| Problems | AC |
|---|---|
| A. Alice and Books | ○ |
| B. New Bakery | ○ |
| C. Manhattan Permutations | ○ |
| D. Elections | ○ |
| E. Computing Machine | ⊕ |
| F. Large Graph | ⊕ |
| Problems | AC |
|---|---|
| A. Alice and Books | ○ |
| B. New Bakery | ○ |
| C. Manhattan Permutations | ○ |
| D. Elections | ○ |
| E. Computing Machine | ⊕ |
| F. Large Graph | ⊕ |
赛后一个题一个题做的
| Problems | AC |
|---|---|
| A. Creating Words | ○ |
| B. Maximum Multiple Sum | ○ |
| C. Good Prefixes | ○ |
| D. Manhattan Circle | ○ |
| E. Secret Box | ○ |
| F. Final Boss | ○ |
| G. D-Function | ○ |
| H1. Maximize the Largest Component (Easy Version) | ○ |
| H2. Maximize the Largest Component (Hard Version) | ○ |
| Problems | AC |
|---|---|
| A. Strange Splitting | ○ |
| B. Large Addition | ○ |
| C1. Magnitude (Easy Version) | ○ |
| C2. Magnitude (Hard Version) | ○ |
| D. ‘’a’’ String Problem | ○ |
| E. Shuffle | |
| F. Reconstruction |
SVD，全称为奇异值分解（Singular Value Decomposition），是一种在数值线性代数中广泛使用的矩阵分解技术。SVD 将一个矩阵分解成三个特定的矩阵的乘积，可以用于许多应用领域，包括信号处理、统计分析、图像处理和推荐系统等。
| Problems | AC |
|---|---|
| A. Guess the Maximum | ○ |
| B. XOR Sequences | ○ |
| C. Earning on Bets | ○ |
| D. Fixing a Binary String | ○ |
| E. Manhattan Triangle | ○ |
| F. Kostyanych’s Theorem |
Stanford CS231n: Deep Learning for Computer Vision
退役后以来，高考的摧残使得老博客几万年没更新过了。
大一开始以后，本着记录知识点的需要，开了一个新的博客，从 Hugo 重新换回了 Hexo Icarus。
因为太懒了所以一整个大一 Blog 都是跑在本地的，现在把 Blog 放到 GitHub Pages 上面了。
但是放上来的时候手残把 Icarus 重装了，得重新魔改一下 Icarus，想着干脆直接开一个贴子记录一下。
准备一下发展心理学的 Final Exam
PSY2013 Research Methods in Psychology
Statistics in Psychology
PSY2013 Research Methods in Psychology
Ch09 Non‐Experimental Design I: Survey Methods
Ch10 Non‐Experimental Design II: Observational and Archival Methods
Ch11 Quasi‐Experimental Designs and Applied Research
Ch12 Small N Designs