[Hinton] Neural Networks for Machine Learning - Converage

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[Hinton] Neural Networks for Machine Learning - Converage

Link: Neural Networks for Machine Learning - 多伦多大学 Link: Hinton的CSC321课程笔记 Ref: 神经网络训练中的Tricks之高效BP (反向传播算法) 关于梯度下降的东西,涉及的知识很多,有必要单独一章 Lecture 06 —— mini批量梯度训练及三个加速的方法 (详见链接) 一.mini-批量梯度下降概述这部分将介绍使用随机梯度下降(SGD)学习来训练NN,着重介绍mini-批量版本,而这个也是现今用的最广泛的关于训练大…

[Hinton] Neural Networks for Machine Learning - Basic

Link: Neural Networks for Machine Learning - 多伦多大学 Link: Hinton的CSC321课程笔记1 Link: Hinton的CSC321课程笔记2 一年后再看课程,亦有收获,虽然看似明白,但细细推敲其实能挖掘出很多深刻的内容:以下为在线课程以及该笔记的课程重难点总结. Lecture 01 增强学习: (这是ng的拿手好戏,他做无人直升机可是做了好久)增强学习的输出是一个动作或者一系列的动作,通过与实际的场合下的环境互动来决定动作,增强学习的…

[Hinton] Neural Networks for Machine Learning - RNN

Link: Neural Networks for Machine Learning - 多伦多大学 Link: Hinton的CSC321课程笔记补充: 参见cs231n 2017版本,ppt写得比过去更好. [译] 理解 LSTM 网络:模块内部解析讲得不错. Lecture 07 Lecture 08 完全递归网络(Fully recurrent network) Hopfield网络(Hopfield network) Elman networks and Jordan network…

[Hinton] Neural Networks for Machine Learning - Bayesian

Link: Neural Networks for Machine Learning - 多伦多大学 Link: Hinton的CSC321课程笔记 Lecture 09 Lecture 10 提高泛化能力介绍不同的方法去控制网络的数据表达能力,并介绍当我们使用这样一种方法的时候如何设置元参数,然后给出一个通过提早结束训练来控制网络能力(其实就是防止过拟合)的例子. 所以我们需要方法来阻止过拟合, 第一个方法也是目前最好的方法:就是简单的增加更多的数据,如果你能提供更多的数据,那么就不需要去提…

[Hinton] Neural Networks for Machine Learning - Hopfield Nets and Boltzmann Machine

Lecture 11 — Hopfield Nets Lecture 12 — Boltzmann machine learning Ref: 能量模型(EBM).限制波尔兹曼机(RBM) 高大上的模型和理论. Hopfield Nets 看了能量函数,发现: These look very much like the weights and biases of a neural network. [点到为止] Boltzmann machine learning From: A Beginne…

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Neural Networks for Machine Learning by Geoffrey Hinton （4）

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第四节，Neural Networks and Deep Learning 一书小节(上)

最近花了半个多月把Mchiael Nielsen所写的Neural Networks and Deep Learning这本书看了一遍,受益匪浅. 该书英文原版地址地址:http://neuralnetworksanddeeplearning.com/ 回顾一下这本书主要讲的内容 1.使用神经网络识别手写数字作者从感知器模型引申到S型神经元.然后再到神经网络的结构.并用一个三层神经网络结构来进行手写数字识别, 作者详细介绍了神经网络学习所使用到梯度下降法,由于当训练输入数量过大时,学习过程将变…