<?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>Cnn on Jeanphilo Blog</title><link>https://shio-chan-dev.github.io/jeanblog/zh/tags/cnn/</link><description>Recent content in Cnn on Jeanphilo Blog</description><generator>Hugo -- 0.159.2</generator><language>zh-cn</language><lastBuildDate>Sat, 24 Jan 2026 16:28:40 +0800</lastBuildDate><atom:link href="https://shio-chan-dev.github.io/jeanblog/zh/tags/cnn/index.xml" rel="self" type="application/rss+xml"/><item><title>CNN 参数量计算：从卷积核到整网规模</title><link>https://shio-chan-dev.github.io/jeanblog/zh/ai/vision/cnn-parameter-count/</link><pubDate>Sat, 24 Jan 2026 16:28:40 +0800</pubDate><guid>https://shio-chan-dev.github.io/jeanblog/zh/ai/vision/cnn-parameter-count/</guid><description>系统讲清 CNN 参数量计算方法与常见陷阱，并给出最小 PyTorch 示例。</description></item><item><title>CNN、RNN、LSTM 与 Transformer 的区别与适用场景</title><link>https://shio-chan-dev.github.io/jeanblog/zh/ai/architecture/cnn-rnn-lstm-transformer-comparison/</link><pubDate>Sat, 24 Jan 2026 16:28:18 +0800</pubDate><guid>https://shio-chan-dev.github.io/jeanblog/zh/ai/architecture/cnn-rnn-lstm-transformer-comparison/</guid><description>从依赖路径长度与资源复杂度两个核心概念出发，系统对比 CNN、RNN、LSTM 与 Transformer，并给出可运行示例与工程选型步骤。</description></item></channel></rss>