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第 88 天:独立掌握度检索——无 AI 应证日

在声明条件下无辅助检索核心 Python、Git 调试、数学、梯度与模型推理,并以真实项目证据为锚的透明量规评分。

Agent 与智能体工作流 480 分钟有效研习(8 小时)
01 120 分钟

检索闯关设计:封存任务清单与辅助条件声明

Agent 辅助工程检查

前置要求

第 85-87 天 Capstone 集成以及已完成的学习项目链(autograd、预训练、真实 LoRA、可评测 Agent、CI 交付)。

学习目标

为覆盖五个域的无辅助检索组装固定封存任务清单,并在任何作答前声明辅助条件,确保独立证据绝不会由辅助产出推定。

操作指引

1. 理解本日存在的理由:检索练习(测试效应)比重读更能巩固长期记忆;今天把整个课程转化为固定的无辅助检查。
2. 封存闯关恰好覆盖五个域,每个域可追溯到真实证据日:
- PY 域(核心 Python):值、控制流、函数、容器、异常、文件(第 4-12 天)。
- GIT 域(Git 与调试):分支、diff 判读、失败诊断、一次性仓库上的安全恢复(第 13-15、79-81 天)。
- MATH 域(数学):导数、链式法则、概率、交叉熵解读(第 16-30 天)。
- GRAD 域(梯度与 Autograd):计算图、共享节点累积、有限差分验证(第 31-33 天与 projects/autograd)。
- MODEL 域(模型推理):分词、因果掩码、参数台账、损失/困惑度判读、LoRA 计数、Agent 循环行为(第 34-87 天)。
3. 现在为每个域固定 3 个任务 ID——检查中途不得即兴更换。把每个任务映射到未达标时使用的恢复日。
4. 书面声明辅助条件:独立掌握检查——无 AI、无笔记、无浏览器,120 分钟块内不超过 105 分钟,记录开始时间。声明条件是诚实的自我报告,不是声称的监控。
5. Agent 只允许协助*整理本清单*;不得为任何封存任务出题、作答或评分。

自主演练与记录

生成 sealed_inventory.json:5 个域各 3 个固定 task_id(共 15 条),每行含 {domain, task_id, recovery_day, declared_condition: 'independent'}。断言恰好 15 行且 task_id 无重复。

独立掌握标准

在任何作答开始前,产出把每个任务映射到具名恢复路径的完整封存清单,并完成辅助条件声明。

权威源资源与权利证明

linking Journal publisher copyright (all rights reserved) - lawful external linking only

Test-Enhanced Learning: Taking Memory Tests Improves Long-Term Retention

作者 / 机构: Henry L. Roediger III, Jeffrey D. Karpicke | 原始出处链接 ↗

对应章节 / 时间戳: Psychological Science 17(3): 249-255, Experiments 1-2

验证版本与日期: Psychological Science, 2006 (Verified: 2026-10-30)

入选理由:

Foundational experimental evidence that retrieval practice (testing) outperforms repeated study for long-term retention; grounds the Day 88 AI-free retrieval checks in peer-reviewed learning science rather than folk advice.

来源前置条件:

Basic understanding of study sessions, retention intervals, and experimental design vocabulary.

安全回退方案(离线弹性):

The curriculum restates the experimental design and effect sizes in original prose; every sealed task runs without the paper.

对比候选资源与选型分析 (2)

Strong meta-analytic coverage of spacing effects but addresses scheduling rather than the retrieval effect itself; retained as support for the Day 90 spaced-review schedule.

Equally foundational follow-up demonstrating repeated retrieval vs repeated study; the 2006 paper is cited as the standard first controlled demonstration.

linking Journal publisher copyright (all rights reserved) - lawful external linking only

Improving Students' Learning With Effective Learning Techniques: Promising Directions From Cognitive and Educational Psychology

作者 / 机构: John Dunlosky, Katherine A. Rawson, Elizabeth J. Marsh, Mitchell J. Nathan, Daniel T. Willingham | 原始出处链接 ↗

对应章节 / 时间戳: Psychological Science in the Public Interest 14(1): 4-58; practice testing and distributed practice sections

验证版本与日期: Psychological Science in the Public Interest, 2013 (Verified: 2026-10-30)

入选理由:

Detailed comparative review rating practice testing and distributed practice as high-utility techniques while rating highlighting and rereading low-utility; anchors the retrieval, correction, and spaced-review design of Days 88-90.

来源前置条件:

None beyond general academic reading.

安全回退方案(离线弹性):

Curriculum restates the utility ratings and application conditions in original prose; every task remains runnable offline.

对比候选资源与选型分析 (2)
Pashler et al. Learning Styles: Concepts and Evidence ↗ compared_alternative [Publisher copyright (originally Wiley; now SAGE Publications)]

Important negative result on learning-styles matching but narrower scope; Dunlosky et al. rates a broader set of techniques directly usable for this curriculum.

Brown, Roediger & McDaniel: Make It Stick: The Science of Successful Learning ↗ compared_alternative [Publisher copyright (Harvard University Press)]

Strong trade-book synthesis of the same evidence base; the peer-reviewed review is preferred for citation precision and technique-by-technique utility ratings.

02 120 分钟

独立检查 A:核心 Python 与 Git 调试(无 AI)

独立掌握检查(无 AI 辅助)

前置要求

已完成第 88 天活动 1 的封存清单声明;辅助条件:独立(无 AI、无笔记)。

学习目标

在固定任务上无辅助地证明核心 Python 与 Git/调试能力:实现带测试的文本工具、诊断失败测试输出、在一次性仓库上规划安全 Git 恢复。

操作指引

任务 PY-1(45 分钟):实现 top_k_words(text, k)——返回输入中频率最高的 k 个词,忽略大小写、剔除标点、同频按字母序。编写函数及覆盖以下情形的 unittest:常规计数、同频排序、空文本、k 大于词表。
任务 GIT-1(35 分钟):在一次性克隆上误把工作直接提交到 main。写出把该提交移到新分支 fix/login 并把 main 恢复到远端状态的准确命令序列,以及找回昨天误删分支尖端的命令。可在临时仓库上演练;破坏性标志只允许在一次性环境内使用。
任务 DBG-1(25 分钟):给定失败输出——FAIL: test_parse (KeyError: 'total') 与 FAIL: test_window (expected 6 items, got 7)——写出两个最可能的根因,以及每个根因对应的第一条诊断命令。
全部答案记入工作表;每条断言必须可用命令验证(python -m unittest、git log、git reflog)。

自主演练与记录

交付工作表条目:PY-1 实现+测试输出;GIT-1 命令序列(附每条命令产生的工作区状态说明);DBG-1 两个诊断及诊断命令。

独立掌握标准

三个任务全部在无辅助条件下以可运行验证正确作答。

权威源资源与权利证明

linking PSF License Version 2

Python Standard Library: unittest

作者 / 机构: Python Software Foundation | 原始出处链接 ↗

对应章节 / 时间戳: Classes and functions: Test cases (TestCase assert methods)

验证版本与日期: Python 3.12 (Verified: 2026-10-30)

入选理由:

Normative reference for unittest.TestCase, assertEqual, assertRaises, the failure report format, and test_*.py discovery conventions.

来源前置条件:

Basic Python syntax from Study Days 01-09

安全回退方案(离线弹性):

unittest ships with the interpreter, so every example runs offline; help(unittest.TestCase) documents the assert methods.

对比候选资源与选型分析 (2)
pytest documentation ↗ compared_alternative [MIT]

Strong third-party runner deferred for now; unittest needs no environment change and is the curriculum baseline.

Powerful third-party runner used industry-wide; unittest is selected for these AI-free checks because it ships in the standard library and needs no external dependency.

linking CC BY-NC-SA 3.0 noncommercial share-alike

Pro Git, Second Edition

作者 / 机构: Scott Chacon and Ben Straub | 原始出处链接 ↗

对应章节 / 时间戳: Chapter 3 Git Branching and Section 7.7 Reset Demystified

验证版本与日期: Second edition (Apress, 2014) (Verified: 2026-10-30)

入选理由:

Freely licensed reference for branching, merging, conflict resolution, and safe recovery vocabulary used on Day 14.

来源前置条件:

Basic git status/log/diff vocabulary from earlier study days

安全回退方案(离线弹性):

git help <command> and the Git manual pages ship with Git and cover the same commands offline; every required workflow is stated locally in the lesson.

对比候选资源与选型分析 (2)
GitHub Skills interactive courses ↗ compared_rejected_for_day14 [MIT (varies by course)]

Interactive but requires a GitHub account and an external service; disposable local repositories keep the exercise self-contained.

Atlassian Git Tutorials ↗ compared_rejected_for_day14 [All Rights Reserved]

Vendor-owned prose with no permissive license; Pro Git covers the same ground under CC BY-NC-SA.

03 120 分钟

独立检查 B:数学、梯度与模型推理(无 AI)

独立掌握检查(无 AI 辅助)

前置要求

第 88 天活动 2;辅助条件:独立。

学习目标

无辅助证明定量推理:手算链式法则梯度、Transformer 参数台账、因果掩码推理、LoRA 适配器计数、对照已记录工件值解读损失/困惑度。

操作指引

任务 M-1(25 分钟):对 f(a,b)=a*b+a,在 a=2.0, b=3.0 处手算 df/da 与 df/db(df/da=b+1=4.0,df/db=a=2.0),然后写出在 float64 下验证用的有限差分公式与步长 h,并说明双侧差分优于单侧的原因。
任务 M-2(25 分钟):重算预训练 smoke 配置(vocab=65, block=16, n_layer=2, n_head=2, n_embd=16)的参数台账:token_embedding=65*16=1040;position_embedding=16*16=256;attention/层=4*(16*16)=1024;mlp/层=2*(16*64)=2048;合计=1040+256+2*(1024+2048)=7440。与 projects/pretrain/artifacts/run_artifact.json 对比。
任务 M-3(20 分钟):用三句话解释位置 t 为何不能读位置 t+1,以及上三角因果掩码如何强制这一点。
任务 M-4(25 分钟):d_model=1024、LoRA 秩 r=16、单个投影:适配器参数=A(16x1024)+B(1024x16)=32768,对比稠密 1024x1024=1,048,576(约 3.125%)。说明 alpha/r 缩放在何处进入。
任务 M-5(25 分钟):解读 smoke 工件中的 val_loss 4.0372:困惑度=exp(4.0372)≈56.7。解释为何 10 步 CPU smoke 运行该值合理,并举出两个会证伪训练健康度的观测(如损失上升、非有限梯度)。

自主演练与记录

五个题目全部写出公式与单位求解,然后把数值答案与已记录工件字段对照——如实记录一致或差异。

独立掌握标准

五个定量题目全部在展示过程下无辅助正确作答,并与已记录工件值核对。

权威源资源与权利证明

linking MIT

CS231n: Backpropagation and Intuitions

作者 / 机构: Andrej Karpathy / Stanford University | 原始出处链接 ↗

对应章节 / 时间戳: Patterns in backward flow

验证版本与日期: Course notes (Verified: 2026-10-30)

入选理由:

Modular local-gradient interpretation (gate operations: add, multiply, max) and gradient flow intuition.

来源前置条件:

Multivariable calculus and basic Python.

安全回退方案(离线弹性):

Local gate definitions and circuit flow diagrams in the lesson provide full coverage.

对比候选资源与选型分析 (2)

Raw circuit view; CS231n notes are more structured with explicit gate derivations.

Fast.ai Practical Deep Learning for Coders ↗ compared_alternative [GPL-3.0]

Top-down approach; CS231n bottom-up modular view matches our first-principles curriculum.

linking MIT

micrograd: A tiny scalar-valued autograd engine

作者 / 机构: Andrej Karpathy | 原始出处链接 ↗

对应章节 / 时间戳: micrograd/engine.py: class Value (_backward closures and backward() topological sort); README 'Example usage'

验证版本与日期: Git commit master (Verified: 2026-10-30)

入选理由:

Minimal, readable implementation of scalar autograd and MLP layers directly matched to our project.

来源前置条件:

Python object-oriented programming and operator overloading from Day 31.

安全回退方案(离线弹性):

The local projects/autograd engine is runnable offline and matches micrograd semantics.

对比候选资源与选型分析 (2)
PyTorch autograd C++ engine ↗ compared_alternative [Modified BSD]

Production scale, but too large and complex for tracing by hand in introductory lessons.

Autograd (HIPS) ↗ compared_alternative [MIT]

Vectorized autograd; micrograd's pure scalar model is pedagogical for hand backprop.

04 120 分钟

评估:透明量规评分与未达标台账

独立掌握检查(无 AI 辅助)

前置要求

第 88 天活动 1-3 完成。

学习目标

按固定 0/1/2 量规为每个封存任务评分、计算各域小计,并产出把每项未达导向具名恢复活动的未达标台账——明示诚实证据规则。

操作指引

1. 量规(评分前固定):0=缺失或错误;1=部分正确(方法对但有一个算术/逻辑错误或缺验证);2=完全正确且命令可验证。
2. 域及格线:每域 ≥5/6 且无 0 分任务。总及格:五域全部达标。
3. 每个 <2 分项写一行台账:{task_id, miss_type ∈ {concept_gap, slip, ambiguity, evidence_gap}, recovery_day, next_attempt_date}。
4. 发布 score_sheet.json:逐题得分、各域小计、各域及格与否、全部台账行。
5. 在表头写明诚实规则:独立掌握检查只证明无辅助能力;Agent 辅助结果永不可替代;本检查不构成官方认证,也不声称人人三个月即可掌握——未达标准保持可见未达。

自主演练与记录

输出填写完整的 score_sheet.json,为每个未满分项写台账行;表头逐字包含 honesty_rules。

独立掌握标准

产出完整、诚实的评分表与补救台账;未达项保持可见未达,每项缺失都带具名恢复路径。

权威源资源与权利证明

linking NIST public information (U.S. government site; NIST/SEMATECH co-production)

NIST/SEMATECH e-Handbook of Statistical Methods

作者 / 机构: NIST (U.S. National Institute of Standards and Technology) and SEMATECH | 原始出处链接 ↗

对应章节 / 时间戳: Sections 1.3.5-1.3.6: quantitative techniques, variability, and probability distributions

验证版本与日期: Current online edition (Verified: 2026-10-30)

入选理由:

Primary engineering reference for descriptive statistics, exploratory data analysis, and uncertainty quantification with worked formulas.

来源前置条件:

High-school arithmetic; no calculus required for the descriptive sections.

安全回退方案(离线弹性):

Every formula used in the lesson (mean, variance, standard error) is reproduced as original explanation; all exercises run fully offline.

对比候选资源与选型分析 (2)
OpenIntro Statistics (4th edition) ↗ compared_selected_for_day28 [CC BY-SA 3.0]

Undergraduate statistics text with a stronger narrative bridge from probability to inference; retained as the second linked source for inferential framing.

Seeing Theory (Brown University) ↗ compared_alternative [Free web resource (site copyright)]

Strong interactive visualizations of probability and sampling, but a browser-only interactive format is a weaker fit for an offline-capable 480-minute ledger.

linking Journal publisher copyright (all rights reserved) - lawful external linking only

Test-Enhanced Learning: Taking Memory Tests Improves Long-Term Retention

作者 / 机构: Henry L. Roediger III, Jeffrey D. Karpicke | 原始出处链接 ↗

对应章节 / 时间戳: Psychological Science 17(3): 249-255, Experiments 1-2

验证版本与日期: Psychological Science, 2006 (Verified: 2026-10-30)

入选理由:

Foundational experimental evidence that retrieval practice (testing) outperforms repeated study for long-term retention; grounds the Day 88 AI-free retrieval checks in peer-reviewed learning science rather than folk advice.

来源前置条件:

Basic understanding of study sessions, retention intervals, and experimental design vocabulary.

安全回退方案(离线弹性):

The curriculum restates the experimental design and effect sizes in original prose; every sealed task runs without the paper.

对比候选资源与选型分析 (2)

Strong meta-analytic coverage of spacing effects but addresses scheduling rather than the retrieval effect itself; retained as support for the Day 90 spaced-review schedule.

Equally foundational follow-up demonstrating repeated retrieval vs repeated study; the 2006 paper is cited as the standard first controlled demonstration.