Neural networks. Chapter 20, Section 5 1


 Jennifer Warren
 1 years ago
 Views:
Transcription
1 Neural networks Chapter 20, Section 5 Chapter 20, Section 5
2 Outline Brains Neural networks Perceptrons Multilayer perceptrons Applications of neural networks Chapter 20, Section 5 2
3 Brains 0 neurons of > 20 types, 0 4 synapses, ms 0ms cycle time Signals are noisy spike trains of electrical potential Axonal arborization Synapse Axon from another cell Dendrite Axon Nucleus Synapses Cell body or Soma Chapter 20, Section 5 3
4 McCulloch Pitts unit Output is a squashed linear function of the inputs: a i g(in i ) = g ( Σ j W j,i a j ) a 0 = a i = g(in i ) Wj,i a j Bias Weight W 0,i Σ in i g a i Input Links Input Function Activation Function Output Output Links A gross oversimplification of real neurons, but its purpose is to develop understanding of what networks of simple units can do Chapter 20, Section 5 4
5 Activation functions g(in i ) g(in i ) + + (a) in i (b) in i (a) is a step function or threshold function (b) is a sigmoid function /( + e x ) Changing the bias weight W 0,i moves the threshold location Chapter 20, Section 5 5
6 Implementing logical functions W 0 =.5 W 0 = 0.5 W 0 = 0.5 W = W 2 = W = W 2 = W = AND OR NOT McCulloch and Pitts: every Boolean function can be implemented Chapter 20, Section 5 6
7 Feedforward networks: singlelayer perceptrons multilayer perceptrons Network structures Feedforward networks implement functions, have no internal state Recurrent networks: Hopfield networks have symmetric weights (W i,j = W j,i ) g(x) = sign(x), a i = ± ; holographic associative memory Boltzmann machines use stochastic activation functions, MCMC in Bayes nets recurrent neural nets have directed cycles with delays have internal state (like flipflops), can oscillate etc. Chapter 20, Section 5 7
8 Feedforward example W,3 W,4 3 W 3,5 5 2 W 2,3 W 2,4 4 W 4,5 Feedforward network = a parameterized family of nonlinear functions: a 5 = g(w 3,5 a 3 + W 4,5 a 4 ) = g(w 3,5 g(w,3 a + W 2,3 a 2 ) + W 4,5 g(w,4 a + W 2,4 a 2 )) Adjusting weights changes the function: do learning this way! Chapter 20, Section 5 8
9 Singlelayer perceptrons Input Units Perceptron output Output 0 x 2 W j,i Units 2 4 x 2 Output units all operate separately no shared weights Adjusting weights moves the location, orientation, and steepness of cliff Chapter 20, Section 5 9
10 Expressiveness of perceptrons Consider a perceptron with g = step function (Rosenblatt, 957, 960) Can represent AND, OR, NOT, majority, etc., but not XOR Represents a linear separator in input space: Σ j W j x j > 0 or W x > 0 x x x? 0 0 x x x 2 (a) x and x 2 (b) x or x2 (c) x xor x2 Minsky & Papert (969) pricked the neural network balloon Chapter 20, Section 5 0
11 Perceptron learning Learn by adjusting weights to reduce error on training set The squared error for an example with input x and true output y is E = 2 Err 2 2 (y h W(x)) 2, Perform optimization search by gradient descent: E W j = Err Err = Err W j W j = Err g (in) x j Simple weight update rule: W j W j + α Err g (in) x j ( y g(σ n j = 0W j x j ) ) E.g., +ve error increase network output increase weights on +ve inputs, decrease on ve inputs Chapter 20, Section 5
12 Perceptron learning contd. Perceptron learning rule converges to a consistent function for any linearly separable data set Proportion correct on test set Perceptron Decision tree Proportion correct on test set Perceptron Decision tree Training set size  MAJORITY on inputs Training set size  RESTAURANT data Perceptron learns majority function easily, DTL is hopeless DTL learns restaurant function easily, perceptron cannot represent it Chapter 20, Section 5 2
13 Multilayer perceptrons Layers are usually fully connected; numbers of hidden units typically chosen by hand Output units a i W j,i Hidden units a j W k,j Input units a k Chapter 20, Section 5 3
14 Expressiveness of MLPs All continuous functions w/ 2 layers, all functions w/ 3 layers h W (x, x 2 ) x x 2 h W (x, x 2 ) x x 2 Combine two oppositefacing threshold functions to make a ridge Combine two perpendicular ridges to make a bump Add bumps of various sizes and locations to fit any surface Proof requires exponentially many hidden units (cf DTL proof) Chapter 20, Section 5 4
15 Backpropagation learning Output layer: same as for singlelayer perceptron, W j,i W j,i + α a j i where i = Err i g (in i ) Hidden layer: backpropagate the error from the output layer: j = g (in j ) W j,i i. i Update rule for weights in hidden layer: W k,j W k,j + α a k j. (Most neuroscientists deny that backpropagation occurs in the brain) Chapter 20, Section 5 5
16 Backpropagation derivation The squared error on a single example is defined as E = 2 i (y i a i ) 2, where the sum is over the nodes in the output layer. E W j,i = (y i a i ) a i W j,i = (y i a i ) g(in i) W j,i = (y i a i )g (in i ) in i = (y i a i )g (in i ) W j,i = (y i a i )g (in i )a j = a j i W j,i j W j,ia j Chapter 20, Section 5 6
17 Backpropagation derivation contd. E W k,j = i (y i a i ) a i W k,j = i (y i a i ) g(in i) = (y i a i )g (in i ) in i = i W i k,j i W k,j W k,j = i iw j,i a j W k,j = i iw j,i g(in j ) W k,j = i iw j,i g (in j ) in j = i iw j,i g (in j ) W k,j W k,j k W k,j a k j W j,ia j = i iw j,i g (in j )a k = a k j Chapter 20, Section 5 7
18 Backpropagation learning contd. At each epoch, sum gradient updates for all examples and apply Training curve for 00 restaurant examples: finds exact fit 4 Total error on training set Number of epochs Typical problems: slow convergence, local minima Chapter 20, Section 5 8
19 Backpropagation learning contd. Learning curve for MLP with 4 hidden units: Proportion correct on test set Decision tree Multilayer network Training set size  RESTAURANT data MLPs are quite good for complex pattern recognition tasks, but resulting hypotheses cannot be understood easily Chapter 20, Section 5 9
20 Handwritten digit recognition 3nearestneighbor = 2.4% error unit MLP =.6% error LeNet: unit MLP = 0.9% error Current best (kernel machines, vision algorithms) 0.6% error Chapter 20, Section 5 20
21 Summary Most brains have lots of neurons; each neuron linear threshold unit (?) Perceptrons (onelayer networks) insufficiently expressive Multilayer networks are sufficiently expressive; can be trained by gradient descent, i.e., error backpropagation Many applications: speech, driving, handwriting, fraud detection, etc. Engineering, cognitive modelling, and neural system modelling subfields have largely diverged Chapter 20, Section 5 2
Neural Networks. Chapter 18, Section 7. TB Artificial Intelligence. Slides from AIMA 1/ 21
Neural Networks Chapter 8, Section 7 TB Artificial Intelligence Slides from AIMA http://aima.cs.berkeley.edu / 2 Outline Brains Neural networks Perceptrons Multilayer perceptrons Applications of neural
More informationNeural networks. Chapter 19, Sections 1 5 1
Neural networks Chapter 19, Sections 1 5 Chapter 19, Sections 1 5 1 Outline Brains Neural networks Perceptrons Multilayer perceptrons Applications of neural networks Chapter 19, Sections 1 5 2 Brains 10
More informationNeural networks. Chapter 20. Chapter 20 1
Neural networks Chapter 20 Chapter 20 1 Outline Brains Neural networks Perceptrons Multilayer networks Applications of neural networks Chapter 20 2 Brains 10 11 neurons of > 20 types, 10 14 synapses, 1ms
More informationLast update: October 26, Neural networks. CMSC 421: Section Dana Nau
Last update: October 26, 207 Neural networks CMSC 42: Section 8.7 Dana Nau Outline Applications of neural networks Brains Neural network units Perceptrons Multilayer perceptrons 2 Example Applications
More informationArtificial neural networks
Artificial neural networks Chapter 8, Section 7 Artificial Intelligence, spring 203, Peter Ljunglöf; based on AIMA Slides c Stuart Russel and Peter Norvig, 2004 Chapter 8, Section 7 Outline Brains Neural
More informationCS:4420 Artificial Intelligence
CS:4420 Artificial Intelligence Spring 2018 Neural Networks Cesare Tinelli The University of Iowa Copyright 2004 18, Cesare Tinelli and Stuart Russell a a These notes were originally developed by Stuart
More informationSections 18.6 and 18.7 Artificial Neural Networks
Sections 18.6 and 18.7 Artificial Neural Networks CS4811  Artificial Intelligence Nilufer Onder Department of Computer Science Michigan Technological University Outline The brain vs artifical neural networks
More informationSections 18.6 and 18.7 Artificial Neural Networks
Sections 18.6 and 18.7 Artificial Neural Networks CS4811  Artificial Intelligence Nilufer Onder Department of Computer Science Michigan Technological University Outline The brain vs. artifical neural
More informationGrundlagen der Künstlichen Intelligenz
Grundlagen der Künstlichen Intelligenz Neural networks Daniel Hennes 21.01.2018 (WS 2017/18) University Stuttgart  IPVS  Machine Learning & Robotics 1 Today Logistic regression Neural networks Perceptron
More informationSections 18.6 and 18.7 Analysis of Artificial Neural Networks
Sections 18.6 and 18.7 Analysis of Artificial Neural Networks CS4811  Artificial Intelligence Nilufer Onder Department of Computer Science Michigan Technological University Outline Univariate regression
More informationSupervised Learning Artificial Neural Networks
Lecture 11 upervised Learning Artificial Marco Chiarandini Department of Mathematics & Computer cience University of outhern Denmark lides by tuart Russell and Peter Norvig Course Overview Introduction
More informationArtificial Neural Networks" and Nonparametric Methods" CMPSCI 383 Nov 17, 2011!
Artificial Neural Networks" and Nonparametric Methods" CMPSCI 383 Nov 17, 2011! 1 Todayʼs lecture" How the brain works (!)! Artificial neural networks! Perceptrons! Multilayer feedforward networks! Error
More information22c145Fall 01: Neural Networks. Neural Networks. Readings: Chapter 19 of Russell & Norvig. Cesare Tinelli 1
Neural Networks Readings: Chapter 19 of Russell & Norvig. Cesare Tinelli 1 Brains as Computational Devices Brains advantages with respect to digital computers: Massively parallel Faulttolerant Reliable
More informationCS 4700: Foundations of Artificial Intelligence
CS 4700: Foundations of Artificial Intelligence Prof. Bart Selman selman@cs.cornell.edu Machine Learning: Neural Networks R&N 18.7 Intro & perceptron learning 1 2 Neuron: How the brain works # neurons
More informationLearning and Neural Networks
Artificial Intelligence Learning and Neural Networks Readings: Chapter 19 & 20.5 of Russell & Norvig Example: A Feedforward Network w 13 I 1 H 3 w 35 w 14 O 5 I 2 w 23 w 24 H 4 w 45 a 5 = g 5 (W 3,5 a
More informationCS 4700: Foundations of Artificial Intelligence
CS 4700: Foundations of Artificial Intelligence Prof. Bart Selman selman@cs.cornell.edu Machine Learning: Neural Networks R&N 18.7 Intro & perceptron learning 1 2 Neuron: How the brain works # neurons
More informationCh.8 Neural Networks
Ch.8 Neural Networks Hantao Zhang http://www.cs.uiowa.edu/ hzhang/c145 The University of Iowa Department of Computer Science Artificial Intelligence p.1/?? Brains as Computational Devices Motivation: Algorithms
More informationIntroduction To Artificial Neural Networks
Introduction To Artificial Neural Networks Machine Learning Supervised circle square circle square Unsupervised group these into two categories Supervised Machine Learning Supervised Machine Learning Supervised
More informationArtificial Neural Networks
Artificial Neural Networks 鮑興國 Ph.D. National Taiwan University of Science and Technology Outline Perceptrons Gradient descent Multilayer networks Backpropagation Hidden layer representations Examples
More information2015 Todd Neller. A.I.M.A. text figures 1995 Prentice Hall. Used by permission. Neural Networks. Todd W. Neller
2015 Todd Neller. A.I.M.A. text figures 1995 Prentice Hall. Used by permission. Neural Networks Todd W. Neller Machine Learning Learning is such an important part of what we consider "intelligence" that
More informationMachine Learning. Neural Networks
Machine Learning Neural Networks Bryan Pardo, Northwestern University, Machine Learning EECS 349 Fall 2007 Biological Analogy Bryan Pardo, Northwestern University, Machine Learning EECS 349 Fall 2007 THE
More informationData Mining Part 5. Prediction
Data Mining Part 5. Prediction 5.5. Spring 2010 Instructor: Dr. Masoud Yaghini Outline How the Brain Works Artificial Neural Networks Simple Computing Elements FeedForward Networks Perceptrons (Singlelayer,
More informationAI Programming CS F20 Neural Networks
AI Programming CS6622008F20 Neural Networks David Galles Department of Computer Science University of San Francisco 200: Symbolic AI Most of this class has been focused on Symbolic AI Focus or symbols
More informationIntroduction Biologically Motivated Crude Model Backpropagation
Introduction Biologically Motivated Crude Model Backpropagation 1 McCullochPitts Neurons In 1943 Warren S. McCulloch, a neuroscientist, and Walter Pitts, a logician, published A logical calculus of the
More informationMachine Learning. Neural Networks. (slides from Domingos, Pardo, others)
Machine Learning Neural Networks (slides from Domingos, Pardo, others) For this week, Reading Chapter 4: Neural Networks (Mitchell, 1997) See Canvas For subsequent weeks: Scaling Learning Algorithms toward
More informationMachine Learning. Neural Networks. (slides from Domingos, Pardo, others)
Machine Learning Neural Networks (slides from Domingos, Pardo, others) Human Brain Neurons InputOutput Transformation Input Spikes Output Spike Spike (= a brief pulse) (Excitatory PostSynaptic Potential)
More informationArtificial Neural Networks. Q550: Models in Cognitive Science Lecture 5
Artificial Neural Networks Q550: Models in Cognitive Science Lecture 5 "Intelligence is 10 million rules." Doug Lenat The human brain has about 100 billion neurons. With an estimated average of one thousand
More informationLecture 5: Logistic Regression. Neural Networks
Lecture 5: Logistic Regression. Neural Networks Logistic regression Comparison with generative models Feedforward neural networks Backpropagation Tricks for training neural networks COMP652, Lecture
More informationMachine Learning. Neural Networks. (slides from Domingos, Pardo, others)
Machine Learning Neural Networks (slides from Domingos, Pardo, others) For this week, Reading Chapter 4: Neural Networks (Mitchell, 1997) See Canvas For subsequent weeks: Scaling Learning Algorithms toward
More informationFeedforward Neural Nets and Backpropagation
Feedforward Neural Nets and Backpropagation Julie Nutini University of British Columbia MLRG September 28 th, 2016 1 / 23 Supervised Learning Roadmap Supervised Learning: Assume that we are given the features
More informationLecture 4: Feed Forward Neural Networks
Lecture 4: Feed Forward Neural Networks Dr. Roman V Belavkin Middlesex University BIS4435 Biological neurons and the brain A Model of A Single Neuron Neurons as datadriven models Neural Networks Training
More informationIntroduction to Neural Networks
Introduction to Neural Networks What are (Artificial) Neural Networks? Models of the brain and nervous system Highly parallel Process information much more like the brain than a serial computer Learning
More informationNeural Networks. Fundamentals of Neural Networks : Architectures, Algorithms and Applications. L, Fausett, 1994
Neural Networks Neural Networks Fundamentals of Neural Networks : Architectures, Algorithms and Applications. L, Fausett, 1994 An Introduction to Neural Networks (nd Ed). Morton, IM, 1995 Neural Networks
More informationCourse 395: Machine Learning  Lectures
Course 395: Machine Learning  Lectures Lecture 12: Concept Learning (M. Pantic) Lecture 34: Decision Trees & CBC Intro (M. Pantic & S. Petridis) Lecture 56: Evaluating Hypotheses (S. Petridis) Lecture
More informationLecture 4: Perceptrons and Multilayer Perceptrons
Lecture 4: Perceptrons and Multilayer Perceptrons Cognitive Systems II  Machine Learning SS 2005 Part I: Basic Approaches of Concept Learning Perceptrons, Artificial Neuronal Networks Lecture 4: Perceptrons
More informationEEE 241: Linear Systems
EEE 4: Linear Systems Summary # 3: Introduction to artificial neural networks DISTRIBUTED REPRESENTATION An ANN consists of simple processing units communicating with each other. The basic elements of
More informationCOMP9444 Neural Networks and Deep Learning 2. Perceptrons. COMP9444 c Alan Blair, 2017
COMP9444 Neural Networks and Deep Learning 2. Perceptrons COMP9444 17s2 Perceptrons 1 Outline Neurons Biological and Artificial Perceptron Learning Linear Separability MultiLayer Networks COMP9444 17s2
More informationArtificial Neural Networks
Artificial Neural Networks Oliver Schulte  CMPT 310 Neural Networks Neural networks arise from attempts to model human/animal brains Many models, many claims of biological plausibility We will focus on
More informationArtifical Neural Networks
Neural Networks Artifical Neural Networks Neural Networks Biological Neural Networks.................................. Artificial Neural Networks................................... 3 ANN Structure...........................................
More informationIntroduction to Artificial Neural Networks
Facultés Universitaires NotreDame de la Paix 27 March 2007 Outline 1 Introduction 2 Fundamentals Biological neuron Artificial neuron Artificial Neural Network Outline 3 Singlelayer ANN Perceptron Adaline
More informationCMSC 421: Neural Computation. Applications of Neural Networks
CMSC 42: Neural Computation definition synonyms neural networks artificial neural networks neural modeling connectionist models parallel distributed processing AI perspective Applications of Neural Networks
More informationUnit 8: Introduction to neural networks. Perceptrons
Unit 8: Introduction to neural networks. Perceptrons D. Balbontín Noval F. J. Martín Mateos J. L. Ruiz Reina A. Riscos Núñez Departamento de Ciencias de la Computación e Inteligencia Artificial Universidad
More informationNeural Networks biological neuron artificial neuron 1
Neural Networks biological neuron artificial neuron 1 A twolayer neural network Output layer (activation represents classification) Weighted connections Hidden layer ( internal representation ) Input
More informationLinear classification with logistic regression
Section 8.6. Regression and Classification with Linear Models 725 Proportion correct.9.7 Proportion correct.9.7 2 3 4 5 6 7 2 4 6 8 2 4 6 8 Number of weight updates Number of weight updates Number of weight
More informationMultilayer Perceptron
Outline Hong Chang Institute of Computing Technology, Chinese Academy of Sciences Machine Learning Methods (Fall 2012) Outline Outline I 1 Introduction 2 Single Perceptron 3 Boolean Function Learning 4
More informationCSC242: Intro to AI. Lecture 21
CSC242: Intro to AI Lecture 21 Administrivia Project 4 (homeworks 18 & 19) due Mon Apr 16 11:59PM Posters Apr 24 and 26 You need an idea! You need to present it nicely on 2wide by 4high landscape pages
More informationNeural Networks Lecturer: J. Matas Authors: J. Matas, B. Flach, O. Drbohlav
Neural Networks 30.11.2015 Lecturer: J. Matas Authors: J. Matas, B. Flach, O. Drbohlav 1 Talk Outline Perceptron Combining neurons to a network Neural network, processing input to an output Learning Cost
More informationMultilayer Neural Networks. (sometimes called Multilayer Perceptrons or MLPs)
Multilayer Neural Networks (sometimes called Multilayer Perceptrons or MLPs) Linear separability Hyperplane In 2D: w x + w 2 x 2 + w 0 = 0 Feature x 2 = w w 2 x w 0 w 2 Feature 2 A perceptron can separate
More informationMultilayer Neural Networks. (sometimes called Multilayer Perceptrons or MLPs)
Multilayer Neural Networks (sometimes called Multilayer Perceptrons or MLPs) Linear separability Hyperplane In 2D: w 1 x 1 + w 2 x 2 + w 0 = 0 Feature 1 x 2 = w 1 w 2 x 1 w 0 w 2 Feature 2 A perceptron
More informationEE04 804(B) Soft Computing Ver. 1.2 Class 2. Neural Networks  I Feb 23, Sasidharan Sreedharan
EE04 804(B) Soft Computing Ver. 1.2 Class 2. Neural Networks  I Feb 23, 2012 Sasidharan Sreedharan www.sasidharan.webs.com 3/1/2012 1 Syllabus Artificial Intelligence Systems Neural Networks, fuzzy logic,
More informationCSC321 Lecture 5: Multilayer Perceptrons
CSC321 Lecture 5: Multilayer Perceptrons Roger Grosse Roger Grosse CSC321 Lecture 5: Multilayer Perceptrons 1 / 21 Overview Recall the simple neuronlike unit: y output output bias i'th weight w 1 w2 w3
More informationSimple Neural Nets For Pattern Classification
CHAPTER 2 Simple Neural Nets For Pattern Classification Neural Networks General Discussion One of the simplest tasks that neural nets can be trained to perform is pattern classification. In pattern classification
More informationLecture 7 Artificial neural networks: Supervised learning
Lecture 7 Artificial neural networks: Supervised learning Introduction, or how the brain works The neuron as a simple computing element The perceptron Multilayer neural networks Accelerated learning in
More informationARTIFICIAL INTELLIGENCE. Artificial Neural Networks
INFOB2KI 20172018 Utrecht University The Netherlands ARTIFICIAL INTELLIGENCE Artificial Neural Networks Lecturer: Silja Renooij These slides are part of the INFOB2KI Course Notes available from www.cs.uu.nl/docs/vakken/b2ki/schema.html
More informationMachine Learning (CSE 446): Neural Networks
Machine Learning (CSE 446): Neural Networks Noah Smith c 2017 University of Washington nasmith@cs.washington.edu November 6, 2017 1 / 22 Admin No Wednesday office hours for Noah; no lecture Friday. 2 /
More informationArtificial Neural Networks. Introduction to Computational Neuroscience Tambet Matiisen
Artificial Neural Networks Introduction to Computational Neuroscience Tambet Matiisen 2.04.2018 Artificial neural network NB! Inspired by biology, not based on biology! Applications Automatic speech recognition
More informationNeural Networks and Deep Learning
Neural Networks and Deep Learning Professor Ameet Talwalkar November 12, 2015 Professor Ameet Talwalkar Neural Networks and Deep Learning November 12, 2015 1 / 16 Outline 1 Review of last lecture AdaBoost
More information18.6 Regression and Classification with Linear Models
18.6 Regression and Classification with Linear Models 352 The hypothesis space of linear functions of continuousvalued inputs has been used for hundreds of years A univariate linear function (a straight
More informationMultilayer Perceptrons (MLPs)
CSE 5526: Introduction to Neural Networks Multilayer Perceptrons (MLPs) 1 Motivation Multilayer networks are more powerful than singlelayer nets Example: XOR problem x 2 1 AND x o x 1 x 2 +11 o x x 11
More informationMultilayer Neural Networks
Multilayer Neural Networks Multilayer Neural Networks Discriminant function flexibility NONLinear But with sets of linear parameters at each layer Provably general function approximators for sufficient
More informationArtificial Neural Networks. Historical description
Artificial Neural Networks Historical description Victor G. Lopez 1 / 23 Artificial Neural Networks (ANN) An artificial neural network is a computational model that attempts to emulate the functions of
More informationArtificial Neural Network
Artificial Neural Network Contents 2 What is ANN? Biological Neuron Structure of Neuron Types of Neuron Models of Neuron Analogy with human NN Perceptron OCR Multilayer Neural Network Back propagation
More informationUnit III. A Survey of Neural Network Model
Unit III A Survey of Neural Network Model 1 Single Layer Perceptron Perceptron the first adaptive network architecture was invented by Frank Rosenblatt in 1957. It can be used for the classification of
More informationLinear Regression, Neural Networks, etc.
Linear Regression, Neural Networks, etc. Gradient Descent Many machine learning problems can be cast as optimization problems Define a function that corresponds to learning error. (More on this later)
More informationPlan. Perceptron Linear discriminant. Associative memories Hopfield networks Chaotic networks. Multilayer perceptron Backpropagation
Neural Networks Plan Perceptron Linear discriminant Associative memories Hopfield networks Chaotic networks Multilayer perceptron Backpropagation Perceptron Historically, the first neural net Inspired
More informationNeural Networks: Introduction
Neural Networks: Introduction Machine Learning Fall 2017 Based on slides and material from Geoffrey Hinton, Richard Socher, Dan Roth, Yoav Goldberg, Shai ShalevShwartz and Shai BenDavid, and others 1
More informationMachine Learning for LargeScale Data Analysis and Decision Making A. Neural Networks Week #6
Machine Learning for LargeScale Data Analysis and Decision Making 8062917A Neural Networks Week #6 Today Neural Networks A. Modeling B. Fitting C. Deep neural networks Today s material is (adapted)
More informationDEEP LEARNING AND NEURAL NETWORKS: BACKGROUND AND HISTORY
DEEP LEARNING AND NEURAL NETWORKS: BACKGROUND AND HISTORY 1 Online Resources http://neuralnetworksanddeeplearning.com/index.html Online book by Michael Nielsen http://matlabtricks.com/post5/3x3convolutionkernelswithonlinedemo
More informationNeural Networks and the Backpropagation Algorithm
Neural Networks and the Backpropagation Algorithm Francisco S. Melo In these notes, we provide a brief overview of the main concepts concerning neural networks and the backpropagation algorithm. We closely
More informationMultilayer Neural Networks
Multilayer Neural Networks Introduction Goal: Classify objects by learning nonlinearity There are many problems for which linear discriminants are insufficient for minimum error In previous methods, the
More information(FeedForward) Neural Networks Dr. Hajira Jabeen, Prof. Jens Lehmann
(FeedForward) Neural Networks 20161206 Dr. Hajira Jabeen, Prof. Jens Lehmann Outline In the previous lectures we have learned about tensors and factorization methods. RESCAL is a bilinear model for
More informationRevision: Neural Network
Revision: Neural Network Exercise 1 Tell whether each of the following statements is true or false by checking the appropriate box. Statement True False a) A perceptron is guaranteed to perfectly learn
More informationIntelligent Systems Discriminative Learning, Neural Networks
Intelligent Systems Discriminative Learning, Neural Networks Carsten Rother, Dmitrij Schlesinger WS2014/2015, Outline 1. Discriminative learning 2. Neurons and linear classifiers: 1) PerceptronAlgorithm
More informationArtificial Neural Networks. Edward Gatt
Artificial Neural Networks Edward Gatt What are Neural Networks? Models of the brain and nervous system Highly parallel Process information much more like the brain than a serial computer Learning Very
More informationSupervised (BPL) verses Hybrid (RBF) Learning. By: Shahed Shahir
Supervised (BPL) verses Hybrid (RBF) Learning By: Shahed Shahir 1 Outline I. Introduction II. Supervised Learning III. Hybrid Learning IV. BPL Verses RBF V. Supervised verses Hybrid learning VI. Conclusion
More informationArtificial Neural Network
Artificial Neural Network Eung Je Woo Department of Biomedical Engineering Impedance Imaging Research Center (IIRC) Kyung Hee University Korea ejwoo@khu.ac.kr Neuron and Neuron Model McCulloch and Pitts
More informationFundamentals of Neural Networks
Fundamentals of Neural Networks : Soft Computing Course Lecture 7 14, notes, slides www.myreaders.info/, RC Chakraborty, email rcchak@gmail.com, Aug. 10, 2010 http://www.myreaders.info/html/soft_computing.html
More informationy(x n, w) t n 2. (1)
Network training: Training a neural network involves determining the weight parameter vector w that minimizes a cost function. Given a training set comprising a set of input vector {x n }, n = 1,...N,
More informationNeural Networks. Mark van Rossum. January 15, School of Informatics, University of Edinburgh 1 / 28
1 / 28 Neural Networks Mark van Rossum School of Informatics, University of Edinburgh January 15, 2018 2 / 28 Goals: Understand how (recurrent) networks behave Find a way to teach networks to do a certain
More informationNeural Networks. Bishop PRML Ch. 5. Alireza Ghane. Feedforward Networks Network Training Error Backpropagation Applications
Neural Networks Bishop PRML Ch. 5 Alireza Ghane Neural Networks Alireza Ghane / Greg Mori 1 Neural Networks Neural networks arise from attempts to model human/animal brains Many models, many claims of
More informationNeural Networks and Fuzzy Logic Rajendra Dept.of CSE ASCET
Unit. Definition Neural network is a massively parallel distributed processing system, made of highly interconnected neural computing elements that have the ability to learn and thereby acquire knowledge
More informationArtificial Neural Networks The Introduction
Artificial Neural Networks The Introduction 01001110 01100101 01110101 01110010 01101111 01101110 01101111 01110110 01100001 00100000 01110011 01101011 01110101 01110000 01101001 01101110 01100001 00100000
More information4. Multilayer Perceptrons
4. Multilayer Perceptrons This is a supervised errorcorrection learning algorithm. 1 4.1 Introduction A multilayer feedforward network consists of an input layer, one or more hidden layers, and an output
More informationARTIFICIAL NEURAL NETWORK PART I HANIEH BORHANAZAD
ARTIFICIAL NEURAL NETWORK PART I HANIEH BORHANAZAD WHAT IS A NEURAL NETWORK? The simplest definition of a neural network, more properly referred to as an 'artificial' neural network (ANN), is provided
More informationCOMP4360 Machine Learning Neural Networks
COMP4360 Machine Learning Neural Networks Jacky Baltes Autonomous Agents Lab University of Manitoba Winnipeg, Canada R3T 2N2 Email: jacky@cs.umanitoba.ca WWW: http://www.cs.umanitoba.ca/~jacky http://aalab.cs.umanitoba.ca
More informationArtificial Neural Network and Fuzzy Logic
Artificial Neural Network and Fuzzy Logic 1 Syllabus 2 Syllabus 3 Books 1. Artificial Neural Networks by B. Yagnanarayan, PHI  (Cover Topologies part of unit 1 and All part of Unit 2) 2. Neural Networks
More informationNeural Nets in PR. Pattern Recognition XII. Michal Haindl. Outline. Neural Nets in PR 2
Neural Nets in PR NM P F Outline Motivation: Pattern Recognition XII human brain study complex cognitive tasks Michal Haindl Faculty of Information Technology, KTI Czech Technical University in Prague
More informationLinear discriminant functions
Andrea Passerini passerini@disi.unitn.it Machine Learning Discriminative learning Discriminative vs generative Generative learning assumes knowledge of the distribution governing the data Discriminative
More information<Special Topics in VLSI> Learning for Deep Neural Networks (Backpropagation)
Learning for Deep Neural Networks (Backpropagation) Outline Summary of Previous Standford Lecture Universal Approximation Theorem Inference vs Training Gradient Descent BackPropagation
More informationIntroduction to Machine Learning
Introduction to Machine Learning Neural Networks Varun Chandola x x 5 Input Outline Contents February 2, 207 Extending Perceptrons 2 Multi Layered Perceptrons 2 2. Generalizing to Multiple Labels.................
More informationIn the Name of God. Lecture 9: ANN Architectures
In the Name of God Lecture 9: ANN Architectures Biological Neuron Organization of Levels in Brains Central Nervous sys Interregional circuits Local circuits Neurons Dendrite tree map into cerebral cortex,
More informationCOMP 551 Applied Machine Learning Lecture 14: Neural Networks
COMP 551 Applied Machine Learning Lecture 14: Neural Networks Instructor: Ryan Lowe (ryan.lowe@mail.mcgill.ca) Slides mostly by: Class web page: www.cs.mcgill.ca/~hvanho2/comp551 Unless otherwise noted,
More informationNumerical Learning Algorithms
Numerical Learning Algorithms Example SVM for Separable Examples.......................... Example SVM for Nonseparable Examples....................... 4 Example Gaussian Kernel SVM...............................
More informationDeep Learning Lecture 2
Fall 2015 Deep Learning CMPSCI 697L Deep Learning Lecture 2 Sridhar Mahadevan Autonomous Learning Lab UMass Amherst COLLEGE Outline Some topics to be covered: 1. Quick review of classic neural nets, single
More informationLearning from Examples
Learning from Examples Data fitting Decision trees Cross validation Computational learning theory Linear classifiers Neural networks Nonparametric methods: nearest neighbor Support vector machines Ensemble
More informationHow to do backpropagation in a brain
How to do backpropagation in a brain Geoffrey Hinton Canadian Institute for Advanced Research & University of Toronto & Google Inc. Prelude I will start with three slides explaining a popular type of deep
More informationBackPropagation Algorithm. Perceptron Gradient Descent Multilayered neural network BackPropagation More on BackPropagation Examples
BackPropagation Algorithm Perceptron Gradient Descent Multilayered neural network BackPropagation More on BackPropagation Examples 1 Innerproduct net =< w, x >= w x cos(θ) net = n i=1 w i x i A measure
More informationArtificial Neural Networks (ANN) Xiaogang Su, Ph.D. Department of Mathematical Science University of Texas at El Paso
Artificial Neural Networks (ANN) Xiaogang Su, Ph.D. Department of Mathematical Science University of Texas at El Paso xsu@utep.edu Fall, 2018 Outline Introduction A Brief History ANN Architecture Terminology
More informationMachine Learning and Data Mining. Multilayer Perceptrons & Neural Networks: Basics. Prof. Alexander Ihler
+ Machine Learning and Data Mining Multilayer Perceptrons & Neural Networks: Basics Prof. Alexander Ihler Linear Classifiers (Perceptrons) Linear Classifiers a linear classifier is a mapping which partitions
More information