<?xml version="1.0" encoding="utf-8" ?><rss version="2.0"><channel><title><![CDATA[for_none的博客]]></title><description><![CDATA[]]></description><link>https://blog.youkuaiyun.com/for_none</link><language>zh-cn</language><generator>https://blog.youkuaiyun.com/</generator><copyright><![CDATA[Copyright &copy; for_none]]></copyright><item><title><![CDATA[Neuronal Dynamics：第五章笔记]]></title><link>https://blog.youkuaiyun.com/for_none/article/details/129360615</link><guid>https://blog.youkuaiyun.com/for_none/article/details/129360615</guid><author>for_none</author><pubDate>Mon, 06 Mar 2023 13:45:06 +0800</pubDate><description><![CDATA[Neuronal Dynamics：第五章笔记]]></description><category></category></item><item><title><![CDATA[OPTIMAL CONVERSION OF CONVENTIONAL ARTIFICIAL NEURAL NETWORKS TO SPIKING NEURAL NETWORKS 阅读总结]]></title><link>https://blog.youkuaiyun.com/for_none/article/details/128793221</link><guid>https://blog.youkuaiyun.com/for_none/article/details/128793221</guid><author>for_none</author><pubDate>Sun, 05 Feb 2023 12:27:19 +0800</pubDate><description><![CDATA[OPTIMAL CONVERSION OF CONVENTIONAL ARTIFICIAL NEURAL NETWORKS TO SPIKING NEURAL NETWORKS 阅读总结]]></description><category></category></item><item><title><![CDATA[Supervised Learning Based on Temporal Coding in Spiking Neural Networks 阅读总结]]></title><link>https://blog.youkuaiyun.com/for_none/article/details/128835812</link><guid>https://blog.youkuaiyun.com/for_none/article/details/128835812</guid><author>for_none</author><pubDate>Sun, 05 Feb 2023 10:00:29 +0800</pubDate><description><![CDATA[Supervised Learning Based on Temporal Coding in Spiking Neural Networks 阅读总结]]></description><category></category></item><item><title><![CDATA[TCL: an ANN-to-SNN Conversion with Trainable Clipping Layers 阅读总结]]></title><link>https://blog.youkuaiyun.com/for_none/article/details/128835577</link><guid>https://blog.youkuaiyun.com/for_none/article/details/128835577</guid><author>for_none</author><pubDate>Fri, 03 Feb 2023 13:50:43 +0800</pubDate><description><![CDATA[TCL: an ANN-to-SNN Conversion with Trainable Clipping Layers 阅读总结]]></description><category></category></item><item><title><![CDATA[Incorporating Learnable Membrane Time Constant to Enhance Learning of Spiking Neural Networks 阅读总结]]></title><link>https://blog.youkuaiyun.com/for_none/article/details/128734525</link><guid>https://blog.youkuaiyun.com/for_none/article/details/128734525</guid><author>for_none</author><pubDate>Thu, 19 Jan 2023 13:54:42 +0800</pubDate><description><![CDATA[Incorporating Learnable Membrane Time Constant to Enhance Learning of Spiking Neural Networks 阅读总结
]]></description><category></category></item><item><title><![CDATA[Optimal ANN-SNN Conversion for High-accuracy and Ultra-low-latency Spiking Neural Networks 阅读总结]]></title><link>https://blog.youkuaiyun.com/for_none/article/details/128703352</link><guid>https://blog.youkuaiyun.com/for_none/article/details/128703352</guid><author>for_none</author><pubDate>Tue, 17 Jan 2023 14:41:08 +0800</pubDate><description><![CDATA[Optimal ANN-SNN Conversion for High-accuracy and Ultra-low-latency Spiking Neural Networks 阅读总结]]></description><category></category></item><item><title><![CDATA[Neuronal Dynamics：Two dimensions model 实验结果展示]]></title><link>https://blog.youkuaiyun.com/for_none/article/details/128673184</link><guid>https://blog.youkuaiyun.com/for_none/article/details/128673184</guid><author>for_none</author><pubDate>Fri, 13 Jan 2023 15:05:34 +0800</pubDate><description><![CDATA[two dimensions model实验结果展示]]></description><category></category></item><item><title><![CDATA[Neuronal Dynamics：第四章笔记]]></title><link>https://blog.youkuaiyun.com/for_none/article/details/128666729</link><guid>https://blog.youkuaiyun.com/for_none/article/details/128666729</guid><author>for_none</author><pubDate>Thu, 12 Jan 2023 21:32:29 +0800</pubDate><description><![CDATA[Neuronal Dynamics：第四章笔记]]></description><category></category></item><item><title><![CDATA[Neuronal Dynamics：第三章笔记]]></title><link>https://blog.youkuaiyun.com/for_none/article/details/128666697</link><guid>https://blog.youkuaiyun.com/for_none/article/details/128666697</guid><author>for_none</author><pubDate>Thu, 12 Jan 2023 21:29:07 +0800</pubDate><description><![CDATA[Neuronal Dynamics：第三章笔记]]></description><category></category></item><item><title><![CDATA[Neuronal Dynamics：第二章笔记]]></title><link>https://blog.youkuaiyun.com/for_none/article/details/128666670</link><guid>https://blog.youkuaiyun.com/for_none/article/details/128666670</guid><author>for_none</author><pubDate>Thu, 12 Jan 2023 21:26:22 +0800</pubDate><description><![CDATA[Neuronal Dynamics：第二章笔记]]></description><category></category></item><item><title><![CDATA[Neuronal Dynamics：第一章笔记]]></title><link>https://blog.youkuaiyun.com/for_none/article/details/128666616</link><guid>https://blog.youkuaiyun.com/for_none/article/details/128666616</guid><author>for_none</author><pubDate>Thu, 12 Jan 2023 21:23:53 +0800</pubDate><description><![CDATA[Neuronal Dynamics：第一章笔记]]></description><category></category></item><item><title><![CDATA[Neuronal Dynamics：Finite cable model 实验结果展示]]></title><link>https://blog.youkuaiyun.com/for_none/article/details/128656541</link><guid>https://blog.youkuaiyun.com/for_none/article/details/128656541</guid><author>for_none</author><pubDate>Thu, 12 Jan 2023 15:07:54 +0800</pubDate><description><![CDATA[Finite cable model]]></description><category></category></item><item><title><![CDATA[Neuronal Dynamics：Hodgkin-Huxley model 实验结果展示]]></title><link>https://blog.youkuaiyun.com/for_none/article/details/128620325</link><guid>https://blog.youkuaiyun.com/for_none/article/details/128620325</guid><author>for_none</author><pubDate>Mon, 09 Jan 2023 21:20:11 +0800</pubDate><description><![CDATA[Hodgkin-Huxley model实验]]></description><category></category></item><item><title><![CDATA[CSAPP大作业]]></title><link>https://blog.youkuaiyun.com/for_none/article/details/124876819</link><guid>https://blog.youkuaiyun.com/for_none/article/details/124876819</guid><author>for_none</author><pubDate>Fri, 20 May 2022 09:35:09 +0800</pubDate><description><![CDATA[计算机系统
大作业
题     目  程序人生-Hello’s P2P	
专       业     人工智能（未来技术）
学　　   号     7203610126
班　　   级     2036011
学       生     黄梓涵         　　　
指 导 教 师     刘宏伟
计算机科学与技术学院
2021年5月
摘  要
本文阐述了hello.c程序在linux系统下的生命周期，以计算机系统的视角，探讨了hello.c的预处理、编译、汇编、链接生成可执行文件的过程，并对可执行文件进]]></description><category></category></item><item><title><![CDATA[python数学建模模板]]></title><link>https://blog.youkuaiyun.com/for_none/article/details/122928436</link><guid>https://blog.youkuaiyun.com/for_none/article/details/122928436</guid><author>for_none</author><pubDate>Mon, 14 Feb 2022 17:15:43 +0800</pubDate><description><![CDATA[去年国赛之前写的，怕以后找不到了]]></description><category></category></item><item><title><![CDATA[反向传播算法误差迭代的理解]]></title><link>https://blog.youkuaiyun.com/for_none/article/details/119840531</link><guid>https://blog.youkuaiyun.com/for_none/article/details/119840531</guid><author>for_none</author><pubDate>Sat, 21 Aug 2021 16:18:38 +0800</pubDate><description><![CDATA[反向传播算法误差迭代的理解
参考自反向误差传播迭代公式

误差传播的关键在于理解
δ(l)=∂J∂z\frac{∂J}{∂z }∂z∂J​=∂J∂a\frac{∂J}{∂a }∂a∂J​∂a∂z\frac{∂a}{∂z }∂z∂a​=(θ(l))Tδ(l+1) .* g’(z(l))
其中a = g(z) = 1(1−e−z)\frac{1}{(1-e^-z^) }(1−e−z)1​
J 指是代价函数，a是该节点的总输出，z是该节点的总误差
详细见
https://www.bilibili.com/vide]]></description><category></category></item><item><title><![CDATA[网易云params和encSecKey的获取]]></title><link>https://blog.youkuaiyun.com/for_none/article/details/119446609</link><guid>https://blog.youkuaiyun.com/for_none/article/details/119446609</guid><author>for_none</author><pubDate>Fri, 06 Aug 2021 12:37:53 +0800</pubDate><description><![CDATA[网易云params和encSecKey的获取params和encSecKey有什么用软件准备1.python及其对应的编辑器，推荐pycharm2.发条js调试工具（我使用的是1.9版本）js代码分析定位生成位置分析传入参数代码初步调试CryptoJS，setMaxDigits相关函数代码的添加python 程序实现调用js
要这两个参数有什么用呢， 屁用没有 ，往下看有惊喜
params和encSecKey有什么用
以《平凡之路》为例

我们通过分析Network变化发现了这样一个数据接口https://]]></description><category></category></item><item><title><![CDATA[HIT暑假python作业四《智能停车场车牌识别计费系统》]]></title><link>https://blog.youkuaiyun.com/for_none/article/details/119029141</link><guid>https://blog.youkuaiyun.com/for_none/article/details/119029141</guid><author>for_none</author><pubDate>Fri, 23 Jul 2021 12:15:08 +0800</pubDate><description><![CDATA[HIT暑假python作业四《智能停车场车牌识别计费系统》运行效果运行前提：同目录下有file文件夹里有key.txt里有datafile文件夹里有停车场车辆表格式如下停车场信息表格式如下作业上交版代码
运行效果
这东西是真的难写，我搞了一晚上到两点，就睡了6个小时第二天还弄到早上十一点点半才弄完
程序运行效果


运行前提：同目录下有

file文件夹里有

key.txt里有
###记得替换成自己的百度API里申请的东西
还有记得申请车牌识别的免费使用额度
登入进入该网址领取：
https://cons]]></description><category></category></item><item><title><![CDATA[HIT暑假python作业三《超级画板》]]></title><link>https://blog.youkuaiyun.com/for_none/article/details/119028639</link><guid>https://blog.youkuaiyun.com/for_none/article/details/119028639</guid><author>for_none</author><pubDate>Fri, 23 Jul 2021 11:57:01 +0800</pubDate><description><![CDATA[HIT暑假python作业三《超级画板》作业上交版代码编译版
程序运行效果**（运行时确保同目录下有文件夹img,里面有pen,eraser,plus,minus四个png图象，如果没有可以使用下文的编译版就不需要这四个图象）**

作业上交版代码
# -*- coding: utf-8 -*-
import pygame
#pyinstaller -F -p F:\pythoncode\venv\lib\site-packages homework3.py
colors = [(255,0,255),(0]]></description><category></category></item><item><title><![CDATA[HIT暑假python作业一《学生信息管理系统》]]></title><link>https://blog.youkuaiyun.com/for_none/article/details/118857800</link><guid>https://blog.youkuaiyun.com/for_none/article/details/118857800</guid><author>for_none</author><pubDate>Sat, 17 Jul 2021 15:53:28 +0800</pubDate><description><![CDATA[HIT暑假python作业一《学生信息管理系统》
运行确保同目录下要有一个homework1.txt文件，
这样程序才有内容来源读取
初始化时homework1.txt中内容应为{}
本地运行效果

生成exe文件后运行效果

# -*- coding: utf-8 -*-
import json
def readin():
    global info
    with open('homework1.txt','r',encoding='utf-8-sig') as f:
        info =]]></description><category></category></item></channel></rss>