logical_key": "9e5caec251204e4aae88d26bffce1ccf"
    },
    "html_anchor": "cell-17"
   },
   "source": "理论感受野由计算路径决定：哪些输入位置存在通往该输出的路径。有效影响则通过梯度、扰动或其他分析测量：在当前输入、参数与测量定义下，哪些位置影响更强。理论范围大，不保证网络有效使用了全部范围，也不保证理解了物体语义。\n\n最小检查可以固定一个输出标量，对输入求梯度，再展示绝对梯度的空间分布；这描述局部敏感性。遮挡是有限幅度干预，结果不必与梯度完全一致。改变输入、目标输出或训练参数，分布也可能变化。\n\n如果梯度为零，应先排查饱和或 ReLU 门、选错目标与是否开启梯度记录，不能直接宣布某区域永远无用。把结构范围、局部导数和干预变化并排记录，比只给一张热图更有解释力。\n\n**先判断**：一个位置有计算路径，但当前梯度是零，二者矛盾吗？\n\n**核对提示**：不矛盾。路径表示结构上的可能性；局部梯度还取决于当前权重、输入与激活状态。有限干预也可能跨越激活边界。\n\n**来源与继续阅读**\n\n- [Luo et al. · Understanding the Effective Receptive Field (2016)](https://arxiv.org/abs/1701.04128)\n- [Araujo et al. · Computing Receptive Fields (2019)](https://distill.pub/2019/computing-receptive-fields/)\n- [He et al. · Deep Residual Learning (2015)](https://arxiv.org/abs/1512.03385)",
   "id": "b7e435ff2bde4b0e9199a7328d49a8f5"
  },
  {
   "cell_type": "code",
   "metadata": {
    "deepnote": {
     "block_id": "f657fd527e244d5ea27e377e76b19e65",
     "type": "code",
     "source_block_id": "563ec7020e534aeda8beed60686eaf7c",
     "logical_key": "8ebaa4d61d3649869e7a9feb832fad21"
    },
    "html_anchor": "cell-18"
   },
   "source": "# 合成可核对示例：对照设计，不是自行车模型的真实测量。\n# 固定分类分数 s=3*object_signal + background_signal。\nbase={'object_signal':1.,'background_signal':1.}\ndef score(values): return 3*values['object_signal']+values['background_signal']\nobject_mask=dict(base,object_signal=0.)\nbackground_mask=dict(base,background_signal=0.)\nprint('基准 / 对象干预 / 背景干预：',score(base),score(object_mask),score(background_mask))\nassert [score(base),score(object_mask),score(background_mask)]==[4.,1.,3.]\n# 正式图像实验还需匹配面积、填充、模型与样本；本式没有模拟这些条件。\nplan={'model_version':'填写固定模型与权重版本',\n      'target':'填写类别及 logit/probability',\n      'preprocessing':'填写图像尺寸、归一化等',\n      'occlusion':'填写面积、位置、填充规则',\n      'control':'填写匹配面积的背景对照',\n      'records':'记录原图、干预图、分数与差值'}\nfor field,value in plan.items():print(field,':',value)\n",
   "id": "f657fd527e244d5ea27e377e76b19e65",
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deepnote": {
     "block_id": "6f31a9fafcac4ead992712154e981f78",
     "type": "text-cell-h2",
     "source_block_id": null,
     "logical_key": null
    },
    "html_anchor": "cell-19"
   },
   "source": "## 课程导航",
   "id": "6f31a9fafcac4ead992712154e981f78"
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deepnote": {
     "block_id": "5dc0fe423f064e0c9f85dd7c8cadc184",
     "type": "markdown",
     "source_block_id": null,
     "logical_key": null
    },
    "html_anchor": "cell-20"
   },
   "source": [
    "[第二讲导读与完整索引](https://codingai-lec02.pages.dev/course/guide.html) · [继续第二讲结构与成本](https://codingai-lec02.pages.dev/#/architecture-connections-compared/0)\n",
    "\n",
    "[在 Deepnote 返回 CLS 00 · 第二讲导读](https://deepnote.com/workspace/ise3309jingao-5ba66b0a-4283-4f5a-ae06-3f68d3201e99/project/c0b185e7-d7e3-48b6-a605-8f1acdfe243c/notebook/008c71041d2d43569af77c9f25509be8?utm_source=openai&utm_medium=mcp&utm_campaign=openaimcp&utm_content=008c71041d2d43569af77c9f25509be8&utm_term=get_notebook)。静态课件链接返回对应主题；需保留原 Slides 停点时，请在新标签页打开材料。"
   ],
   "id": "5dc0fe423f064e0c9f85dd7c8cadc184"
  }
 ]
}