SlideShare a Scribd company logo

Introductions to Neural Networks,Basic concepts

•
•
Q
Qin Jian
Technology
1 of 17
Download to read offline
Introductions to Neural Networks Basic concepts Jian QIN Intro Single-Layer Introductions to Neural Networks Basic concepts Multilayer Applications Next Jian QIN December 23, 2013 . . . . . .
Introductions to Neural Networks Basic concepts Outline Jian QIN Intro Intro Single-Layer Multilayer Applications Single-Layer Next Multilayer Applications Next . . . . . .
Introductions to Neural Networks Basic concepts Show Jian QIN Intro Single-Layer Multilayer Applications Next ./asamples.gif Get more from http://yann.lecun.com/exdb/lenet/index.html . . . . . .
Introductions to Neural Networks Basic concepts Show cont. Jian QIN Intro Single-Layer Multilayer Applications Next Source: Learning Internal Representations by Error Propagation . . . . . .
Introductions to Neural Networks Basic concepts Neuron Jian QIN Intro Single-Layer Multilayer Applications Next ▶ Input Vector: X = [x0 = 1, x1 , x2 , x3 …] ▶ Weights: W = [w0 = b, w1 , w2 , w3 …] ▶ Sum: X*WT ▶ Activation Function … . . . . . .
Introductions to Neural Networks Basic concepts Activation Function Jian QIN Intro Single-Layer ▶ Threshold Multilayer Applications 1 2 3 4 5 if < threshold then 0 else 1 ▶ Next Sigmoid 1 1. y = 1+e−ax 2. y = tanh(x) . . . . . .
Introductions to Neural Networks Basic concepts Perceptrons Jian QIN Intro ▶ ▶ Single-Layer Can perform pattern classification only on linearly separable patterns. Multilayer Applications XOR Problem Next a 1 0 1 0 b 1 1 0 0 a XOR b 0 1 1 0 Critique from Minsky and Selfridge . . . . . .
Introductions to Neural Networks Basic concepts Perceptrons ▶ On-Line learning (One by One) Jian QIN Intro w(0) = [0, 0, 0...] Single-Layer w(n + 1) = w(n) + η [d(n) − y(n)] x(n) ▶ Multilayer Applications Batch Learning where E is the set of misclassifed x perceptron cost function J(w) = ∑ Next (−wT x) x∈E gradient vector J(w) = ∑ (−x) x∈E algorithm w(n + 1) = w(n) − η J(w) ∑ w(n + 1) = w(n) − η x . . x∈E . . . .
Introductions to Neural Networks Basic concepts SVM Jian QIN A kernel learning method on (sort of) single layer perceptron. Intro Single-Layer Multilayer Applications Next From: PDF materials of Neural Networks and Learning Machines . . . . . .
Introductions to Neural Networks Basic concepts Multilayer Perceptrons Jian QIN Intro ▶ Input + Hidden Layer + Output Single-Layer Multilayer Applications Next http://www.seattlerobotics.org/encoder/ nov98/neural.html . . . . . .
Introductions to Neural Networks Basic concepts Feed Forward Jian QIN Intro Single-Layer Multilayer Applications Next xlayer(N+1) = sgn(xlayer(N) ∗ Wlayer(N) ) . . . . . .
Introductions to Neural Networks Basic concepts Back-Propagation Jian QIN Intro Single-Layer Multilayer Cost Function Applications 1 ∑∑ 2 = ej (n) N N ξav Next n=1 j∈C ej (n) = dj (n) − yj (n) http://galaxy.agh.edu.pl/~vlsi/AI/backp_t_en/ backprop.html . . . . . .
Introductions to Neural Networks Basic concepts Word2Vec Jian QIN Intro Single-Layer Multilayer Applications Next Working on Java Version. 1/7 speed of original c version. Original Version: https://code.google.com/p/word2vec/ . . . . . .
Introductions to Neural Networks Basic concepts matlab code Jian QIN Intro Single-Layer Multilayer Applications Next Hand-writing Image Processing . . . . . .
Introductions to Neural Networks Basic concepts Personal Views Jian QIN Intro Single-Layer Multilayer Applications Next 1. Pattern Recognition: Image Processing. 2. Build Prior Information into NN: by hand. 3. Have no idea about the structure of the solution. . . . . . .
Introductions to Neural Networks Basic concepts Next Jian QIN Intro Single-Layer Multilayer Applications Next 1. Recurrent NN 2. Applications 3. Neural Networks on NLP. . . . . . .
Introductions to Neural Networks Basic concepts Links Jian QIN Intro Single-Layer Multilayer ▶ Machine Learning Applications Next https://www.coursera.org/course/ml ▶ Neural Networks for Machine Learning https://www.coursera.org/course/neuralnets ▶ Neural Networks and Learning Machines http://book.douban.com/subject/5952531/ . . . . . .

Recommended

The Perceptron (D1L1 Insight@DCU Machine Learning Workshop 2017)
The Perceptron (D1L1 Insight@DCU Machine Learning Workshop 2017)The Perceptron (D1L1 Insight@DCU Machine Learning Workshop 2017)
The Perceptron (D1L1 Insight@DCU Machine Learning Workshop 2017)Universitat Politècnica de Catalunya
 
Convolutional Neural Networks - Veronica Vilaplana - UPC Barcelona 2018
Convolutional Neural Networks - Veronica Vilaplana - UPC Barcelona 2018Convolutional Neural Networks - Veronica Vilaplana - UPC Barcelona 2018
Convolutional Neural Networks - Veronica Vilaplana - UPC Barcelona 2018Universitat Politècnica de Catalunya
 
FUNCTION APPROXIMATION
FUNCTION APPROXIMATIONFUNCTION APPROXIMATION
FUNCTION APPROXIMATIONankita pandey
 
Convolutional Neural Networks - Xavier Giro - UPC TelecomBCN Barcelona 2020
Convolutional Neural Networks - Xavier Giro - UPC TelecomBCN Barcelona 2020Convolutional Neural Networks - Xavier Giro - UPC TelecomBCN Barcelona 2020
Convolutional Neural Networks - Xavier Giro - UPC TelecomBCN Barcelona 2020Universitat Politècnica de Catalunya
 
Multilayer Perceptron - Elisa Sayrol - UPC Barcelona 2018
Multilayer Perceptron - Elisa Sayrol - UPC Barcelona 2018Multilayer Perceptron - Elisa Sayrol - UPC Barcelona 2018
Multilayer Perceptron - Elisa Sayrol - UPC Barcelona 2018Universitat Politècnica de Catalunya
 
Convolutional Neural Networks (DLAI D5L1 2017 UPC Deep Learning for Artificia...
Convolutional Neural Networks (DLAI D5L1 2017 UPC Deep Learning for Artificia...Convolutional Neural Networks (DLAI D5L1 2017 UPC Deep Learning for Artificia...
Convolutional Neural Networks (DLAI D5L1 2017 UPC Deep Learning for Artificia...Universitat Politècnica de Catalunya
 
Variational Autoencoders VAE - Santiago Pascual - UPC Barcelona 2018
Variational Autoencoders VAE - Santiago Pascual - UPC Barcelona 2018Variational Autoencoders VAE - Santiago Pascual - UPC Barcelona 2018
Variational Autoencoders VAE - Santiago Pascual - UPC Barcelona 2018Universitat Politècnica de Catalunya
 
Artificial Neural Network
Artificial Neural NetworkArtificial Neural Network
Artificial Neural NetworkPratik Aggarwal
 

Recommended

The Perceptron (D1L1 Insight@DCU Machine Learning Workshop 2017)
The Perceptron (D1L1 Insight@DCU Machine Learning Workshop 2017)The Perceptron (D1L1 Insight@DCU Machine Learning Workshop 2017)
The Perceptron (D1L1 Insight@DCU Machine Learning Workshop 2017)Universitat Politècnica de Catalunya
 
Convolutional Neural Networks - Veronica Vilaplana - UPC Barcelona 2018
Convolutional Neural Networks - Veronica Vilaplana - UPC Barcelona 2018Convolutional Neural Networks - Veronica Vilaplana - UPC Barcelona 2018
Convolutional Neural Networks - Veronica Vilaplana - UPC Barcelona 2018Universitat Politècnica de Catalunya
 
FUNCTION APPROXIMATION
FUNCTION APPROXIMATIONFUNCTION APPROXIMATION
FUNCTION APPROXIMATIONankita pandey
 
Convolutional Neural Networks - Xavier Giro - UPC TelecomBCN Barcelona 2020
Convolutional Neural Networks - Xavier Giro - UPC TelecomBCN Barcelona 2020Convolutional Neural Networks - Xavier Giro - UPC TelecomBCN Barcelona 2020
Convolutional Neural Networks - Xavier Giro - UPC TelecomBCN Barcelona 2020Universitat Politècnica de Catalunya
 
Multilayer Perceptron - Elisa Sayrol - UPC Barcelona 2018
Multilayer Perceptron - Elisa Sayrol - UPC Barcelona 2018Multilayer Perceptron - Elisa Sayrol - UPC Barcelona 2018
Multilayer Perceptron - Elisa Sayrol - UPC Barcelona 2018Universitat Politècnica de Catalunya
 
Convolutional Neural Networks (DLAI D5L1 2017 UPC Deep Learning for Artificia...
Convolutional Neural Networks (DLAI D5L1 2017 UPC Deep Learning for Artificia...Convolutional Neural Networks (DLAI D5L1 2017 UPC Deep Learning for Artificia...
Convolutional Neural Networks (DLAI D5L1 2017 UPC Deep Learning for Artificia...Universitat Politècnica de Catalunya
 
Variational Autoencoders VAE - Santiago Pascual - UPC Barcelona 2018
Variational Autoencoders VAE - Santiago Pascual - UPC Barcelona 2018Variational Autoencoders VAE - Santiago Pascual - UPC Barcelona 2018
Variational Autoencoders VAE - Santiago Pascual - UPC Barcelona 2018Universitat Politècnica de Catalunya
 
Artificial Neural Network
Artificial Neural NetworkArtificial Neural Network
Artificial Neural NetworkPratik Aggarwal
 
Convolution Neural Networks
Convolution Neural NetworksConvolution Neural Networks
Convolution Neural NetworksAhmedMahany
 
Soft Computering Technics - Unit2
Soft Computering Technics - Unit2Soft Computering Technics - Unit2
Soft Computering Technics - Unit2sravanthi computers
 
(Artificial) Neural Network
(Artificial) Neural Network(Artificial) Neural Network
(Artificial) Neural NetworkPutri Wikie
 
Convolutional Neural Networks (D1L3 2017 UPC Deep Learning for Computer Vision)
Convolutional Neural Networks (D1L3 2017 UPC Deep Learning for Computer Vision)Convolutional Neural Networks (D1L3 2017 UPC Deep Learning for Computer Vision)
Convolutional Neural Networks (D1L3 2017 UPC Deep Learning for Computer Vision)Universitat Politècnica de Catalunya
 
Artificial Neuron network
Artificial Neuron network Artificial Neuron network
Artificial Neuron network Smruti Ranjan Sahoo
 
Nn 1light
Nn 1lightNn 1light
Nn 1lightDegeneration Deseases
 
Neural networks
Neural networksNeural networks
Neural networksSlideshare
 
Dr. kiani artificial neural network lecture 1
Dr. kiani artificial neural network lecture 1Dr. kiani artificial neural network lecture 1
Dr. kiani artificial neural network lecture 1Parinaz Faraji
 
Neural Networks
Neural NetworksNeural Networks
Neural NetworksR A Akerkar
 
Neural network 20161210_jintaekseo
Neural network 20161210_jintaekseoNeural network 20161210_jintaekseo
Neural network 20161210_jintaekseoJinTaek Seo
 
Artificial Neural Network
Artificial Neural Network Artificial Neural Network
Artificial Neural Network Iman Ardekani
 
Backpropagation - Elisa Sayrol - UPC Barcelona 2018
Backpropagation - Elisa Sayrol - UPC Barcelona 2018Backpropagation - Elisa Sayrol - UPC Barcelona 2018
Backpropagation - Elisa Sayrol - UPC Barcelona 2018Universitat Politècnica de Catalunya
 
Multilayer Perceptron (DLAI D1L2 2017 UPC Deep Learning for Artificial Intell...
Multilayer Perceptron (DLAI D1L2 2017 UPC Deep Learning for Artificial Intell...Multilayer Perceptron (DLAI D1L2 2017 UPC Deep Learning for Artificial Intell...
Multilayer Perceptron (DLAI D1L2 2017 UPC Deep Learning for Artificial Intell...Universitat Politècnica de Catalunya
 
Attention Models (D3L6 2017 UPC Deep Learning for Computer Vision)
Attention Models (D3L6 2017 UPC Deep Learning for Computer Vision)Attention Models (D3L6 2017 UPC Deep Learning for Computer Vision)
Attention Models (D3L6 2017 UPC Deep Learning for Computer Vision)Universitat Politècnica de Catalunya
 
Loss Functions for Deep Learning - Javier Ruiz Hidalgo - UPC Barcelona 2018
Loss Functions for Deep Learning - Javier Ruiz Hidalgo - UPC Barcelona 2018Loss Functions for Deep Learning - Javier Ruiz Hidalgo - UPC Barcelona 2018
Loss Functions for Deep Learning - Javier Ruiz Hidalgo - UPC Barcelona 2018Universitat Politècnica de Catalunya
 
Neural network
Neural networkNeural network
Neural networkSilicon
 
The Perceptron - Xavier Giro-i-Nieto - UPC Barcelona 2018
The Perceptron - Xavier Giro-i-Nieto - UPC Barcelona 2018The Perceptron - Xavier Giro-i-Nieto - UPC Barcelona 2018
The Perceptron - Xavier Giro-i-Nieto - UPC Barcelona 2018Universitat Politècnica de Catalunya
 
Multilayer perceptron
Multilayer perceptronMultilayer perceptron
Multilayer perceptronsmitamm
 
Artificial neural networks
Artificial neural networks Artificial neural networks
Artificial neural networks Institute of Technology Telkom
 
Neural network
Neural networkNeural network
Neural networkFacebook
 
A TWO-LAYER RECURRENT NEURAL NETWORK FOR NONSMOOTH CONVEX OPTIMIZATION PROBLEMS
A TWO-LAYER RECURRENT NEURAL NETWORK FOR NONSMOOTH CONVEX OPTIMIZATION PROBLEMSA TWO-LAYER RECURRENT NEURAL NETWORK FOR NONSMOOTH CONVEX OPTIMIZATION PROBLEMS
A TWO-LAYER RECURRENT NEURAL NETWORK FOR NONSMOOTH CONVEX OPTIMIZATION PROBLEMSI3E Technologies
 
Master Defense Slides (translated)
Master Defense Slides (translated)Master Defense Slides (translated)
Master Defense Slides (translated)Francis Piéraut
 

More Related Content

What's hot

Convolution Neural Networks
Convolution Neural NetworksConvolution Neural Networks
Convolution Neural NetworksAhmedMahany
 
Soft Computering Technics - Unit2
Soft Computering Technics - Unit2Soft Computering Technics - Unit2
Soft Computering Technics - Unit2sravanthi computers
 
(Artificial) Neural Network
(Artificial) Neural Network(Artificial) Neural Network
(Artificial) Neural NetworkPutri Wikie
 
Convolutional Neural Networks (D1L3 2017 UPC Deep Learning for Computer Vision)
Convolutional Neural Networks (D1L3 2017 UPC Deep Learning for Computer Vision)Convolutional Neural Networks (D1L3 2017 UPC Deep Learning for Computer Vision)
Convolutional Neural Networks (D1L3 2017 UPC Deep Learning for Computer Vision)Universitat Politècnica de Catalunya
 
Neural networks
Neural networksNeural networks
Neural networksSlideshare
 
Dr. kiani artificial neural network lecture 1
Dr. kiani artificial neural network lecture 1Dr. kiani artificial neural network lecture 1
Dr. kiani artificial neural network lecture 1Parinaz Faraji
 
Neural network 20161210_jintaekseo
Neural network 20161210_jintaekseoNeural network 20161210_jintaekseo
Neural network 20161210_jintaekseoJinTaek Seo
 
Artificial Neural Network
Artificial Neural Network Artificial Neural Network
Artificial Neural Network Iman Ardekani
 
Multilayer Perceptron (DLAI D1L2 2017 UPC Deep Learning for Artificial Intell...
Multilayer Perceptron (DLAI D1L2 2017 UPC Deep Learning for Artificial Intell...Multilayer Perceptron (DLAI D1L2 2017 UPC Deep Learning for Artificial Intell...
Multilayer Perceptron (DLAI D1L2 2017 UPC Deep Learning for Artificial Intell...Universitat Politècnica de Catalunya
 
Loss Functions for Deep Learning - Javier Ruiz Hidalgo - UPC Barcelona 2018
Loss Functions for Deep Learning - Javier Ruiz Hidalgo - UPC Barcelona 2018Loss Functions for Deep Learning - Javier Ruiz Hidalgo - UPC Barcelona 2018
Loss Functions for Deep Learning - Javier Ruiz Hidalgo - UPC Barcelona 2018Universitat Politècnica de Catalunya
 
Neural network
Neural networkNeural network
Neural networkSilicon
 
Multilayer perceptron
Multilayer perceptronMultilayer perceptron
Multilayer perceptronsmitamm
 
Neural network
Neural networkNeural network
Neural networkFacebook
 

What's hot (20)

Convolution Neural Networks
Convolution Neural NetworksConvolution Neural Networks
Convolution Neural Networks
 
Soft Computering Technics - Unit2
Soft Computering Technics - Unit2Soft Computering Technics - Unit2
Soft Computering Technics - Unit2
 
(Artificial) Neural Network
(Artificial) Neural Network(Artificial) Neural Network
(Artificial) Neural Network
 
Convolutional Neural Networks (D1L3 2017 UPC Deep Learning for Computer Vision)
Convolutional Neural Networks (D1L3 2017 UPC Deep Learning for Computer Vision)Convolutional Neural Networks (D1L3 2017 UPC Deep Learning for Computer Vision)
Convolutional Neural Networks (D1L3 2017 UPC Deep Learning for Computer Vision)
 
Artificial Neuron network
Artificial Neuron network Artificial Neuron network
Artificial Neuron network
 
Nn 1light
Nn 1lightNn 1light
Nn 1light
 
Neural networks
Neural networksNeural networks
Neural networks
 
Dr. kiani artificial neural network lecture 1
Dr. kiani artificial neural network lecture 1Dr. kiani artificial neural network lecture 1
Dr. kiani artificial neural network lecture 1
 
Neural Networks
Neural NetworksNeural Networks
Neural Networks
 
Neural network 20161210_jintaekseo
Neural network 20161210_jintaekseoNeural network 20161210_jintaekseo
Neural network 20161210_jintaekseo
 
Artificial Neural Network
Artificial Neural Network Artificial Neural Network
Artificial Neural Network
 
Backpropagation - Elisa Sayrol - UPC Barcelona 2018
Backpropagation - Elisa Sayrol - UPC Barcelona 2018Backpropagation - Elisa Sayrol - UPC Barcelona 2018
Backpropagation - Elisa Sayrol - UPC Barcelona 2018
 
Multilayer Perceptron (DLAI D1L2 2017 UPC Deep Learning for Artificial Intell...
Multilayer Perceptron (DLAI D1L2 2017 UPC Deep Learning for Artificial Intell...Multilayer Perceptron (DLAI D1L2 2017 UPC Deep Learning for Artificial Intell...
Multilayer Perceptron (DLAI D1L2 2017 UPC Deep Learning for Artificial Intell...
 
Attention Models (D3L6 2017 UPC Deep Learning for Computer Vision)
Attention Models (D3L6 2017 UPC Deep Learning for Computer Vision)Attention Models (D3L6 2017 UPC Deep Learning for Computer Vision)
Attention Models (D3L6 2017 UPC Deep Learning for Computer Vision)
 
Loss Functions for Deep Learning - Javier Ruiz Hidalgo - UPC Barcelona 2018
Loss Functions for Deep Learning - Javier Ruiz Hidalgo - UPC Barcelona 2018Loss Functions for Deep Learning - Javier Ruiz Hidalgo - UPC Barcelona 2018
Loss Functions for Deep Learning - Javier Ruiz Hidalgo - UPC Barcelona 2018
 
Neural network
Neural networkNeural network
Neural network
 
The Perceptron - Xavier Giro-i-Nieto - UPC Barcelona 2018
The Perceptron - Xavier Giro-i-Nieto - UPC Barcelona 2018The Perceptron - Xavier Giro-i-Nieto - UPC Barcelona 2018
The Perceptron - Xavier Giro-i-Nieto - UPC Barcelona 2018
 
Multilayer perceptron
Multilayer perceptronMultilayer perceptron
Multilayer perceptron
 
Artificial neural networks
Artificial neural networks Artificial neural networks
Artificial neural networks
 
Neural network
Neural networkNeural network
Neural network
 

Viewers also liked

A TWO-LAYER RECURRENT NEURAL NETWORK FOR NONSMOOTH CONVEX OPTIMIZATION PROBLEMS
A TWO-LAYER RECURRENT NEURAL NETWORK FOR NONSMOOTH CONVEX OPTIMIZATION PROBLEMSA TWO-LAYER RECURRENT NEURAL NETWORK FOR NONSMOOTH CONVEX OPTIMIZATION PROBLEMS
A TWO-LAYER RECURRENT NEURAL NETWORK FOR NONSMOOTH CONVEX OPTIMIZATION PROBLEMSI3E Technologies
 
Master Defense Slides (translated)
Master Defense Slides (translated)Master Defense Slides (translated)
Master Defense Slides (translated)Francis Piéraut
 
Introduction to Neural networks (under graduate course) Lecture 2 of 9
Introduction to Neural networks (under graduate course) Lecture 2 of 9Introduction to Neural networks (under graduate course) Lecture 2 of 9
Introduction to Neural networks (under graduate course) Lecture 2 of 9Randa Elanwar
 
Neural network & its applications
Neural network & its applications Neural network & its applications
Neural network & its applications Ahmed_hashmi
 
Neuron Mc Culloch Pitts dan Hebb
Neuron Mc Culloch Pitts dan HebbNeuron Mc Culloch Pitts dan Hebb
Neuron Mc Culloch Pitts dan HebbSherly Uda
 
Solving Simple Problems With Neural Networks presented by Mark Nguyen.
Solving Simple Problems With Neural Networks presented by Mark Nguyen.Solving Simple Problems With Neural Networks presented by Mark Nguyen.
Solving Simple Problems With Neural Networks presented by Mark Nguyen.Brian Curry
 
Introduction Of Artificial neural network
Introduction Of Artificial neural networkIntroduction Of Artificial neural network
Introduction Of Artificial neural networkNagarajan
 
neural network
neural networkneural network
neural networkSTUDENT
 
Artificial neural network
Artificial neural networkArtificial neural network
Artificial neural networkDEEPASHRI HK
 
Hand Written Character Recognition Using Neural Networks
Hand Written Character Recognition Using Neural Networks Hand Written Character Recognition Using Neural Networks
Hand Written Character Recognition Using Neural Networks Chiranjeevi Adi
 
Data Science - Part VIII - Artifical Neural Network
Data Science - Part VIII - Artifical Neural NetworkData Science - Part VIII - Artifical Neural Network
Data Science - Part VIII - Artifical Neural NetworkDerek Kane
 

Viewers also liked (11)

A TWO-LAYER RECURRENT NEURAL NETWORK FOR NONSMOOTH CONVEX OPTIMIZATION PROBLEMS
A TWO-LAYER RECURRENT NEURAL NETWORK FOR NONSMOOTH CONVEX OPTIMIZATION PROBLEMSA TWO-LAYER RECURRENT NEURAL NETWORK FOR NONSMOOTH CONVEX OPTIMIZATION PROBLEMS
A TWO-LAYER RECURRENT NEURAL NETWORK FOR NONSMOOTH CONVEX OPTIMIZATION PROBLEMS
 
Master Defense Slides (translated)
Master Defense Slides (translated)Master Defense Slides (translated)
Master Defense Slides (translated)
 
Introduction to Neural networks (under graduate course) Lecture 2 of 9
Introduction to Neural networks (under graduate course) Lecture 2 of 9Introduction to Neural networks (under graduate course) Lecture 2 of 9
Introduction to Neural networks (under graduate course) Lecture 2 of 9
 
Neural network & its applications
Neural network & its applications Neural network & its applications
Neural network & its applications
 
Neuron Mc Culloch Pitts dan Hebb
Neuron Mc Culloch Pitts dan HebbNeuron Mc Culloch Pitts dan Hebb
Neuron Mc Culloch Pitts dan Hebb
 
Solving Simple Problems With Neural Networks presented by Mark Nguyen.
Solving Simple Problems With Neural Networks presented by Mark Nguyen.Solving Simple Problems With Neural Networks presented by Mark Nguyen.
Solving Simple Problems With Neural Networks presented by Mark Nguyen.
 
Introduction Of Artificial neural network
Introduction Of Artificial neural networkIntroduction Of Artificial neural network
Introduction Of Artificial neural network
 
neural network
neural networkneural network
neural network
 
Artificial neural network
Artificial neural networkArtificial neural network
Artificial neural network
 
Hand Written Character Recognition Using Neural Networks
Hand Written Character Recognition Using Neural Networks Hand Written Character Recognition Using Neural Networks
Hand Written Character Recognition Using Neural Networks
 
Data Science - Part VIII - Artifical Neural Network
Data Science - Part VIII - Artifical Neural NetworkData Science - Part VIII - Artifical Neural Network
Data Science - Part VIII - Artifical Neural Network
 

Similar to Introductions to Neural Networks,Basic concepts

Convolutional Neural Network (CNN) - image recognition
Convolutional Neural Network (CNN) - image recognitionConvolutional Neural Network (CNN) - image recognition
Convolutional Neural Network (CNN) - image recognitionYUNG-KUEI CHEN
 
Pres Tesi LM-2016+transcript_eng
Pres Tesi LM-2016+transcript_engPres Tesi LM-2016+transcript_eng
Pres Tesi LM-2016+transcript_engDaniele Ciriello
 
Deep Learning - 인공지능 기계학습의 새로운 트랜드 :김인중
Deep Learning - 인공지능 기계학습의 새로운 트랜드 :김인중Deep Learning - 인공지능 기계학습의 새로운 트랜드 :김인중
Deep Learning - 인공지능 기계학습의 새로운 트랜드 :김인중datasciencekorea
 
Deep Neural Networks (D1L2 Insight@DCU Machine Learning Workshop 2017)
Deep Neural Networks (D1L2 Insight@DCU Machine Learning Workshop 2017)Deep Neural Networks (D1L2 Insight@DCU Machine Learning Workshop 2017)
Deep Neural Networks (D1L2 Insight@DCU Machine Learning Workshop 2017)Universitat Politècnica de Catalunya
 
Minimum Complexity Decoupling Networks for Arbitrary Coupled Loads
Minimum Complexity Decoupling Networks for Arbitrary Coupled LoadsMinimum Complexity Decoupling Networks for Arbitrary Coupled Loads
Minimum Complexity Decoupling Networks for Arbitrary Coupled LoadsDing Nie
 
AILABS - Lecture Series - Is AI the New Electricity? Topic:- Classification a...
AILABS - Lecture Series - Is AI the New Electricity? Topic:- Classification a...AILABS - Lecture Series - Is AI the New Electricity? Topic:- Classification a...
AILABS - Lecture Series - Is AI the New Electricity? Topic:- Classification a...AILABS Academy
 
[系列活動] 一日搞懂生成式對抗網路
[系列活動] 一日搞懂生成式對抗網路[系列活動] 一日搞懂生成式對抗網路
[系列活動] 一日搞懂生成式對抗網路台灣資料科學年會
 
1-pytorch-CNN-RNN.pdf
1-pytorch-CNN-RNN.pdf1-pytorch-CNN-RNN.pdf
1-pytorch-CNN-RNN.pdfAndrey63387
 
Deep Learning and TensorFlow
Deep Learning and TensorFlowDeep Learning and TensorFlow
Deep Learning and TensorFlowOswald Campesato
 
Classification using perceptron.pptx
Classification using perceptron.pptxClassification using perceptron.pptx
Classification using perceptron.pptxsomeyamohsen3
 
NNFL 3 - Guru Nanak Dev Engineering College
NNFL 3 - Guru Nanak Dev Engineering CollegeNNFL 3 - Guru Nanak Dev Engineering College
NNFL 3 - Guru Nanak Dev Engineering CollegeMR. VIKRAM SNEHI
 
DEEPLEARNING recurrent neural networs.pdf
DEEPLEARNING recurrent neural networs.pdfDEEPLEARNING recurrent neural networs.pdf
DEEPLEARNING recurrent neural networs.pdfAamirMaqsood8
 
neural network non-linear hypothesis.pdf
neural network non-linear hypothesis.pdfneural network non-linear hypothesis.pdf
neural network non-linear hypothesis.pdfSamikshaGoel10
 
Convolution Neural Network Lecture Slides
Convolution Neural Network Lecture SlidesConvolution Neural Network Lecture Slides
Convolution Neural Network Lecture SlidesAdnanHaider234505
 

Similar to Introductions to Neural Networks,Basic concepts (20)

Convolutional Neural Network (CNN) - image recognition
Convolutional Neural Network (CNN) - image recognitionConvolutional Neural Network (CNN) - image recognition
Convolutional Neural Network (CNN) - image recognition
 
Pres Tesi LM-2016+transcript_eng
Pres Tesi LM-2016+transcript_engPres Tesi LM-2016+transcript_eng
Pres Tesi LM-2016+transcript_eng
 
Deep Learning - 인공지능 기계학습의 새로운 트랜드 :김인중
Deep Learning - 인공지능 기계학습의 새로운 트랜드 :김인중Deep Learning - 인공지능 기계학습의 새로운 트랜드 :김인중
Deep Learning - 인공지능 기계학습의 새로운 트랜드 :김인중
 
Deep Neural Networks (D1L2 Insight@DCU Machine Learning Workshop 2017)
Deep Neural Networks (D1L2 Insight@DCU Machine Learning Workshop 2017)Deep Neural Networks (D1L2 Insight@DCU Machine Learning Workshop 2017)
Deep Neural Networks (D1L2 Insight@DCU Machine Learning Workshop 2017)
 
Minimum Complexity Decoupling Networks for Arbitrary Coupled Loads
Minimum Complexity Decoupling Networks for Arbitrary Coupled LoadsMinimum Complexity Decoupling Networks for Arbitrary Coupled Loads
Minimum Complexity Decoupling Networks for Arbitrary Coupled Loads
 
ai7.ppt
ai7.pptai7.ppt
ai7.ppt
 
AILABS - Lecture Series - Is AI the New Electricity? Topic:- Classification a...
AILABS - Lecture Series - Is AI the New Electricity? Topic:- Classification a...AILABS - Lecture Series - Is AI the New Electricity? Topic:- Classification a...
AILABS - Lecture Series - Is AI the New Electricity? Topic:- Classification a...
 
[系列活動] 一日搞懂生成式對抗網路
[系列活動] 一日搞懂生成式對抗網路[系列活動] 一日搞懂生成式對抗網路
[系列活動] 一日搞懂生成式對抗網路
 
1-pytorch-CNN-RNN.pdf
1-pytorch-CNN-RNN.pdf1-pytorch-CNN-RNN.pdf
1-pytorch-CNN-RNN.pdf
 
Deep Learning and TensorFlow
Deep Learning and TensorFlowDeep Learning and TensorFlow
Deep Learning and TensorFlow
 
Classification using perceptron.pptx
Classification using perceptron.pptxClassification using perceptron.pptx
Classification using perceptron.pptx
 
Deep Learning for Computer Vision: Deep Networks (UPC 2016)
Deep Learning for Computer Vision: Deep Networks (UPC 2016)Deep Learning for Computer Vision: Deep Networks (UPC 2016)
Deep Learning for Computer Vision: Deep Networks (UPC 2016)
 
ai7.ppt
ai7.pptai7.ppt
ai7.ppt
 
NNFL 3 - Guru Nanak Dev Engineering College
NNFL 3 - Guru Nanak Dev Engineering CollegeNNFL 3 - Guru Nanak Dev Engineering College
NNFL 3 - Guru Nanak Dev Engineering College
 
DEEPLEARNING recurrent neural networs.pdf
DEEPLEARNING recurrent neural networs.pdfDEEPLEARNING recurrent neural networs.pdf
DEEPLEARNING recurrent neural networs.pdf
 
ANNs.pdf
ANNs.pdfANNs.pdf
ANNs.pdf
 
neural network non-linear hypothesis.pdf
neural network non-linear hypothesis.pdfneural network non-linear hypothesis.pdf
neural network non-linear hypothesis.pdf
 
MNN
MNNMNN
MNN
 
Convolution Neural Network Lecture Slides
Convolution Neural Network Lecture SlidesConvolution Neural Network Lecture Slides
Convolution Neural Network Lecture Slides
 
Review_Cibe Sridharan
Review_Cibe SridharanReview_Cibe Sridharan
Review_Cibe Sridharan
 

Recently uploaded

Designing IA for AI - Information Architecture Conference 2024
Designing IA for AI - Information Architecture Conference 2024Designing IA for AI - Information Architecture Conference 2024
Designing IA for AI - Information Architecture Conference 2024Enterprise Knowledge
 
Anypoint Exchange: It’s Not Just a Repo!
Anypoint Exchange: It’s Not Just a Repo!Anypoint Exchange: It’s Not Just a Repo!
Anypoint Exchange: It’s Not Just a Repo!Manik S Magar
 
Leverage Zilliz Serverless - Up to 50X Saving for Your Vector Storage Cost
Leverage Zilliz Serverless - Up to 50X Saving for Your Vector Storage CostLeverage Zilliz Serverless - Up to 50X Saving for Your Vector Storage Cost
Leverage Zilliz Serverless - Up to 50X Saving for Your Vector Storage CostZilliz
 
"ML in Production",Oleksandr Bagan
"ML in Production",Oleksandr Bagan"ML in Production",Oleksandr Bagan
"ML in Production",Oleksandr BaganFwdays
 
How to write a Business Continuity Plan
How to write a Business Continuity PlanHow to write a Business Continuity Plan
How to write a Business Continuity PlanDatabarracks
 
Scanning the Internet for External Cloud Exposures via SSL Certs
Scanning the Internet for External Cloud Exposures via SSL CertsScanning the Internet for External Cloud Exposures via SSL Certs
Scanning the Internet for External Cloud Exposures via SSL CertsRizwan Syed
 
Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024BookNet Canada
 
TrustArc Webinar - How to Build Consumer Trust Through Data Privacy
TrustArc Webinar - How to Build Consumer Trust Through Data PrivacyTrustArc Webinar - How to Build Consumer Trust Through Data Privacy
TrustArc Webinar - How to Build Consumer Trust Through Data PrivacyTrustArc
 
Dev Dives: Streamline document processing with UiPath Studio Web
Dev Dives: Streamline document processing with UiPath Studio WebDev Dives: Streamline document processing with UiPath Studio Web
Dev Dives: Streamline document processing with UiPath Studio WebUiPathCommunity
 
Take control of your SAP testing with UiPath Test Suite
Take control of your SAP testing with UiPath Test SuiteTake control of your SAP testing with UiPath Test Suite
Take control of your SAP testing with UiPath Test SuiteDianaGray10
 
What's New in Teams Calling, Meetings and Devices March 2024
What's New in Teams Calling, Meetings and Devices March 2024What's New in Teams Calling, Meetings and Devices March 2024
What's New in Teams Calling, Meetings and Devices March 2024Stephanie Beckett
 
Unleash Your Potential - Namagunga Girls Coding Club
Unleash Your Potential - Namagunga Girls Coding ClubUnleash Your Potential - Namagunga Girls Coding Club
Unleash Your Potential - Namagunga Girls Coding ClubKalema Edgar
 
Artificial intelligence in cctv survelliance.pptx
Artificial intelligence in cctv survelliance.pptxArtificial intelligence in cctv survelliance.pptx
Artificial intelligence in cctv survelliance.pptxhariprasad279825
 
"LLMs for Python Engineers: Advanced Data Analysis and Semantic Kernel",Oleks...
"LLMs for Python Engineers: Advanced Data Analysis and Semantic Kernel",Oleks..."LLMs for Python Engineers: Advanced Data Analysis and Semantic Kernel",Oleks...
"LLMs for Python Engineers: Advanced Data Analysis and Semantic Kernel",Oleks...Fwdays
 
From Family Reminiscence to Scholarly Archive .
From Family Reminiscence to Scholarly Archive .From Family Reminiscence to Scholarly Archive .
From Family Reminiscence to Scholarly Archive .Alan Dix
 
Unraveling Multimodality with Large Language Models.pdf
Unraveling Multimodality with Large Language Models.pdfUnraveling Multimodality with Large Language Models.pdf
Unraveling Multimodality with Large Language Models.pdfAlex Barbosa Coqueiro
 
SIP trunking in Janus @ Kamailio World 2024
SIP trunking in Janus @ Kamailio World 2024SIP trunking in Janus @ Kamailio World 2024
SIP trunking in Janus @ Kamailio World 2024Lorenzo Miniero
 
Hyperautomation and AI/ML: A Strategy for Digital Transformation Success.pdf
Hyperautomation and AI/ML: A Strategy for Digital Transformation Success.pdfHyperautomation and AI/ML: A Strategy for Digital Transformation Success.pdf
Hyperautomation and AI/ML: A Strategy for Digital Transformation Success.pdfPrecisely
 
Tampa BSides - Chef's Tour of Microsoft Security Adoption Framework (SAF)
Tampa BSides - Chef's Tour of Microsoft Security Adoption Framework (SAF)Tampa BSides - Chef's Tour of Microsoft Security Adoption Framework (SAF)
Tampa BSides - Chef's Tour of Microsoft Security Adoption Framework (SAF)Mark Simos
 

Recently uploaded (20)

Designing IA for AI - Information Architecture Conference 2024
Designing IA for AI - Information Architecture Conference 2024Designing IA for AI - Information Architecture Conference 2024
Designing IA for AI - Information Architecture Conference 2024
 
Anypoint Exchange: It’s Not Just a Repo!
Anypoint Exchange: It’s Not Just a Repo!Anypoint Exchange: It’s Not Just a Repo!
Anypoint Exchange: It’s Not Just a Repo!
 
Leverage Zilliz Serverless - Up to 50X Saving for Your Vector Storage Cost
Leverage Zilliz Serverless - Up to 50X Saving for Your Vector Storage CostLeverage Zilliz Serverless - Up to 50X Saving for Your Vector Storage Cost
Leverage Zilliz Serverless - Up to 50X Saving for Your Vector Storage Cost
 
"ML in Production",Oleksandr Bagan
"ML in Production",Oleksandr Bagan"ML in Production",Oleksandr Bagan
"ML in Production",Oleksandr Bagan
 
How to write a Business Continuity Plan
How to write a Business Continuity PlanHow to write a Business Continuity Plan
How to write a Business Continuity Plan
 
Scanning the Internet for External Cloud Exposures via SSL Certs
Scanning the Internet for External Cloud Exposures via SSL CertsScanning the Internet for External Cloud Exposures via SSL Certs
Scanning the Internet for External Cloud Exposures via SSL Certs
 
Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
 
E-Vehicle_Hacking_by_Parul Sharma_null_owasp.pptx
E-Vehicle_Hacking_by_Parul Sharma_null_owasp.pptxE-Vehicle_Hacking_by_Parul Sharma_null_owasp.pptx
E-Vehicle_Hacking_by_Parul Sharma_null_owasp.pptx
 
TrustArc Webinar - How to Build Consumer Trust Through Data Privacy
TrustArc Webinar - How to Build Consumer Trust Through Data PrivacyTrustArc Webinar - How to Build Consumer Trust Through Data Privacy
TrustArc Webinar - How to Build Consumer Trust Through Data Privacy
 
Dev Dives: Streamline document processing with UiPath Studio Web
Dev Dives: Streamline document processing with UiPath Studio WebDev Dives: Streamline document processing with UiPath Studio Web
Dev Dives: Streamline document processing with UiPath Studio Web
 
Take control of your SAP testing with UiPath Test Suite
Take control of your SAP testing with UiPath Test SuiteTake control of your SAP testing with UiPath Test Suite
Take control of your SAP testing with UiPath Test Suite
 
What's New in Teams Calling, Meetings and Devices March 2024
What's New in Teams Calling, Meetings and Devices March 2024What's New in Teams Calling, Meetings and Devices March 2024
What's New in Teams Calling, Meetings and Devices March 2024
 
Unleash Your Potential - Namagunga Girls Coding Club
Unleash Your Potential - Namagunga Girls Coding ClubUnleash Your Potential - Namagunga Girls Coding Club
Unleash Your Potential - Namagunga Girls Coding Club
 
Artificial intelligence in cctv survelliance.pptx
Artificial intelligence in cctv survelliance.pptxArtificial intelligence in cctv survelliance.pptx
Artificial intelligence in cctv survelliance.pptx
 
"LLMs for Python Engineers: Advanced Data Analysis and Semantic Kernel",Oleks...
"LLMs for Python Engineers: Advanced Data Analysis and Semantic Kernel",Oleks..."LLMs for Python Engineers: Advanced Data Analysis and Semantic Kernel",Oleks...
"LLMs for Python Engineers: Advanced Data Analysis and Semantic Kernel",Oleks...
 
From Family Reminiscence to Scholarly Archive .
From Family Reminiscence to Scholarly Archive .From Family Reminiscence to Scholarly Archive .
From Family Reminiscence to Scholarly Archive .
 
Unraveling Multimodality with Large Language Models.pdf
Unraveling Multimodality with Large Language Models.pdfUnraveling Multimodality with Large Language Models.pdf
Unraveling Multimodality with Large Language Models.pdf
 
SIP trunking in Janus @ Kamailio World 2024
SIP trunking in Janus @ Kamailio World 2024SIP trunking in Janus @ Kamailio World 2024
SIP trunking in Janus @ Kamailio World 2024
 
Hyperautomation and AI/ML: A Strategy for Digital Transformation Success.pdf
Hyperautomation and AI/ML: A Strategy for Digital Transformation Success.pdfHyperautomation and AI/ML: A Strategy for Digital Transformation Success.pdf
Hyperautomation and AI/ML: A Strategy for Digital Transformation Success.pdf
 
Tampa BSides - Chef's Tour of Microsoft Security Adoption Framework (SAF)
Tampa BSides - Chef's Tour of Microsoft Security Adoption Framework (SAF)Tampa BSides - Chef's Tour of Microsoft Security Adoption Framework (SAF)
Tampa BSides - Chef's Tour of Microsoft Security Adoption Framework (SAF)
 

Introductions to Neural Networks,Basic concepts

  • 1. Introductions to Neural Networks Basic concepts Jian QIN Intro Single-Layer Introductions to Neural Networks Basic concepts Multilayer Applications Next Jian QIN December 23, 2013 . . . . . .
  • 2. Introductions to Neural Networks Basic concepts Outline Jian QIN Intro Intro Single-Layer Multilayer Applications Single-Layer Next Multilayer Applications Next . . . . . .
  • 3. Introductions to Neural Networks Basic concepts Show Jian QIN Intro Single-Layer Multilayer Applications Next ./asamples.gif Get more from http://yann.lecun.com/exdb/lenet/index.html . . . . . .
  • 4. Introductions to Neural Networks Basic concepts Show cont. Jian QIN Intro Single-Layer Multilayer Applications Next Source: Learning Internal Representations by Error Propagation . . . . . .
  • 5. Introductions to Neural Networks Basic concepts Neuron Jian QIN Intro Single-Layer Multilayer Applications Next ▶ Input Vector: X = [x0 = 1, x1 , x2 , x3 …] ▶ Weights: W = [w0 = b, w1 , w2 , w3 …] ▶ Sum: X*WT ▶ Activation Function … . . . . . .
  • 6. Introductions to Neural Networks Basic concepts Activation Function Jian QIN Intro Single-Layer ▶ Threshold Multilayer Applications 1 2 3 4 5 if < threshold then 0 else 1 ▶ Next Sigmoid 1 1. y = 1+e−ax 2. y = tanh(x) . . . . . .
  • 7. Introductions to Neural Networks Basic concepts Perceptrons Jian QIN Intro ▶ ▶ Single-Layer Can perform pattern classification only on linearly separable patterns. Multilayer Applications XOR Problem Next a 1 0 1 0 b 1 1 0 0 a XOR b 0 1 1 0 Critique from Minsky and Selfridge . . . . . .
  • 8. Introductions to Neural Networks Basic concepts Perceptrons ▶ On-Line learning (One by One) Jian QIN Intro w(0) = [0, 0, 0...] Single-Layer w(n + 1) = w(n) + η [d(n) − y(n)] x(n) ▶ Multilayer Applications Batch Learning where E is the set of misclassifed x perceptron cost function J(w) = ∑ Next (−wT x) x∈E gradient vector J(w) = ∑ (−x) x∈E algorithm w(n + 1) = w(n) − η J(w) ∑ w(n + 1) = w(n) − η x . . x∈E . . . .
  • 9. Introductions to Neural Networks Basic concepts SVM Jian QIN A kernel learning method on (sort of) single layer perceptron. Intro Single-Layer Multilayer Applications Next From: PDF materials of Neural Networks and Learning Machines . . . . . .
  • 10. Introductions to Neural Networks Basic concepts Multilayer Perceptrons Jian QIN Intro ▶ Input + Hidden Layer + Output Single-Layer Multilayer Applications Next http://www.seattlerobotics.org/encoder/ nov98/neural.html . . . . . .
  • 11. Introductions to Neural Networks Basic concepts Feed Forward Jian QIN Intro Single-Layer Multilayer Applications Next xlayer(N+1) = sgn(xlayer(N) ∗ Wlayer(N) ) . . . . . .
  • 12. Introductions to Neural Networks Basic concepts Back-Propagation Jian QIN Intro Single-Layer Multilayer Cost Function Applications 1 ∑∑ 2 = ej (n) N N ξav Next n=1 j∈C ej (n) = dj (n) − yj (n) http://galaxy.agh.edu.pl/~vlsi/AI/backp_t_en/ backprop.html . . . . . .
  • 13. Introductions to Neural Networks Basic concepts Word2Vec Jian QIN Intro Single-Layer Multilayer Applications Next Working on Java Version. 1/7 speed of original c version. Original Version: https://code.google.com/p/word2vec/ . . . . . .
  • 14. Introductions to Neural Networks Basic concepts matlab code Jian QIN Intro Single-Layer Multilayer Applications Next Hand-writing Image Processing . . . . . .
  • 15. Introductions to Neural Networks Basic concepts Personal Views Jian QIN Intro Single-Layer Multilayer Applications Next 1. Pattern Recognition: Image Processing. 2. Build Prior Information into NN: by hand. 3. Have no idea about the structure of the solution. . . . . . .
  • 16. Introductions to Neural Networks Basic concepts Next Jian QIN Intro Single-Layer Multilayer Applications Next 1. Recurrent NN 2. Applications 3. Neural Networks on NLP. . . . . . .
  • 17. Introductions to Neural Networks Basic concepts Links Jian QIN Intro Single-Layer Multilayer ▶ Machine Learning Applications Next https://www.coursera.org/course/ml ▶ Neural Networks for Machine Learning https://www.coursera.org/course/neuralnets ▶ Neural Networks and Learning Machines http://book.douban.com/subject/5952531/ . . . . . .