Backpropagation

摘要 本文是对 Andrew Ng 在 Coursera 上的机器学习课程中 Backpropagation Algorithm 一小节的延伸.文章分三个部分:第一部分给出一个简单的神经网络模型和 Backpropagation(以下简称 BP)算法的具体流程.第二部分以分别计算第一层和第二层中的第一个参数(parameters,在神经网络中也称之为 weights)的梯度为例来解释 BP 算法流程,并给出了具体的推导过程.第三个部分采用了更加直观的图例来解释 BP 算法的工作流程. 注:1.

Calculus on Computational Graphs: Backpropagation Introduction Backpropagation is the key algorithm that makes training deep models computationally tractable. For modern neural networks, it can make training with gradient descent as much as ten milli

This series of articles are the study notes of " Machine Learning ", by Prof. Andrew Ng., Stanford University. This article is the notes of week 5, Neural Networks Learning. This article contains some topic about Cost Function and Backpropagatio

This series of articles are the study notes of " Machine Learning ", by Prof. Andrew Ng., Stanford University. This article is the notes of week 5, Neural Networks Learning. This article contains some topic about how to apply Backpropagation alg

Principles of training multi-layer neural network using backpropagation http://galaxy.agh.edu.pl/~vlsi/AI/backp_t_en/backprop.html The project describes teaching process of multi-layer neural network employing backpropagation algorithm. To illustrate

About Contact Subscribe Back-propagation, an introduction Sanjeev Arora and Tengyu Ma  ?  Dec 20, 2016  ?  20 minute read Given the sheer number of backpropagation tutorials on the internet, is there really need for another? One of us (Sanjeev) recen

神经网络的训练算法,目前基本上是以Backpropagation (BP) 反向传播为主(加上一些变化),NN的训练是在1986年被提出,但实际上,BP 已经在不同领域中被重复发明了数十次了(参见 Griewank (2010)[1]).更加一般性且与应用场景独立的名称叫做:反向微分 (reverse-mode differentiation).本文是看了资料[2]中的介绍,写的蛮好,自己记录一下,方便理解. 从本质上看,BP 是一种快速求导的技术,可以作为一种不单单用在深度学习中并且可以胜任大

没啥自己的内容,推荐若干链接,这些是真爱,你值得拥有. Principles of training multi-layer neural network using backpropagation A Step by Step Backpropagation Example Derivation: Derivatives for Common Neural Network Activation Functions

In the last chapter we saw how neural networks can learn their weights and biases using the gradient descent algorithm. There was, however, a gap in our explanation: we didn't discuss how to compute the gradient of the cost function. That's quite a g

几个有助于加深对反向传播算法直观理解的网页,包括普通前向神经网络,卷积神经网络以及利用BP对一般性函数求导(UFLDL) A Visual Explanation of the Back Propagation Algorithm for Neural Networks By Sebastian Raschka, Michigan State University. Let's assume we are really into mountain climbing, and to add a l