神经网络英语演讲PPT

神经网络介绍PPT详解课件

1940s
1960s
1980s
2000s
MP 模型阈值加和模型 Hebb学习规则
感知器模型自适应线性单元
Hopfield网络 Boltzman 机 BP算法
深度网络 DBN
CNN DBM
LeCun 98 Deep CNN RNN
低谷
低谷
人工神经网络发展历程
Deep Learning, Science 2006 (vol. 313, pp. 504-507)
网络模型
LeNet
网络结构的改进
NIN
AlexNet
网络深度的增加
VGGNet
GoogLeNet (Inception)
ResNet
Inception ResNet 图2：深度卷积神经网络发展图
图3：ILSVRC图像分类竞赛近年结果。
LeNet
最早的深度卷积神经网络模型，用于字符识别。网络具有如下特点：
AlexNet
AlexNet在LeNet基础上进行了更宽更深的网络设计，首次在CNN中引入了ReLU、 Dropout和Local Response Norm (LRN)等技巧。网络的技术特点如下：
➢ 使用ReLU （Rectified Linear Units）作为CNN的激活函数，并验证其效果在较深的网络超过了Sigmoid，成功解决了Sigmoid在网络较深时的梯度弥散问题，提高了网络的训练速率。
人工神经网络发展历程
• 发展基础：
✓ 数据爆炸：图像数据、文本数据、语音数据、社交网络数据、科学计算等
✓ 计算性能大幅提高
• 为什么有效
– 浅层神经网络可以近似任意函数，为何多层？
✓深层网络结构中，高层可以综合应用低层信息。 ✓低层关注“局部”，高层关注“全局”、更具有语

深度学习英文课件：卷积神经网络(Convolutional Neural Networks,CNN)

Convolutional NeuralNetworksCMSC 733 Fall 2015Angjoo KanazawaOverviewGoal: Understand what Convolutional Neural Networks (ConvNets) are & intuition behind it.1.Brief Motivation for Deep Learning2.What are ConvNets?3.ConvNets for Object DetectionFirst of all what is Deep Learning?●Composition of non-linear transformation ofthe data.●Goal: Learn useful representations, akafeatures, directly from data.●Many varieties, can be unsupervised or supervised.●Today is about ConvNets, which is a supervised deeplearning method.Recap: Supervised LearningSlide: M. RanzatoSupervised Learning: ExamplesSlide: M. RanzatoSupervised Deep LearningSo deep learning is about learning feature representation in acompositional manner.But wait,why learn features?Traditional Recognition ApproachPreprocessingFeatureExtraction(HOG, SIFT, etc)Post-processing(Feature selection,MKL etc)Classifier(SVM,boosting, etc)Traditional Recognition ApproachPreprocessingFeatureExtraction(HOG, SIFT, etc)Post-processing(Feature selection,MKL etc)Classifier(SVM,boosting, etc)H a n dE n g i ne e r e d●Most critical for accuracy ●Most time-consuming in development ●What is the best feature???●What is next?? Keep on crafting better features?⇒ Let’s learn feature representation directly from data.Preprocessing Feature Extraction (HOG, SIFT, etc)Post-processing (Feature selection, MKL etc)Learn features and classifiertogether⇒ Learn an end-to-end recognition system.A non-linear map that takes raw pixels directly to labels.Each box is a simple nonlinear function●Composition is at the core of deep learning methods ●Each “simple function” will have parameters subject toLayer 1Layer 2Layer 3Layer 4The final layer outputs a probability distribution of categories.A simple single layer Neural Network Consists of a linear combination of inputthrough a nonlinear function:W is the weight parameter to be learned.x is the output of the previous layerf is a simple nonlinear function. Popular choice is max(x,0), called ReLu (Rectified Linear Unit)1 layer: Graphical Representationff f h is called a neuron, hidden unit or feature.Joint training architecture overviewReduce connection to local regionsReuse the same kernel everywhereBecause interestingfeatures (edges) canhappen at anywhere inthe image.Convolutional Neural NetsDetailIf the input has 3 channels (R,G,B), 3 separate k by k filter is applied to each channel.Output of convolving 1 feature is called a feature map.This is just sliding window, ex. the output of one part filter of DPM is a feature mapUsing multiple filters Each filter detects features inthe output of previous layer.So to capture different features, learn multiple filters.Example of filteringSlide: R.FergusBuilding Translation InvarianceBuilding Translation Invariance via Spatial PoolingPooling also subsamples the image,allowing the next layer to look at largerspatial regions.Summary of a typical convolutional layerDoing all of this consists onelayer.○Pooling and normalization isoptional.Stack them up and train just like multi-layer neural nets.Final layer is usually fully connectedneural net with output size == number ofclassesRevisiting the composition ideaEvery layer learns a feature detector by combining the output of the layer before.⇒ More and more abstract features are learned as we stack layers.Keep this in mind and let’s look at what kind of things ConvNets learn.Slide: R.FergusArchitecture of Alex Krizhevsky et al.●8 layers total.●Trained on Imagenet Dataset(1000 categories, 1.2Mtraining images, 150k testimages)●18.2% top-5 error○Winner of the ILSVRC-2012 challenge.Architecture of Alex Krizhevsky et al.First layer filtersShowing 81 filters of 11x11x3.Capture low-level features like oriented edges, blobs.Note these oriented edges are analogous to what SIFT uses to compute the gradients.Top 9 patches that activate each filterin layer 1Each 3x3 block showsthe top 9 patches forone filter.Note how the previous low-level features are combined to detect a little more abstract features like textures.。

MLP神经网络PPT课件

contents
• structure • universal theorem • MLP for classification • mechanism of MLP for classification
– nonlinear mapping – binary coding of the areas
i, j
Ep i jk
O k1 i
• Situation for k M
E p i jM
E p O j M
O j M i jM
( y j OjM ) f '(ijM )
• Situation for k M
Ep
i jk
l
E p il k1
il k1 O j k
O j k i jk
l
• We ended by looking at some practical issues that didn’t arise for the single layer networks
Structure of an MLP
• it is composed of several layers • neurons within each layer are not connected • ith layer is only fully connected to the (i+1)jth layer • Signal is transmitted only in a feedforward manner
ij (xi )
• It is impractical
– the functions Ej(.) and ij (.) are not the simple weighted sums passed through

深度神经网络PPT课件

“Deep learning is a set of algorithms in machine learning that attempt to model high-level abstractions in data by using model architectures composed of multiple non-linear transformations.” (Aug. 2014)
• ImageNet 2014 – object detection challenge
Neural network Back propagation
Deep belief net Science
Speech
1986
2006
2011 2p learning based visual search engines (2013)
Vincent又从大脑认知角度给了解释：人类具有认知被阻挡的破损图像能力，此源于我们高等的联想记忆感受机能。
那输第入二信层息和的第第一二层个的表训达练了方。式就没有差别了，将第一层输出的c•ode当S成o第lv二e层g的e输n入e信ra号l ，le同a样rn最i小ng化重p构ro误b差le，m就s会得到第二层的参数，并且得到第二层输入的code，也就是原
Neural network Back propagation
Nature
Deep belief net Science
Speech
通常能够获取到输入的“层次型分组”或者“部分-整体分解”结构。
p(v,h), p(v), p(h), p(v|h), p(h|v)
Wang, an1d9X8.6
2006
系统主要的计算和测试工作都耗在这一大部分。其他层也以同样的方法进行。

RBF神经网络英文课件

Computing the Output Weights Our equations for the weights are most conveniently written in matrix form by defining matrices with components (Wkj = wkj, (Φpj = φj(xp, and (Tpk = {tkp}. This gives Φ T ΦW T − T = 0 and the formal solution for the weights is ( W T = Φ †T in which we have the standard pseudo inverse of Φ Φ † ≡ (Φ T Φ −1 Φ T which can be seen to have the property Φ †Φ = I. We see that the network weights can be computed by fast linear matrix inversion techniques. In practice we tend to use singular value decomposition (SVD to avoid possible ill-conditioning of Φ , i.e. ΦTΦ being singular or near singular. L13-11Overview and Reading 1. 2. 3. 4. 5. We began by defining Radial Basis Function (RBF mappings and the corresponding network architecture. Then we considered the computational power of RBF networks. We then saw how the two layers of network weights were rather different and different techniques were appropriate for training each of them. We first looked at several unsupervised techniques for carrying out the first stage, namely optimizing the basis functions. We then saw how the second stage, determining the output weights, could be performed by fast linear matrix inversion techniques. Reading 1. 2. Bishop: Sections 5.2, 5.3, 5.9, 5.10, 3.4 Haykin: Sections 5.4, 5.9, 5.10, 5.13 L13-12。

人工神经网络讲稿ppt课件

举例：2-3岁小孩能够从人群中认出父母、3-4岁能够顺利地穿过十字路口，但最先进机器人也难以完成这项任务。
因而模仿人类思维方式能够提升机器人能力
人工神经网络讲稿
5/40
1.2 神经细胞与生物神经网络
1. 神经网络
组织形式大脑中大约有100亿个神经元，它们相互连接，形成一个复杂庞大网络
系统。所以大脑结构是一个神经（元）网络。依据预计，每个神经元大约与上千个神经元相互连接。大脑所形成神经网络是由一些小网络连接而成。依据预计，全部神经元
层次结构：神经元联接按层次排列。模块结构：主要特点是将整个网络按功效划分为不一样模块，每个模块内部神经元紧密互联，并完成各自特定功效，模块之间再互联以完成整体功效；层次模块结构：将模块结构和层次结构结合起来，使之更靠近人脑神经系统结构，这也是当前为人们广泛注意一个新型网络互联模式。依据网络中神经元层数不一样，可将神经网络分为单层网络和多层网络；依据同层网络神经元之间有没有相互联接以及后层神经元与前层神经元有没有反馈作用不一样，可将神经网络分为以下各种。
Hopfield网络和BP算法出现，使得人工神经研究出现了复兴。因为人工神经网络在信息处理方面优点，使得大批学者加入到了这一研究领域，掀起了神经网络研究新高潮。
人工神经网络讲稿
13/40
4. 全方面发展时期（1987-现在） 1987年在美国召开了第一届国际神经网络学术大会，并宣告成立了
国际神经网络学会，与会代表1600多人。这次大会也宣告了神经网络学科诞生。神经网络研究进入了一个转折点，其范围不停扩大，领域几乎包含各个方面。神经网络应用使工业技术发生了很大改变，尤其是在自动控制领域有了新突破。
互制约，从而能够将层内神经元分为几组，让每组作为一个整体来动作。

神经网络基本理论资料PPT课件

1984年，博士又提出了连续神经网络模型，实现了神经网络的电子线路仿真，开拓了计算机应用神经网络的新途径，成功解决了著名的优化组合问题——旅行商问题，引起了相关领域研究人员的广泛关注。
1986年，等提出多层网络的逆推学习算法，即BP算法，否定了M.Minsky等人的错误结论，该算法一直成为应用最广、研究最多、发展最快的算法。
2.1 神经网络概述
胞体：也称为细胞体，包括细胞质、细胞核和细胞膜三部分，是细胞的营养中心。
树突：胞体的伸延部分产生的分枝称为树突，是接受从其它神经元传入的信息入口。但不一定是神经传入的唯一通道，还可以是胞体膜。
轴突：每个神经元只有一个轴突，一般自胞体发出，与一个或多个目标神经元连接，为神经元的输出通道，其作用是将细胞体发出的神经冲动传递给另一个或多个神经元。
如果在输出层没有得到期望的输出，则计算输出层的误差变化值，然后转向反向传播，通过网络将误差信号沿原来的连接通路反传回
1949年，心理学家提出神经来，修改各层神经元的权值，直至达到期望目标。
但人们在应用专家系统解决语音识别、图像处理和机器人控制等类似人脑形象思维的问题时却遇到很大的唐困纳难。德·赫布
BP算法的核心是最速下降法，这是一种以梯度为基础的误差下降算法，具有原理简单、实现方便等特点，但也有许多不足之处：联想记忆的作用是用一个不完整或模糊的信息联想出存储在记忆中的某个完整、清晰的模式来。
初创期：标志就是提出模型，建立规则。神经网络的自学习和自适应能力使其成为对各类信号进行多用途加工处理的一种天然工具。人工智能
侧，右脑支配人体的左侧，大脑受伤会使他支配的那部分身体产生功能障碍。
左右脑具有不同的功能。左脑主要是语言中枢，同时从事分析性工作，如逻辑推理、数学运算和写作等。右脑主要处理空间概念和模式识别。

BP神经网络模型PPT课件

激活函数: f()
误差函数：e

1 2
q o1
(do (k )

yoo (k ))2
BP网络的标准学习算法
第一步，网络初始化给各连接权值分别赋一个区间（-1，1）内的随机数，设定误差函数e，给定计算精度值和最大学习次数M。
第二步,随机选取第 k个输入样本及对应期望输出
修正各单元权值
误差的反向传播
BP网络的标准学习算法-学习过程
正向传播：
输入样本－－－输入层－－－各隐层－－－输出层
判断是否转入反向传播阶段：
若输出层的实际输出与期望的输出（教师信号）不符
误差反传
误差以某种形式在各层表示－－－－修正各层单元的权值
网络输出的误差减少到可接受的程度进行到预先设定的学习次数为止
x(k) x1(k), x2(k), , xn(k)
do (k) d1(k),d2(k), ,dq(k)
BP网络的标准学习算法
第三步，计算隐含层各神经元的输入和
输出
n
hih (k ) wih xi (k ) bh
i 1
h 1, 2, , p
hoh (k) f(hih (k)) h 1, 2, , p

f(
yio (k)))2)
hoh (k)
hoh (k)
hih (k)

( 1 2
q
((do (k)
o1

p
f(
h1
whohoh (k)
bo )2 ))
hoh (k)
hoh (k)
hih (k)

q o1
(do (k )

神经网络之——递归神经网络PPT演示课件

dt
If a1 a2, then
at, a1 at, a2 for any t 0
L
35
Trajectories
da(t) ga(t), p(t),t
dt
If a1 a2, then
at, a1 at, a2 for any t 0
L
36
A Simple Example
L
46
/people/seung/index.html
L
47
Linear RNNs
H. S. Seung, How the brain keeps the eyes still, Proc. Natl. Acad. Sci. USA, vol. 93, pp. 13339-13344, 1996
x(t ) x(t) x(t) f wx(t) b

L
28
From Discrete Computing to Continuous Computing
x(t ) x(t) x(t) f wx(t) b

0
dx(t) x(t) f wx(t) b
dt
L
29
Continuous Computing RNNs
dx(t) x(t) f wx(t) b
dt
L
30
Recurrent NNs
RNN model:
da(t) ga(t), p(t),t
dt
Network time
Network state
Network input
x(k 1) f wx(k) b
L
18
Discrete Time RNNs