Machine Learning - IX. Neural Networks Learning (Week 5)

http://blog.csdn.net/pipisorry/article/details/44119187

机器学习Machine Learning - Andrew NG courses学习笔记

Neural Networks Learning 神经网络学习

Neural Networks are one of the most powerful learning algorithms that we have today.

Cost Function代价函数

Note: 对于multi-class classfication,要求K>=3. if we had two classes then, we will need to use only one output unit.{[01]+[10]和0+1效果一样}

Note: h of x subscript i, to denote the ith output.That is h of x is a K dimensional vector.

Except that we don‘t sum over the terms corresponding to these bias values

Backpropagation Algorithm BP反向传播算法

Backpropagation Intuition反向传播直觉知识

Implementation Note_ Unrolling Parameters执行节点展开参数

Gradient Checking梯度检查

Random Initialization随机初始化

Putting It Together组合在一起

Autonomous Driving自动驾驶

from:http://blog.csdn.net/pipisorry/article/details/44119187

时间： 2024-12-09 22:56:56

Machine Learning - IX. Neural Networks Learning (Week 5)的相关文章

Machine Learning - Neural Networks Learning: Cost Function and Backpropagation

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

Machine Learning - Neural Networks Learning: Backpropagation in Practice

【论文笔记】Learning Convolutional Neural Networks for Graphs

Learning Convolutional Neural Networks for Graphs 2018-01-17 21:41:57 [Introduction] 这篇 paper 是发表在 ICML 2016 的:http://jmlr.org/proceedings/papers/v48/niepert16.pdf 上图展示了传统 CNN 在 image 上进行卷积操作的工作流程.(a)就是通过滑动窗口的形式,利用3*3 的卷积核在 image 上进行滑动,来感知以某一个像素点为中心

Learning Convolutional Neural Networks for Graphs（网友分析）

paper Learning Convolutional Neural Networks for Graphs论文导读及代码链接(比较细,全面) [论文笔记]Learning Convolutional Neural Networks for Graphs(与上面这篇互为补充) Learning Convolutional Neural Networks for Graphs(上面2个的进一步补充) 原文地址:https://www.cnblogs.com/hugh2006/p/12642135

Machine Learning - VIII. Neural Networks Representation (Week 4)

http://blog.csdn.net/pipisorry/article/details/4397356 机器学习Machine Learning - Andrew NG courses学习笔记 Neural Networks Representation神经网络表示 Non-linear Hypotheses非线性假设 Neurons and the Brain神经元和大脑 Model Representation模型表示 Examples and Intuitions示例和直觉知识 Mu

Stanford机器学习---第五讲. 神经网络的学习 Neural Networks learning

原文见http://blog.csdn.net/abcjennifer/article/details/7758797,加入了一些自己的理解本栏目(Machine learning)包含单參数的线性回归.多參数的线性回归.Octave Tutorial.Logistic Regression.Regularization.神经网络.机器学习系统设计.SVM(Support Vector Machines 支持向量机).聚类.降维.异常检測.大规模机器学习等章节.全部内容均来自Standford

Coursera 机器学习第5章 Neural Networks: Learning 学习笔记

5.1节 Cost Function神经网络的代价函数. 上图回顾神经网络中的一些概念: L 神经网络的总层数. sl 第l层的单元数量(不包括偏差单元). 2类分类问题:二元分类和多元分类. 上图展现的是神经网络的损失函数,注意这是正则化的形式. 正则化部分,i.j不为0.当然i.j可以为0,此时的损失函数不会有太大的差异,只是当i.j不为0的形式更为常见. 5.2节 Backpropagation Algorithm最小化损失函数的算法——反向传播算法:找到合适的参数是J(θ)最小. 如

斯坦福大学公开课机器学习： neural networks learning - autonomous driving example（通过神经网络实现自动驾驶实例）

使用神经网络来实现自动驾驶,也就是说使汽车通过学习来自己驾驶. 下图是通过神经网络学习实现自动驾驶的图例讲解: 左下角是汽车所看到的前方的路况图像.左上图,可以看到一条水平的菜单栏(数字4所指示方向),白亮的区段显示的就是人类驾驶者选择的方向.而最右端则对应向右急转的操作(箭头3),中心稍微向左一点的位置(箭头2),则表示在这一点上人类驾驶者的操作是慢慢的向左拐.这幅图的第二部分(箭头5)对应的就是学习算法选出的行驶方向,类似的白亮的区段(箭头6)显示的就是神经网络在这里选择的行驶方向是稍微的左

论文笔记-Sequence to Sequence Learning with Neural Networks

大体思想和RNN encoder-decoder是一样的,只是用来LSTM来实现. paper提到三个important point: 1)encoder和decoder的LSTM是两个不同的模型 2)deep LSTM表现比shallow好,选用了4层的LSTM 3)实践中发现将输入句子reverse后再进行训练效果更好.So for example, instead of mapping the sentence a,b,c to the sentence α,β,γ, the LSTM i