<?xml version="1.0" encoding="utf-8" ?><rss version="2.0"><channel><title><![CDATA[franklin569的博客]]></title><description><![CDATA[]]></description><link>https://blog.youkuaiyun.com/franklin569</link><language>zh-cn</language><generator>https://blog.youkuaiyun.com/</generator><copyright><![CDATA[Copyright &copy; franklin569]]></copyright><item><title><![CDATA[Anaconda Navigator运行没有反应]]></title><link>https://blog.youkuaiyun.com/franklin569/article/details/100283926</link><guid>https://blog.youkuaiyun.com/franklin569/article/details/100283926</guid><author>franklin569</author><pubDate>Mon, 02 Sep 2019 15:00:11 +0800</pubDate><description><![CDATA[Anaconda prompt中运行这两行代码

conda update conda

conda update --all
]]></description><category></category></item><item><title><![CDATA[python报"IndentationError: unexpected indent"错]]></title><link>https://blog.youkuaiyun.com/franklin569/article/details/100282910</link><guid>https://blog.youkuaiyun.com/franklin569/article/details/100282910</guid><author>franklin569</author><pubDate>Mon, 02 Sep 2019 14:57:48 +0800</pubDate><description><![CDATA[python文件里格式不对，可能是tab和空格没对齐的问题，检查下tab和空格
]]></description><category></category></item><item><title><![CDATA[pycharm常用快捷键]]></title><link>https://blog.youkuaiyun.com/franklin569/article/details/94596535</link><guid>https://blog.youkuaiyun.com/franklin569/article/details/94596535</guid><author>franklin569</author><pubDate>Thu, 04 Jul 2019 10:09:53 +0800</pubDate><description><![CDATA[pycharm常用快捷键
ctrl+shift+enter 代码补全
ctrl+/                  单行注释
ctrl+shift+/         块注释
CTRL + ALT + L	代码格式标准化
CTRL + ALT + I	自动缩进
CTRL + D	复制当前代码行/块
CTRL + Y	删除当前代码行/块
SHIFT + ENTER	开启新一行
欢迎使用Markdo...]]></description><category></category></item><item><title><![CDATA[深度学习中epoch，batch概念与训练]]></title><link>https://blog.youkuaiyun.com/franklin569/article/details/88555714</link><guid>https://blog.youkuaiyun.com/franklin569/article/details/88555714</guid><author>franklin569</author><pubDate>Thu, 14 Mar 2019 16:30:51 +0800</pubDate><description><![CDATA[#深度学习中epoch，batch概念与训练
epoch全部数据集训练完为一epoch
batch全数据集划分，批数据处理
##三种梯度下降优化
全梯度下降（公式内包含全部样本集）
随机梯度下降
小批量梯度下降(Mini-batch gradient descent)
###问题
学习率
非凸目标函数的鞍点
##改进
###动量
###adagrad
对每个参数自适应不同的学习速率，Penning...]]></description><category></category></item></channel></rss>