Hello World
Welcome to Hexo! This is your very first post. Check documentation for more info. If you get any problems when using Hexo, you can find the answer in troubleshooting or you can ask me on GitHub. Quick
What Is Greedy Algorithm
Description:A greedy algorithm is any algorithm that follows the problem-solving heuristic of making the locally optimal choice at each stage.[1] In many problems, a greedy strategy does not produce a
SWPUCTF2021新生赛Web刷题记录
[SWPUCTF 2021 新生赛]gift_F12Ctrl+U查看前端源码再Ctrl+F搜搜Flag即可 [SWPUCTF 2021 新生赛]jicao<?php highlight_file('index.php'); include("flag.php"); $id=$_POST['id']; $json=json_decod
攻防世界Crypto-babyFibo
分析附件内容： import os import libnum from secret import flag def fibo(n): assert n >= 0 if n < 2: return n return fibo(n-1) + fibo(n-2) s = fibo(1000) m = libnum.s2n(flag+os.ura
攻防世界Crypto-初识RSA
分析附件内容： from Crypto.Util.number import bytes_to_long,inverse,getPrime from flag import flag m = bytes_to_long(flag) p = getPrime(1024) q = getPrime(1024) n = p*q print(n) e = 65537 c = pow(m,e,n)
攻防世界Crypto-二元一次方程组
分析：import libnum from Crypto.Util import number from secret import flag size = 256 e = 65537 p = number.getPrime(size) q = number.getPrime(size) avg = (p+q)/2 n = p*q m = libnum.s2n(flag) c = pow(m
攻防世界Crypto-baigeiRSA
拿到附件后查看内容import libnum from Crypto.Util import number from secret import flag size = 128 e = 65537 p = number.getPrime(size) q = number.getPrime(size) n = p*q m = libnum.s2n(flag) c = pow(m, e, n)
深度学习：从头构建神经网络
Description本文旨在使用PyTorch构建并训练一个最简单的神经网络，无需添加任何花哨的层或依赖包。 该模型将足够简单，大家都能使用CPU或GPU来构建和训练。 这个模型虽然简单，但包含了当前诸如LLM和Stable Diffusions等大型模型所拥有的所有基本元素。 准备数据假设我们要训练一个具有四个权重并能输出一个数字结果的模型，如下所示： $ y = w_1 * x_1