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Disclaimer This post is from the link posted by GitHub user Vonng in this comment. All credit for this post goes to the original author. Table of Contents 神经网络表示 神经元模型 单层神经网络模型 多层神经网络模型 神经网络推断 神经网络训练 代价函数 梯度下降算法 反向传播 反向传播误差δ 反向传播方程 反向传播方程的证明 神经网络的实现 神经网络优化 代价函数：交叉熵 规范化 神经网络相关基本知识笔记 神经网络表示 # 神经元模型 # 神经网络从大脑的工作原理得到启发，可用于解决通用的学习问题。神经网络的基本组成单元是 神经元(neuron) 。每个神经元具有一个轴突和多个树突。每个连接到本神经元的树突都是一个输入，当所有输入树突的兴奋水平之和超过某一阈值，神经元就会被激活。激活的神经元会沿着其轴突发射信号，轴突分出数以万计的树突连接至其他神经元，并将本神经元的输出并作为其他神经元的输入。数学上，神经元可以用 感知机 的模型表示。 一个神经元的数学模型主要包括以下内容： 名称 符号 说明 输入 (input) \(x\) 列向量 权值 (weight) \(w\) 行向量，维度等于输入个数 偏置 (bias) \(b\) 标量值，是阈值的相反数 带权输入 (weighted input) \(z\) \(z=w · x + b\) ，激活函数的输入值 激活函数 (activation function) \(σ\) 接受带权输入，给出激活值。 激活值 (activation) \(a\) 标量值，\(a = σ(\vec{w}·\vec{x}+b)\) 激活函数表达式 # \[ a = \sigma( \left[ \begin{matrix} w_{1} & ⋯ & w_{n} \\ \end{matrix}\right] · \left[ \begin{array}{x} x_1 \\ ⋮ \\ ⋮ \\ x_n \end{array}\right] + b ) \]
ox-hugo Issue #336 This issue only appears when there is a citation Giovanelli (2016) Tag Exclude tag References # Giovanelli, Marco. 2016. “"...But I Still Can’t Get Rid of a Sense of Artificiality" The Reichenbach–Einstein Debate on the Geometrization of the Electromagnetic Field.” Studies in History and Philosophy of Science 54: 35–51.
TODO states if present are prefixed to the headings – both in the body and TOC.
No TODO states are prefixed to the headings – neither in the body, nor in the TOC.
Test the #+toc: headlines N local syntax where a TOC is exported
containing headings only up to level-N relative to the heading in
which that keyword is used.
Test the #+toc keyword with :target attribute
hugo-bare-min-theme + Hugo 0.101.0
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