<?xml version="1.0" encoding="UTF-8"?><rss version="2.0"><channel><title>凡记</title><link>https://fanish.me/</link><description>技术、阅读与生活片刻</description><item><title><![CDATA[物联网与传感网课程复习笔记]]></title><link>https://fanish.me/articles/iot-and-sensor-network-review/</link><guid>https://fanish.me/articles/iot-and-sensor-network-review/</guid><pubDate>Tue, 11 Nov 2025 10:00:00 GMT</pubDate><description><![CDATA[整合物联网课程重点，记录相关概念与实践过程。]]></description></item><item><title><![CDATA[CNN 网络架构的设计]]></title><link>https://fanish.me/articles/cnn-architecture-design/</link><guid>https://fanish.me/articles/cnn-architecture-design/</guid><pubDate>Tue, 12 Aug 2025 10:00:00 GMT</pubDate><description><![CDATA[深入探索CNN架构设计的演进历程，从手工设计到网络架构搜索(NAS)，理解RegNet的设计空间优化方法]]></description></item><item><title><![CDATA[稠密连接网络 (DenseNet)]]></title><link>https://fanish.me/articles/densenet/</link><guid>https://fanish.me/articles/densenet/</guid><pubDate>Mon, 11 Aug 2025 10:00:00 GMT</pubDate><description><![CDATA[探索DenseNet的稠密连接机制与特征复用策略，对比传统CNN架构的参数效率与内存使用特点]]></description></item><item><title><![CDATA[残差网络 (ResNet 与 ResNeXt)]]></title><link>https://fanish.me/articles/resnet/</link><guid>https://fanish.me/articles/resnet/</guid><pubDate>Sun, 10 Aug 2025 14:00:00 GMT</pubDate><description><![CDATA[探索残差连接的革命性突破，深入理解ResNet如何解决深度网络训练难题]]></description></item><item><title><![CDATA[并行连接的网络 (GoogLeNet)]]></title><link>https://fanish.me/articles/googlenet/</link><guid>https://fanish.me/articles/googlenet/</guid><pubDate>Sun, 10 Aug 2025 12:00:00 GMT</pubDate><description><![CDATA[探索GoogLeNet的Inception架构创新，学习多尺度并行卷积的设计理念]]></description></item><item><title><![CDATA[使用批量归一化层的 LeNet]]></title><link>https://fanish.me/articles/bn-lenet/</link><guid>https://fanish.me/articles/bn-lenet/</guid><pubDate>Sun, 10 Aug 2025 10:00:00 GMT</pubDate><description><![CDATA[探索批量归一化在经典LeNet网络中的应用效果，对比自定义实现与PyTorch高级API的性能差异]]></description></item><item><title><![CDATA[网络中的网络 (NiN)]]></title><link>https://fanish.me/articles/nin/</link><guid>https://fanish.me/articles/nin/</guid><pubDate>Sat, 09 Aug 2025 11:00:00 GMT</pubDate><description><![CDATA[探索Network In Network架构的创新设计，理解1×1卷积和全局平均池化的重要作用]]></description></item><item><title><![CDATA[使用块的网络 (VGG)]]></title><link>https://fanish.me/articles/vgg-network/</link><guid>https://fanish.me/articles/vgg-network/</guid><pubDate>Sat, 09 Aug 2025 11:00:00 GMT</pubDate><description><![CDATA[探索VGG网络的模块化设计理念，理解块结构在深度网络中的重要作用和实现方法]]></description></item><item><title><![CDATA[深度卷积神经网络 (AlexNet)]]></title><link>https://fanish.me/articles/alexnet/</link><guid>https://fanish.me/articles/alexnet/</guid><pubDate>Sat, 09 Aug 2025 10:00:00 GMT</pubDate><description><![CDATA[探索深度学习史上的里程碑网络AlexNet，理解其创新的网络架构设计和关键技术突破]]></description></item><item><title><![CDATA[卷积神经网络(LeNet)]]></title><link>https://fanish.me/articles/lenet/</link><guid>https://fanish.me/articles/lenet/</guid><pubDate>Sat, 09 Aug 2025 09:00:00 GMT</pubDate><description><![CDATA[探索首个成功的卷积神经网络LeNet，了解其历史地位和网络架构设计，掌握早期CNN的实现原理]]></description></item><item><title><![CDATA[池化层]]></title><link>https://fanish.me/articles/pooling-layer/</link><guid>https://fanish.me/articles/pooling-layer/</guid><pubDate>Fri, 08 Aug 2025 14:00:00 GMT</pubDate><description><![CDATA[理解池化层的工作原理和作用，掌握最大池化和平均池化的实现方法，探索池化操作在CNN中的重要意义]]></description></item><item><title><![CDATA[多通道的输入输出]]></title><link>https://fanish.me/articles/multi-channel-io/</link><guid>https://fanish.me/articles/multi-channel-io/</guid><pubDate>Fri, 08 Aug 2025 12:00:00 GMT</pubDate><description><![CDATA[深入理解多通道卷积操作原理，掌握多输入多输出卷积层的实现方法和1×1卷积的应用技巧]]></description></item><item><title><![CDATA[填充与步幅]]></title><link>https://fanish.me/articles/padding-stride/</link><guid>https://fanish.me/articles/padding-stride/</guid><pubDate>Fri, 08 Aug 2025 10:00:00 GMT</pubDate><description><![CDATA[深入理解卷积神经网络中填充和步幅的作用机制，掌握控制特征图尺寸的关键技巧]]></description></item><item><title><![CDATA[图像卷积]]></title><link>https://fanish.me/articles/image-convolution/</link><guid>https://fanish.me/articles/image-convolution/</guid><pubDate>Thu, 07 Aug 2025 10:00:00 GMT</pubDate><description><![CDATA[深入理解图像卷积的数学原理和PyTorch实现，掌握卷积层的核心概念和应用技巧]]></description></item><item><title><![CDATA[稠密块]]></title><link>https://fanish.me/articles/dense-block/</link><guid>https://fanish.me/articles/dense-block/</guid><pubDate>Wed, 06 Aug 2025 10:00:00 GMT</pubDate><description><![CDATA[深入理解稠密连接网络中的稠密块结构，掌握DenseNet的核心设计思想和实现方法]]></description></item><item><title><![CDATA[残差块与分组残差块]]></title><link>https://fanish.me/articles/residual-block/</link><guid>https://fanish.me/articles/residual-block/</guid><pubDate>Wed, 06 Aug 2025 08:00:00 GMT</pubDate><description><![CDATA[深入理解残差连接的设计原理，掌握ResNet和ResNeXt中残差块和分组残差块的实现方法]]></description></item><item><title><![CDATA[从全连接层到卷积层：计算机视觉的数学基础]]></title><link>https://fanish.me/articles/from-fc-to-conv/</link><guid>https://fanish.me/articles/from-fc-to-conv/</guid><pubDate>Tue, 05 Aug 2025 12:00:00 GMT</pubDate><description><![CDATA[本文讲述从全连接层到卷积层的演进过程，掌握卷积神经网络的数学原理和设计思想]]></description></item><item><title><![CDATA[模型参数的读写]]></title><link>https://fanish.me/articles/model-parameter-io/</link><guid>https://fanish.me/articles/model-parameter-io/</guid><pubDate>Tue, 05 Aug 2025 10:00:00 GMT</pubDate><description><![CDATA[掌握PyTorch中模型参数的保存与加载技术，学习训练过程中的检查点管理]]></description></item><item><title><![CDATA[自定义层]]></title><link>https://fanish.me/articles/custom-layer/</link><guid>https://fanish.me/articles/custom-layer/</guid><pubDate>Mon, 04 Aug 2025 19:00:00 GMT</pubDate><description><![CDATA[本节将展示如何构建自定义层]]></description></item><item><title><![CDATA[层和块]]></title><link>https://fanish.me/articles/layers-and-blocks/</link><guid>https://fanish.me/articles/layers-and-blocks/</guid><pubDate>Mon, 04 Aug 2025 18:00:00 GMT</pubDate><description><![CDATA[深入理解深度学习中层和块的概念，掌握PyTorch中自定义模块的实现方法]]></description></item><item><title><![CDATA[延后初始化]]></title><link>https://fanish.me/articles/deferred-initialization/</link><guid>https://fanish.me/articles/deferred-initialization/</guid><pubDate>Mon, 04 Aug 2025 10:00:00 GMT</pubDate><description><![CDATA[本节讲述延后初始化技术，掌握在输入维度未知情况下的动态参数推断方法]]></description></item><item><title><![CDATA[参数管理]]></title><link>https://fanish.me/articles/parameter-management/</link><guid>https://fanish.me/articles/parameter-management/</guid><pubDate>Sun, 03 Aug 2025 10:00:00 GMT</pubDate><description><![CDATA[本节讲述PyTorch中的参数管理机制，掌握参数访问、初始化和共享的实用技巧]]></description></item><item><title><![CDATA[Claude Code 常用技巧]]></title><link>https://fanish.me/articles/claude-code-tips/</link><guid>https://fanish.me/articles/claude-code-tips/</guid><pubDate>Fri, 25 Jul 2025 18:00:00 GMT</pubDate><description><![CDATA[掌握Claude Code的进阶使用技巧，从基础命令到高级功能，提升AI编程效率]]></description></item><item><title><![CDATA[权重衰减]]></title><link>https://fanish.me/articles/weight-decay-l2-regularization/</link><guid>https://fanish.me/articles/weight-decay-l2-regularization/</guid><pubDate>Sat, 26 Apr 2025 18:00:00 GMT</pubDate><description><![CDATA[了解权重衰减（L2正则化）的数学原理，掌握正则化技术在防止过拟合中的应用]]></description></item><item><title><![CDATA[过拟合及其对抗方法：模型选择与多项式拟合实验]]></title><link>https://fanish.me/articles/overfitting-model-selection/</link><guid>https://fanish.me/articles/overfitting-model-selection/</guid><pubDate>Fri, 25 Apr 2025 10:00:00 GMT</pubDate><description><![CDATA[理解过拟合现象，掌握验证集、交叉验证等模型选择方法，通过多项式拟合实验探索欠拟合与过拟合]]></description></item><item><title><![CDATA[WSL的清理与迁出]]></title><link>https://fanish.me/articles/wsl-clear/</link><guid>https://fanish.me/articles/wsl-clear/</guid><pubDate>Fri, 21 Feb 2025 10:00:00 GMT</pubDate><description><![CDATA[记录一次Wsl ubuntu清理内存占用的过程]]></description></item><item><title><![CDATA[线性代数基础：标量、向量、矩阵与张量]]></title><link>https://fanish.me/articles/linear-algebra-basics/</link><guid>https://fanish.me/articles/linear-algebra-basics/</guid><pubDate>Sun, 08 Dec 2024 10:00:00 GMT</pubDate><description><![CDATA[深入理解线性代数的基本概念，包括标量、向量、矩阵和张量的定义、性质及PyTorch实现]]></description></item></channel></rss>