https://answer.freetools.me/tags/%E6%A2%AF%E5%BA%A6%E4%B8%8B%E9%99%8D/ Recent content in 梯度下降 on Answer Hugo -- 0.152.2 zh-cn Fri, 13 Mar 2026 00:53:12 +0800 https://answer.freetools.me/%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C%E7%9A%84%E4%BC%98%E5%8C%96%E6%99%AF%E8%A7%82%E4%B8%BA%E4%BB%80%E4%B9%88%E9%9D%9E%E5%87%B8%E6%8D%9F%E5%A4%B1%E5%87%BD%E6%95%B0%E6%B2%A1%E6%9C%89%E6%83%B3%E8%B1%A1%E4%B8%AD%E5%8F%AF%E6%80%95/ Fri, 13 Mar 2026 00:53:12 +0800 https://answer.freetools.me/%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C%E7%9A%84%E4%BC%98%E5%8C%96%E6%99%AF%E8%A7%82%E4%B8%BA%E4%BB%80%E4%B9%88%E9%9D%9E%E5%87%B8%E6%8D%9F%E5%A4%B1%E5%87%BD%E6%95%B0%E6%B2%A1%E6%9C%89%E6%83%B3%E8%B1%A1%E4%B8%AD%E5%8F%AF%E6%80%95/ 从损失景观的几何性质出发，深入解析为什么梯度下降能够在高度非凸的高维空间中找到好解。涵盖鞍点、平坦最小值、模式连通性等核心概念，以及残差连接、批量归一化等技术如何重塑优化景观。 https://answer.freetools.me/%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C%E6%98%AF%E5%A6%82%E4%BD%95%E5%AD%A6%E4%B9%A0%E7%9A%84%E4%BB%8E%E5%89%8D%E5%90%91%E4%BC%A0%E6%92%AD%E5%88%B0%E5%8F%8D%E5%90%91%E4%BC%A0%E6%92%AD%E7%9A%84%E5%AE%8C%E6%95%B4%E8%AE%AD%E7%BB%83%E8%BF%87%E7%A8%8B%E8%A7%A3%E6%9E%90/ Thu, 12 Mar 2026 16:06:50 +0800 https://answer.freetools.me/%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C%E6%98%AF%E5%A6%82%E4%BD%95%E5%AD%A6%E4%B9%A0%E7%9A%84%E4%BB%8E%E5%89%8D%E5%90%91%E4%BC%A0%E6%92%AD%E5%88%B0%E5%8F%8D%E5%90%91%E4%BC%A0%E6%92%AD%E7%9A%84%E5%AE%8C%E6%95%B4%E8%AE%AD%E7%BB%83%E8%BF%87%E7%A8%8B%E8%A7%A3%E6%9E%90/ 神经网络是如何学习的：从前向传播到反向传播的完整训练过程解析 https://answer.freetools.me/%E6%A2%AF%E5%BA%A6%E4%B8%8B%E9%99%8D%E4%BC%98%E5%8C%96%E5%99%A8%E4%BB%8Esgd%E5%88%B0adamw%E4%B8%BA%E4%BB%80%E4%B9%88%E8%BF%99%E4%B8%AA%E9%80%89%E6%8B%A9%E8%83%BD%E5%86%B3%E5%AE%9A%E6%A8%A1%E5%9E%8B%E7%9A%84%E5%91%BD%E8%BF%90/ Thu, 12 Mar 2026 06:04:32 +0800 https://answer.freetools.me/%E6%A2%AF%E5%BA%A6%E4%B8%8B%E9%99%8D%E4%BC%98%E5%8C%96%E5%99%A8%E4%BB%8Esgd%E5%88%B0adamw%E4%B8%BA%E4%BB%80%E4%B9%88%E8%BF%99%E4%B8%AA%E9%80%89%E6%8B%A9%E8%83%BD%E5%86%B3%E5%AE%9A%E6%A8%A1%E5%9E%8B%E7%9A%84%E5%91%BD%E8%BF%90/ 深入解析深度学习中梯度下降优化器的演进历程：从最基础的SGD到现代的AdamW，从Momentum到Nesterov，从AdaGrad到RMSprop。剖析为什么Adam收敛快却泛化不如SGD，为什么权重衰减需要解耦，以及不同场景下如何选择正确的优化器。