<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom" xmlns:content="http://purl.org/rss/1.0/modules/content/"><channel><title>2019 on Simplestory</title><link>https://simplestory.github.io/categories/2019/</link><description>Recent content in 2019 on Simplestory</description><generator>Hugo</generator><language>zh-cn</language><copyright>Simplestory</copyright><lastBuildDate>Thu, 03 Oct 2019 00:00:00 +0000</lastBuildDate><atom:link href="https://simplestory.github.io/categories/2019/index.xml" rel="self" type="application/rss+xml"/><item><title>SSD</title><link>https://simplestory.github.io/posts/20191003_34df20bab5e85dc75bfc94ef569cced9/</link><pubDate>Thu, 03 Oct 2019 00:00:00 +0000</pubDate><guid>https://simplestory.github.io/posts/20191003_34df20bab5e85dc75bfc94ef569cced9/</guid><description>Single Shot MultiBox Detector</description></item><item><title>Faster RCNN</title><link>https://simplestory.github.io/posts/20190922_5e8040095d43e97ccedd09771f9eab38/</link><pubDate>Sun, 22 Sep 2019 00:00:00 +0000</pubDate><guid>https://simplestory.github.io/posts/20190922_5e8040095d43e97ccedd09771f9eab38/</guid><description>目标检测识别算法</description></item><item><title>Fast RCNN</title><link>https://simplestory.github.io/posts/20190921_817fe11366b1149fdc44a5a4c92685c4/</link><pubDate>Sat, 21 Sep 2019 00:00:00 +0000</pubDate><guid>https://simplestory.github.io/posts/20190921_817fe11366b1149fdc44a5a4c92685c4/</guid><description>经典目标检测识别算法</description></item><item><title>RCNN</title><link>https://simplestory.github.io/posts/20190919_13243638926ee79bc7810c80a460408c/</link><pubDate>Thu, 19 Sep 2019 00:00:00 +0000</pubDate><guid>https://simplestory.github.io/posts/20190919_13243638926ee79bc7810c80a460408c/</guid><description>经典目标检测识别算法</description></item><item><title>ResNet</title><link>https://simplestory.github.io/posts/20190910_cefb8aa8290a0b4cfddf3f88bf5a2a3c/</link><pubDate>Tue, 10 Sep 2019 00:00:00 +0000</pubDate><guid>https://simplestory.github.io/posts/20190910_cefb8aa8290a0b4cfddf3f88bf5a2a3c/</guid><description>&lt;blockquote&gt;
&lt;p&gt;首先祝所有教师节日快乐，感谢你们赠人玫瑰手留余香的奉献精神。然后时隔将近一个月，我又滚回来更新博客了，之前一直在忙着入职这件事，希望能尽快融入公司开发业务中而”忙碌“了一个月。接下来继续经典图像识别网络模型的介绍。上文我们已经介绍了AlexNet和VGG网络，这两个网络都将当时的神经网络的深度发挥到了极致，后续的神经网络模型性能基本上好不了多少。这是由于随着网络的加深，在误差后向传播时，靠前的网络层权重基本得不到更新，一直维持在某一个值附近。在2015年，何凯明大神提出了一种快捷连接方式将误差传播到了前面的网络层，很大程度上提高了网络的性能。下面简单解析一下ResNet网络。&lt;/p&gt;
&lt;/blockquote&gt;</description></item><item><title>区分几种归一化算法</title><link>https://simplestory.github.io/posts/20190811_58bd041ca5bf7b009cdc8d07392dc061/</link><pubDate>Sun, 11 Aug 2019 00:00:00 +0000</pubDate><guid>https://simplestory.github.io/posts/20190811_58bd041ca5bf7b009cdc8d07392dc061/</guid><description>&lt;blockquote&gt;
&lt;p&gt;神经网络已经有几个归一化算法了，它们长得都很相似，基本上都有统一的形式。本文参考了知乎专栏SIGAI关于归一化算法的文章，具体链接在文章底部。&lt;/p&gt;
&lt;/blockquote&gt;</description></item><item><title>VGG</title><link>https://simplestory.github.io/posts/20190730_1036fd1af9bc59d1a811b4e2ab23d60c/</link><pubDate>Tue, 30 Jul 2019 00:00:00 +0000</pubDate><guid>https://simplestory.github.io/posts/20190730_1036fd1af9bc59d1a811b4e2ab23d60c/</guid><description>经典的卷积神经网络</description></item><item><title>AlexNet</title><link>https://simplestory.github.io/posts/20190722_cdebdc3cc4ecd47a04cff48d03a688d3/</link><pubDate>Mon, 22 Jul 2019 00:00:00 +0000</pubDate><guid>https://simplestory.github.io/posts/20190722_cdebdc3cc4ecd47a04cff48d03a688d3/</guid><description>经典的卷积神经网络</description></item><item><title>LightGBM模型</title><link>https://simplestory.github.io/posts/20190324_161af8745042398050ad585874c3eec3/</link><pubDate>Sun, 24 Mar 2019 00:00:00 +0000</pubDate><guid>https://simplestory.github.io/posts/20190324_161af8745042398050ad585874c3eec3/</guid><description>A Highly Efficient Gradient Boosting Decision Tree</description></item><item><title>Xgboost模型</title><link>https://simplestory.github.io/posts/20190317_8fc39e31f30337e40756914df1f8b607/</link><pubDate>Sun, 17 Mar 2019 00:00:00 +0000</pubDate><guid>https://simplestory.github.io/posts/20190317_8fc39e31f30337e40756914df1f8b607/</guid><description>A Scalabe Tree Boosting Systeam</description></item></channel></rss>