<?xml version="1.0" encoding="utf-8" ?><rss version="2.0"><channel><title><![CDATA[AtYeXTolearn的博客]]></title><description><![CDATA[]]></description><link>https://blog.youkuaiyun.com/AtYeXTolearn</link><language>zh-cn</language><generator>https://blog.youkuaiyun.com/</generator><copyright><![CDATA[Copyright &copy; AtYeXTolearn]]></copyright><item><title><![CDATA[tensorflow的基础2]]></title><link>https://blog.youkuaiyun.com/AtYeXTolearn/article/details/156912825</link><guid>https://blog.youkuaiyun.com/AtYeXTolearn/article/details/156912825</guid><author>AtYeXTolearn</author><pubDate>Tue, 13 Jan 2026 21:05:44 +0800</pubDate><description><![CDATA[介绍了TensorFlow中协调器(Coordinator)的用法.]]></description><category></category></item><item><title><![CDATA[使用卷积神经网络对图片的分类]]></title><link>https://blog.youkuaiyun.com/AtYeXTolearn/article/details/156805187</link><guid>https://blog.youkuaiyun.com/AtYeXTolearn/article/details/156805187</guid><author>AtYeXTolearn</author><pubDate>Mon, 12 Jan 2026 16:45:38 +0800</pubDate><description><![CDATA[本文介绍了CIFAR-10数据集的使用方法及其在TensorFlow中的实现。CIFAR-10包含6万张32x32彩色图片，分为10类。]]></description><category></category></item><item><title><![CDATA[识别图中模糊的手写数字]]></title><link>https://blog.youkuaiyun.com/AtYeXTolearn/article/details/156771262</link><guid>https://blog.youkuaiyun.com/AtYeXTolearn/article/details/156771262</guid><author>AtYeXTolearn</author><pubDate>Fri, 09 Jan 2026 21:26:55 +0800</pubDate><description><![CDATA[本文介绍了MNIST手写数字数据集的特性及其在TensorFlow中的处理流程。]]></description><category></category></item><item><title><![CDATA[ Tensorflow的基础]]></title><link>https://blog.youkuaiyun.com/AtYeXTolearn/article/details/156768948</link><guid>https://blog.youkuaiyun.com/AtYeXTolearn/article/details/156768948</guid><author>AtYeXTolearn</author><pubDate>Fri, 09 Jan 2026 18:41:22 +0800</pubDate><description><![CDATA[tensorflow基础部分]]></description><category></category></item><item><title><![CDATA[Rhino模型可视化]]></title><link>https://blog.youkuaiyun.com/AtYeXTolearn/article/details/135251608</link><guid>https://blog.youkuaiyun.com/AtYeXTolearn/article/details/135251608</guid><author>AtYeXTolearn</author><pubDate>Wed, 27 Dec 2023 19:06:31 +0800</pubDate><description><![CDATA[Rhino模型开发到网页展示一种开发路径]]></description><category></category></item><item><title><![CDATA[React基础]]></title><link>https://blog.youkuaiyun.com/AtYeXTolearn/article/details/135247426</link><guid>https://blog.youkuaiyun.com/AtYeXTolearn/article/details/135247426</guid><author>AtYeXTolearn</author><pubDate>Wed, 27 Dec 2023 17:16:56 +0800</pubDate><description><![CDATA[React前端框架基础记录笔记]]></description><category></category></item><item><title><![CDATA[基于Lenet-5预测股票走势]]></title><link>https://blog.youkuaiyun.com/AtYeXTolearn/article/details/132354537</link><guid>https://blog.youkuaiyun.com/AtYeXTolearn/article/details/132354537</guid><author>AtYeXTolearn</author><pubDate>Fri, 18 Aug 2023 10:08:32 +0800</pubDate><description><![CDATA[Lenet-5是基于对图片的二维卷积，图片本质上是二维数据(黑白)和三维数据(彩色)，需要构建二维数据。其中以窗口滑动的方式，将open、high、low构成特征集，以窗口大小里出现label次数最多的数字作为标签集]]></description><category></category></item><item><title><![CDATA[基于LSTM的股票预测]]></title><link>https://blog.youkuaiyun.com/AtYeXTolearn/article/details/131838820</link><guid>https://blog.youkuaiyun.com/AtYeXTolearn/article/details/131838820</guid><author>AtYeXTolearn</author><pubDate>Thu, 20 Jul 2023 21:06:40 +0800</pubDate><description><![CDATA[RNN主要用来处理序列数据，在传统的神经网络模型中，是从输入层到隐含 层再到输出层，每层内的节点之间无连接，循环神经网络中一个当前神经元的输出与前面的输出也有关，网络会对前面的信息进行记忆并应用于当前神经元的计算中，隐藏层之间的节点是有连接的，并且隐藏层的输入不仅包含输入层的输出还包含上一时刻隐藏层的输出。理论上，RNN可以对任意长度的序列数据进行处理。一个RNN可认为是同一网络的多次重复执行，每一次执行的结果是下一次执行的输入。循环神经网络展开图如右图所示。其中𝑥𝑡是输入序列，𝑠𝑡是在𝑡。]]></description><category></category></item><item><title><![CDATA[vgg16动物识别]]></title><link>https://blog.youkuaiyun.com/AtYeXTolearn/article/details/131796792</link><guid>https://blog.youkuaiyun.com/AtYeXTolearn/article/details/131796792</guid><author>AtYeXTolearn</author><pubDate>Wed, 19 Jul 2023 08:27:59 +0800</pubDate><description><![CDATA[VGG是5个group的卷积、2层全连接层用于提取图像特征、1层全连接层用于分类特征。根据前5个卷积层组每个组中的不同配置，卷积层数从8到16递增，网络结构如下图所示。]]></description><category></category></item><item><title><![CDATA[CNN-LeNet网络图像分类]]></title><link>https://blog.youkuaiyun.com/AtYeXTolearn/article/details/131794885</link><guid>https://blog.youkuaiyun.com/AtYeXTolearn/article/details/131794885</guid><author>AtYeXTolearn</author><pubDate>Tue, 18 Jul 2023 21:14:30 +0800</pubDate><description><![CDATA[卷积神经网络相对于BP神经网络多了个感受野机制。感受野就是视觉感受区域的大小。在卷积神经网络中， 感受野是CNN中的某一层输出结果的一个元素对应输入层的一个映射。LeNet网络结构图：输入层。]]></description><category></category></item><item><title><![CDATA[基于神经网络(多层感知器)识别手写数字]]></title><link>https://blog.youkuaiyun.com/AtYeXTolearn/article/details/131793361</link><guid>https://blog.youkuaiyun.com/AtYeXTolearn/article/details/131793361</guid><author>AtYeXTolearn</author><pubDate>Tue, 18 Jul 2023 18:14:00 +0800</pubDate><description><![CDATA[数据集为经典mnist。]]></description><category></category></item><item><title><![CDATA[银行客户流失预测（BP神经）]]></title><link>https://blog.youkuaiyun.com/AtYeXTolearn/article/details/131788232</link><guid>https://blog.youkuaiyun.com/AtYeXTolearn/article/details/131788232</guid><author>AtYeXTolearn</author><pubDate>Tue, 18 Jul 2023 16:52:59 +0800</pubDate><description><![CDATA[前馈神经网络（feedforward neural network），简称前馈网络，是人工神经网络的一种。在此种神经网络中，各神经元从输入层开始，接收前一级输入，并输入到下一级，直至输出层。整个网络中无反馈，可用一个有向无环图表示。前馈神经网络是最早被提出的人工神经网络，也是最简单的人工神经网络类型。按照前馈神经网络的层数不同，可以将其划分为单层前馈神经网络和多层前馈神经网络。]]></description><category></category></item><item><title><![CDATA[Spring Cloud Nacos]]></title><link>https://blog.youkuaiyun.com/AtYeXTolearn/article/details/130649472</link><guid>https://blog.youkuaiyun.com/AtYeXTolearn/article/details/130649472</guid><author>AtYeXTolearn</author><pubDate>Fri, 12 May 2023 21:51:52 +0800</pubDate><description><![CDATA[包含注册中心和配置中心，注册中心不需要数据库。]]></description><category></category></item><item><title><![CDATA[Spring Cloud Gateway快速入门]]></title><link>https://blog.youkuaiyun.com/AtYeXTolearn/article/details/130648735</link><guid>https://blog.youkuaiyun.com/AtYeXTolearn/article/details/130648735</guid><author>AtYeXTolearn</author><pubDate>Fri, 12 May 2023 20:08:53 +0800</pubDate><description><![CDATA[不需要选择web,web里面的服务器是tomcat,Gateway里面的服务器是Netty。]]></description><category></category></item><item><title><![CDATA[Spring Cloud Sleuth快速入门]]></title><link>https://blog.youkuaiyun.com/AtYeXTolearn/article/details/130625956</link><guid>https://blog.youkuaiyun.com/AtYeXTolearn/article/details/130625956</guid><author>AtYeXTolearn</author><pubDate>Thu, 11 May 2023 17:13:23 +0800</pubDate><description><![CDATA[链路追踪的由来在微服务框架中，一个由客户端发起的请求在后端系统中会经过多个不同的服务节点调用来协同产生最后的请求结果，每一个请求都会开成一条复杂的分布式服务调用链路，链路中的任何一环出现高延时或错误都会引导起整个请求最后的失败。(不建议微服务中链路调用超过3次)]]></description><category></category></item><item><title><![CDATA[Spring Cloud Hystrix快速入门]]></title><link>https://blog.youkuaiyun.com/AtYeXTolearn/article/details/130186142</link><guid>https://blog.youkuaiyun.com/AtYeXTolearn/article/details/130186142</guid><author>AtYeXTolearn</author><pubDate>Thu, 11 May 2023 16:37:35 +0800</pubDate><description><![CDATA[Hystrix是Netflix公司开源的一个项目，它提供了熔断器功能，能够阻止分布式系统中出现联动故障。Hystrix是通过隔离服务的访问点阻止联动故障的，并提供了故障的解决方案，从而提高了整个分布式系统的弹性。微博弹性云扩容Docker K8s。熔断器，也叫断路器!(正常情况下断路器是关的只有出了问题才打开)用来保护微服务不雪崩的方法。在分布式链路中，有一个服务宕机了就会整个业务线瘫痪。]]></description><category></category></item><item><title><![CDATA[Spring Cloud OpenFeign快速入门]]></title><link>https://blog.youkuaiyun.com/AtYeXTolearn/article/details/130185755</link><guid>https://blog.youkuaiyun.com/AtYeXTolearn/article/details/130185755</guid><author>AtYeXTolearn</author><pubDate>Sun, 16 Apr 2023 18:35:04 +0800</pubDate><description><![CDATA[Feign是声明性(注解)web服务客户端。它使编写web服务客户端更加容易。要使用Feign,请创建一个接口并对其进行注解。它具有可插入注解支持，包括Feign注解和JAX-RS注解。Feign是声明性(注解)web服务客户端。它使编写web服务客户端更加容易。Feign还支持可插拔编码器和解码器。Spring Cloud添加了对Spring MVC注解的支持，并支持使用HttpMessageConverters，Spring web 中默认使用的注解。]]></description><category></category></item><item><title><![CDATA[Spring Cloud Ribbon 快速入门]]></title><link>https://blog.youkuaiyun.com/AtYeXTolearn/article/details/130161554</link><guid>https://blog.youkuaiyun.com/AtYeXTolearn/article/details/130161554</guid><author>AtYeXTolearn</author><pubDate>Sun, 16 Apr 2023 18:04:46 +0800</pubDate><description><![CDATA[Spring cloud Ribbon是一个基于HTTP和TCP的客户端负载均衡工具，它基于Netflix Ribbon实现。主要功能是提供客户端均衡算法和服务调用。Ribbon作为服务消费的负载均衡器，有两种使用方式，一种和RestTemplate相结合，另一种是和OpenFeign相结合。OpenFeign默认集成了Ribbon。]]></description><category></category></item><item><title><![CDATA[Eureka注册中心集群]]></title><link>https://blog.youkuaiyun.com/AtYeXTolearn/article/details/130159958</link><guid>https://blog.youkuaiyun.com/AtYeXTolearn/article/details/130159958</guid><author>AtYeXTolearn</author><pubDate>Fri, 14 Apr 2023 19:03:34 +0800</pubDate><description><![CDATA[当eureka客户端启动时，会向eureka服务端发送自己的元数据（原始数据）。eureka服           务端会在自己的内部保留这些数据（内存）。元数据：ip、端口、健康的状态监测等。]]></description><category></category></item><item><title><![CDATA[Eureka快速入门]]></title><link>https://blog.youkuaiyun.com/AtYeXTolearn/article/details/130089279</link><guid>https://blog.youkuaiyun.com/AtYeXTolearn/article/details/130089279</guid><author>AtYeXTolearn</author><pubDate>Tue, 11 Apr 2023 18:23:04 +0800</pubDate><description><![CDATA[​
Eureka是Netflix开发的服务发现框架，本身是一个基于REST的服务，主要用于定位运行在AWS域中的中间层服务，以达到负载均衡和中间层服务故障转移的目的。]]></description><category></category></item></channel></rss>