Neural Networks for Machine Learning by Geoffrey Hinton （1～2）

【Neural Networks for Machine Learning by Geoffrey Hinton （1～2）】的更多相关文章

Neural Networks for Machine Learning by Geoffrey Hinton （1～2）

机器学习能良好解决的问题识别模式识别异常预測大脑工作模式人类有个神经元,每一个包括个权重,带宽要远好于工作站. 神经元的不同类型 Linear (线性)神经元 Binary threshold (二值)神经元 watermark/2/text/aHR0cDovL2Jsb2cuY3Nkbi5uZXQv/font/5a6L5L2T/fontsize/400/fill/I0JBQkFCMA==/dissolve/70/gravity/Center" width="300&quo…

Neural Networks for Machine Learning by Geoffrey Hinton （4）

一种能够学习家谱关系的简单神经网络血缘一共同拥有12种关系: son, daughter, nephew, niece, father, mother, uncle, aunt, brother, sister, husband, wife 有1个英国家庭以及1个意大利家庭,每一个家庭有12个人. 各种家庭关系都是可用三元数组表示.即( Agent / Relation / Patient ) 结构: (colin has-father james) (colin has-mother vic…

[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 - 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 - 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…

Machine Learning and Data Mining（机器学习与数据挖掘）

Problems[show] Classification Clustering Regression Anomaly detection Association rules Reinforcement learning Structured prediction Feature engineering Feature learning Online learning Semi-supervised learning Unsupervised learning Learning to rank…

[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…

课程一(Neural Networks and Deep Learning)，第二周（Basics of Neural Network programming）—— 4、Logistic Regression with a Neural Network mindset

Logistic Regression with a Neural Network mindset Welcome to the first (required) programming exercise of the deep learning specialization. In this notebook you will build your first image recognition algorithm. You will build a cat classifier that r…

课程一(Neural Networks and Deep Learning)，第一周（Introduction to Deep Learning）—— 2、10个测验题

1.What does the analogy “AI is the new electricity” refer to? (B) A. Through the “smart grid”, AI is delivering a new wave of electricity. B. Similar to electricity starting about 100 years ago, AI is transforming multiple industries. C. AI is power…