Perceptrons and Backpropagation. Fabio Zachert Cognitive Modelling WiSe 2014/15
|
|
|
- Gerald Robbins
- 6 years ago
- Views:
Transcription
1 Perceptrons and Backpropagation Fabio Zachert Cognitive Modelling WiSe 2014/15
2 Content History Mathematical View of Perceptrons Network Structures Gradient Descent Backpropagation (Single-Layer-, Multilayer-Networks) Learning The Structure Questions
3 History Inspired by the brains' information processing 1943: Warren McCulloch and Walter Pitts create computational model 1974: Paul Werbos creates backpropagation-algorithm Generally used for regression and classification in AI
4 Mathematical Model of Perceptrons
5 Mathematical Model of Perceptrons Activation function Non-linear function with output between 0 and 1 Often a thresholdhold function or sigmoid Sigmoid:
6 Boolean Function But how to create the XOR operator? Use a network of units
7 Network Structures Combine multiple units to generate more complex functions Feed-forward networks No loops allowed Recurrent networks Loops allowed More complex functions Possibility to simulate memory No convergence guaranteed
8 Network Structures
9 Network Structures Input Units
10 Network Structures Hidden Layer
11 Network Structures Output unit
12 Network Structures
13 Gradient Descent Problem: Find argminx f(x) Minimum cannot be calculated directly Gradient defines the direction of the highest slope at a given point Use gradient of f(x) to search downhill in the direction of the minimum Algorithm: Init x Until convergence x x α * f'(x) α is the learning rate
14 Gradient Descent When to converge? After n steps Gradient threshold Gain threshold How to set learn rate α? Experience of the programmer Adaptive algorithms No guarantee to find global minimum
15 Single-Layer Backpropagation
16 Single-Layer Backpropagation A supervised learning task Given (x,y) pairs Learn general function for all x Minimize sum of square error (SSE) for network-function h(x) with weight-vector w Use gradient descent to find optimal w Calculate partial derivation of the error over the weight-vector
17 Single-Layer Backpropagation Recall: Derivation Partial derivation : derivation of a function with multiple input above one single input Rules: Sum-rule: Chain-rule: Product-rule: Power-rule: Constant-factor-rule:
18 Single-Layer Backprogation Calculate partial derivation For
19 Single-Layer Backpropagation Algorithm: Calculate perceptron output and error for each training sample Update weights, based on error-gradient
20 Multi-Layer Backpropagation
21 Multi-Layer Backpropagation Extend network with a hidden-layer Update-rule for output-layer based on output of hidden-layer with Propagate the error back on the hidden layer Update-rule for hidden-layer is based on its output One hidden-layer unit is partly responsible for the errors of the connected output-units with
22 Multi-Layer Backpropagation Algorithm:
23 Multi-Layer Backpropagation Algorithm: Init input-layer Feed-forward input through layers
24 Multi-Layer Backpropagation Algorithm: Calculate error of output-layer
25 Multi-Layer Backpropagation Algorithm: Propagate error backwards through the layers
26 Multi-Layer Backpropagation Algorithm: Correct weights based on error-gradient
27 Learning the Structure But, how to learn the size/structure of the network? Oversized nets tend to overfitting Learn the general function and not the noise Try to estimate the optimal size: Optimal-brain-damage-algorithm Cross-validation
28 N-Fold-Crossvalidation Separate data for learning and evaluation Split dataset in n parts n-1 parts for training, 1 part for evaluation n Use different parts for evaluation Calculate average evaluation error Use network-size with the lowest evaluation-error
29 Questions
30 Questions How to initialize weights?
31 Sources Russell, S., & Norvig, P. (2003). Artificial intelligence: A modern approach. Upper Saddle River, NJ: Pearson Education Madsen, K., Nielsen, H.B., Tingleff, O., (2004, 2nd Edition), Methods for non-linear least squares problems Murphy, Kevin P. (2012). Machine learning: a probabilistic perspective. MIT press
32 Thank you for your attention!
Learning. Learning agents Inductive learning. Neural Networks. Different Learning Scenarios Evaluation
Learning Learning agents Inductive learning Different Learning Scenarios Evaluation Slides based on Slides by Russell/Norvig, Ronald Williams, and Torsten Reil Material from Russell & Norvig, chapters
Supervised Learning in Neural Networks (Part 2)
Supervised Learning in Neural Networks (Part 2) Multilayer neural networks (back-propagation training algorithm) The input signals are propagated in a forward direction on a layer-bylayer basis. Learning
Multilayer Feed-forward networks
Multi Feed-forward networks 1. Computational models of McCulloch and Pitts proposed a binary threshold unit as a computational model for artificial neuron. This first type of neuron has been generalized
Artificial Neural Networks Lecture Notes Part 5. Stephen Lucci, PhD. Part 5
Artificial Neural Networks Lecture Notes Part 5 About this file: If you have trouble reading the contents of this file, or in case of transcription errors, email gi0062@bcmail.brooklyn.cuny.edu Acknowledgments:
Neural Network Neurons
Neural Networks Neural Network Neurons 1 Receives n inputs (plus a bias term) Multiplies each input by its weight Applies activation function to the sum of results Outputs result Activation Functions Given
COMP 551 Applied Machine Learning Lecture 14: Neural Networks
COMP 551 Applied Machine Learning Lecture 14: Neural Networks Instructor: (jpineau@cs.mcgill.ca) Class web page: www.cs.mcgill.ca/~jpineau/comp551 Unless otherwise noted, all material posted for this course
CP365 Artificial Intelligence
CP365 Artificial Intelligence Tech News! Apple news conference tomorrow? Tech News! Apple news conference tomorrow? Google cancels Project Ara modular phone Weather-Based Stock Market Predictions? Dataset
Motivation. Problem: With our linear methods, we can train the weights but not the basis functions: Activator Trainable weight. Fixed basis function
Neural Networks Motivation Problem: With our linear methods, we can train the weights but not the basis functions: Activator Trainable weight Fixed basis function Flashback: Linear regression Flashback:
Notes on Multilayer, Feedforward Neural Networks
Notes on Multilayer, Feedforward Neural Networks CS425/528: Machine Learning Fall 2012 Prepared by: Lynne E. Parker [Material in these notes was gleaned from various sources, including E. Alpaydin s book
Natural Language Processing CS 6320 Lecture 6 Neural Language Models. Instructor: Sanda Harabagiu
Natural Language Processing CS 6320 Lecture 6 Neural Language Models Instructor: Sanda Harabagiu In this lecture We shall cover: Deep Neural Models for Natural Language Processing Introduce Feed Forward
Neural Networks. CE-725: Statistical Pattern Recognition Sharif University of Technology Spring Soleymani
Neural Networks CE-725: Statistical Pattern Recognition Sharif University of Technology Spring 2013 Soleymani Outline Biological and artificial neural networks Feed-forward neural networks Single layer
Week 3: Perceptron and Multi-layer Perceptron
Week 3: Perceptron and Multi-layer Perceptron Phong Le, Willem Zuidema November 12, 2013 Last week we studied two famous biological neuron models, Fitzhugh-Nagumo model and Izhikevich model. This week,
Data Mining. Neural Networks
Data Mining Neural Networks Goals for this Unit Basic understanding of Neural Networks and how they work Ability to use Neural Networks to solve real problems Understand when neural networks may be most
Lecture 20: Neural Networks for NLP. Zubin Pahuja
Lecture 20: Neural Networks for NLP Zubin Pahuja zpahuja2@illinois.edu courses.engr.illinois.edu/cs447 CS447: Natural Language Processing 1 Today s Lecture Feed-forward neural networks as classifiers simple
CMPT 882 Week 3 Summary
CMPT 882 Week 3 Summary! Artificial Neural Networks (ANNs) are networks of interconnected simple units that are based on a greatly simplified model of the brain. ANNs are useful learning tools by being
Character Recognition Using Convolutional Neural Networks
Character Recognition Using Convolutional Neural Networks David Bouchain Seminar Statistical Learning Theory University of Ulm, Germany Institute for Neural Information Processing Winter 2006/2007 Abstract
DEEP LEARNING IN PYTHON. The need for optimization
DEEP LEARNING IN PYTHON The need for optimization A baseline neural network Input 2 Hidden Layer 5 2 Output - 9-3 Actual Value of Target: 3 Error: Actual - Predicted = 4 A baseline neural network Input
Supervised Learning (contd) Linear Separation. Mausam (based on slides by UW-AI faculty)
Supervised Learning (contd) Linear Separation Mausam (based on slides by UW-AI faculty) Images as Vectors Binary handwritten characters Treat an image as a highdimensional vector (e.g., by reading pixel
Neural Network Learning. Today s Lecture. Continuation of Neural Networks. Artificial Neural Networks. Lecture 24: Learning 3. Victor R.
Lecture 24: Learning 3 Victor R. Lesser CMPSCI 683 Fall 2010 Today s Lecture Continuation of Neural Networks Artificial Neural Networks Compose of nodes/units connected by links Each link has a numeric
Lecture 17: Neural Networks and Deep Learning. Instructor: Saravanan Thirumuruganathan
Lecture 17: Neural Networks and Deep Learning Instructor: Saravanan Thirumuruganathan Outline Perceptron Neural Networks Deep Learning Convolutional Neural Networks Recurrent Neural Networks Auto Encoders
Assignment # 5. Farrukh Jabeen Due Date: November 2, Neural Networks: Backpropation
Farrukh Jabeen Due Date: November 2, 2009. Neural Networks: Backpropation Assignment # 5 The "Backpropagation" method is one of the most popular methods of "learning" by a neural network. Read the class
An Algorithm For Training Multilayer Perceptron (MLP) For Image Reconstruction Using Neural Network Without Overfitting.
An Algorithm For Training Multilayer Perceptron (MLP) For Image Reconstruction Using Neural Network Without Overfitting. Mohammad Mahmudul Alam Mia, Shovasis Kumar Biswas, Monalisa Chowdhury Urmi, Abubakar
Introduction to Neural Networks
Introduction to Neural Networks What are connectionist neural networks? Connectionism refers to a computer modeling approach to computation that is loosely based upon the architecture of the brain Many
Neural Networks. Robot Image Credit: Viktoriya Sukhanova 123RF.com
Neural Networks These slides were assembled by Eric Eaton, with grateful acknowledgement of the many others who made their course materials freely available online. Feel free to reuse or adapt these slides
Hybrid PSO-SA algorithm for training a Neural Network for Classification
Hybrid PSO-SA algorithm for training a Neural Network for Classification Sriram G. Sanjeevi 1, A. Naga Nikhila 2,Thaseem Khan 3 and G. Sumathi 4 1 Associate Professor, Dept. of CSE, National Institute
LECTURE NOTES Professor Anita Wasilewska NEURAL NETWORKS
LECTURE NOTES Professor Anita Wasilewska NEURAL NETWORKS Neural Networks Classifier Introduction INPUT: classification data, i.e. it contains an classification (class) attribute. WE also say that the class
For Monday. Read chapter 18, sections Homework:
For Monday Read chapter 18, sections 10-12 The material in section 8 and 9 is interesting, but we won t take time to cover it this semester Homework: Chapter 18, exercise 25 a-b Program 4 Model Neuron
Deep Learning Basic Lecture - Complex Systems & Artificial Intelligence 2017/18 (VO) Asan Agibetov, PhD.
Deep Learning 861.061 Basic Lecture - Complex Systems & Artificial Intelligence 2017/18 (VO) Asan Agibetov, PhD asan.agibetov@meduniwien.ac.at Medical University of Vienna Center for Medical Statistics,
Neural Networks CMSC475/675
Introduction to Neural Networks CMSC475/675 Chapter 1 Introduction Why ANN Introduction Some tasks can be done easily (effortlessly) by humans but are hard by conventional paradigms on Von Neumann machine
Machine Learning Classifiers and Boosting
Machine Learning Classifiers and Boosting Reading Ch 18.6-18.12, 20.1-20.3.2 Outline Different types of learning problems Different types of learning algorithms Supervised learning Decision trees Naïve
Linear Separability. Linear Separability. Capabilities of Threshold Neurons. Capabilities of Threshold Neurons. Capabilities of Threshold Neurons
Linear Separability Input space in the two-dimensional case (n = ): - - - - - - w =, w =, = - - - - - - w = -, w =, = - - - - - - w = -, w =, = Linear Separability So by varying the weights and the threshold,
Artificial Neural Networks MLP, RBF & GMDH
Artificial Neural Networks MLP, RBF & GMDH Jan Drchal drchajan@fel.cvut.cz Computational Intelligence Group Department of Computer Science and Engineering Faculty of Electrical Engineering Czech Technical
CS6220: DATA MINING TECHNIQUES
CS6220: DATA MINING TECHNIQUES Image Data: Classification via Neural Networks Instructor: Yizhou Sun yzsun@ccs.neu.edu November 19, 2015 Methods to Learn Classification Clustering Frequent Pattern Mining
11/14/2010 Intelligent Systems and Soft Computing 1
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
Khmer Character Recognition using Artificial Neural Network
Khmer Character Recognition using Artificial Neural Network Hann Meng * and Daniel Morariu * Faculty of Engineering, Lucian Blaga University of Sibiu, Sibiu, Romania E-mail: meng.hann@rupp.edu.kh Tel:
Ensemble methods in machine learning. Example. Neural networks. Neural networks
Ensemble methods in machine learning Bootstrap aggregating (bagging) train an ensemble of models based on randomly resampled versions of the training set, then take a majority vote Example What if you
Machine Learning. Deep Learning. Eric Xing (and Pengtao Xie) , Fall Lecture 8, October 6, Eric CMU,
Machine Learning 10-701, Fall 2015 Deep Learning Eric Xing (and Pengtao Xie) Lecture 8, October 6, 2015 Eric Xing @ CMU, 2015 1 A perennial challenge in computer vision: feature engineering SIFT Spin image
Artificial neural networks are the paradigm of connectionist systems (connectionism vs. symbolism)
Artificial Neural Networks Analogy to biological neural systems, the most robust learning systems we know. Attempt to: Understand natural biological systems through computational modeling. Model intelligent
Artificial Neural Networks (Feedforward Nets)
Artificial Neural Networks (Feedforward Nets) y w 03-1 w 13 y 1 w 23 y 2 w 01 w 21 w 22 w 02-1 w 11 w 12-1 x 1 x 2 6.034 - Spring 1 Single Perceptron Unit y w 0 w 1 w n w 2 w 3 x 0 =1 x 1 x 2 x 3... x
ImageNet Classification with Deep Convolutional Neural Networks
ImageNet Classification with Deep Convolutional Neural Networks Alex Krizhevsky Ilya Sutskever Geoffrey Hinton University of Toronto Canada Paper with same name to appear in NIPS 2012 Main idea Architecture
2. Neural network basics
2. Neural network basics Next commonalities among different neural networks are discussed in order to get started and show which structural parts or concepts appear in almost all networks. It is presented
Artificial Neural Networks. Introduction to Computational Neuroscience Ardi Tampuu
Artificial Neural Networks Introduction to Computational Neuroscience Ardi Tampuu 7.0.206 Artificial neural network NB! Inspired by biology, not based on biology! Applications Automatic speech recognition
Multi Layer Perceptron with Back Propagation. User Manual
Multi Layer Perceptron with Back Propagation User Manual DAME-MAN-NA-0011 Issue: 1.3 Date: September 03, 2013 Author: S. Cavuoti, M. Brescia Doc. : MLPBP_UserManual_DAME-MAN-NA-0011-Rel1.3 1 INDEX 1 Introduction...
Simple Model Selection Cross Validation Regularization Neural Networks
Neural Nets: Many possible refs e.g., Mitchell Chapter 4 Simple Model Selection Cross Validation Regularization Neural Networks Machine Learning 10701/15781 Carlos Guestrin Carnegie Mellon University February
COMPUTATIONAL INTELLIGENCE
COMPUTATIONAL INTELLIGENCE Fundamentals Adrian Horzyk Preface Before we can proceed to discuss specific complex methods we have to introduce basic concepts, principles, and models of computational intelligence
Neural Networks. Single-layer neural network. CSE 446: Machine Learning Emily Fox University of Washington March 10, /10/2017
3/0/207 Neural Networks Emily Fox University of Washington March 0, 207 Slides adapted from Ali Farhadi (via Carlos Guestrin and Luke Zettlemoyer) Single-layer neural network 3/0/207 Perceptron as a neural
Neural Network and Deep Learning. Donglin Zeng, Department of Biostatistics, University of North Carolina
Neural Network and Deep Learning Early history of deep learning Deep learning dates back to 1940s: known as cybernetics in the 1940s-60s, connectionism in the 1980s-90s, and under the current name starting
House Price Prediction Using LSTM
House Price Prediction Using LSTM Xiaochen Chen Lai Wei The Hong Kong University of Science and Technology Jiaxin Xu ABSTRACT In this paper, we use the house price data ranging from January 2004 to October
A Systematic Overview of Data Mining Algorithms
A Systematic Overview of Data Mining Algorithms 1 Data Mining Algorithm A well-defined procedure that takes data as input and produces output as models or patterns well-defined: precisely encoded as a
Reservoir Computing with Emphasis on Liquid State Machines
Reservoir Computing with Emphasis on Liquid State Machines Alex Klibisz University of Tennessee aklibisz@gmail.com November 28, 2016 Context and Motivation Traditional ANNs are useful for non-linear problems,
Technical University of Munich. Exercise 7: Neural Network Basics
Technical University of Munich Chair for Bioinformatics and Computational Biology Protein Prediction I for Computer Scientists SoSe 2018 Prof. B. Rost M. Bernhofer, M. Heinzinger, D. Nechaev, L. Richter
In this assignment, we investigated the use of neural networks for supervised classification
Paul Couchman Fabien Imbault Ronan Tigreat Gorka Urchegui Tellechea Classification assignment (group 6) Image processing MSc Embedded Systems March 2003 Classification includes a broad range of decision-theoric
CS535 Big Data Fall 2017 Colorado State University 10/10/2017 Sangmi Lee Pallickara Week 8- A.
CS535 Big Data - Fall 2017 Week 8-A-1 CS535 BIG DATA FAQs Term project proposal New deadline: Tomorrow PA1 demo PART 1. BATCH COMPUTING MODELS FOR BIG DATA ANALYTICS 5. ADVANCED DATA ANALYTICS WITH APACHE
Deep Learning. Vladimir Golkov Technical University of Munich Computer Vision Group
Deep Learning Vladimir Golkov Technical University of Munich Computer Vision Group 1D Input, 1D Output target input 2 2D Input, 1D Output: Data Distribution Complexity Imagine many dimensions (data occupies
Back propagation Algorithm:
Network Neural: A neural network is a class of computing system. They are created from very simple processing nodes formed into a network. They are inspired by the way that biological systems such as the
IMPLEMENTING DEEP LEARNING USING CUDNN 이예하 VUNO INC.
IMPLEMENTING DEEP LEARNING USING CUDNN 이예하 VUNO INC. CONTENTS Deep Learning Review Implementation on GPU using cudnn Optimization Issues Introduction to VUNO-Net DEEP LEARNING REVIEW BRIEF HISTORY OF NEURAL
Lecture #11: The Perceptron
Lecture #11: The Perceptron Mat Kallada STAT2450 - Introduction to Data Mining Outline for Today Welcome back! Assignment 3 The Perceptron Learning Method Perceptron Learning Rule Assignment 3 Will be
Artificial Neural Networks
The Perceptron Rodrigo Fernandes de Mello Invited Professor at Télécom ParisTech Associate Professor at Universidade de São Paulo, ICMC, Brazil http://www.icmc.usp.br/~mello mello@icmc.usp.br Conceptually
5 Learning hypothesis classes (16 points)
5 Learning hypothesis classes (16 points) Consider a classification problem with two real valued inputs. For each of the following algorithms, specify all of the separators below that it could have generated
CS 4510/9010 Applied Machine Learning
CS 4510/9010 Applied Machine Learning Neural Nets Paula Matuszek Spring, 2015 1 Neural Nets, the very short version A neural net consists of layers of nodes, or neurons, each of which has an activation
Predict the Likelihood of Responding to Direct Mail Campaign in Consumer Lending Industry
Predict the Likelihood of Responding to Direct Mail Campaign in Consumer Lending Industry Jincheng Cao, SCPD Jincheng@stanford.edu 1. INTRODUCTION When running a direct mail campaign, it s common practice
Logical Rhythm - Class 3. August 27, 2018
Logical Rhythm - Class 3 August 27, 2018 In this Class Neural Networks (Intro To Deep Learning) Decision Trees Ensemble Methods(Random Forest) Hyperparameter Optimisation and Bias Variance Tradeoff Biological
Performance Evaluation of Artificial Neural Networks for Spatial Data Analysis
Performance Evaluation of Artificial Neural Networks for Spatial Data Analysis Akram A. Moustafa 1*, Ziad A. Alqadi 2 and Eyad A. Shahroury 3 1 Department of Computer Science Al Al-Bayt University P.O.
Neural Networks and Deep Learning
Neural Networks and Deep Learning Example Learning Problem Example Learning Problem Celebrity Faces in the Wild Machine Learning Pipeline Raw data Feature extract. Feature computation Inference: prediction,
4.12 Generalization. In back-propagation learning, as many training examples as possible are typically used.
1 4.12 Generalization In back-propagation learning, as many training examples as possible are typically used. It is hoped that the network so designed generalizes well. A network generalizes well when
CS 4510/9010 Applied Machine Learning. Neural Nets. Paula Matuszek Fall copyright Paula Matuszek 2016
CS 4510/9010 Applied Machine Learning 1 Neural Nets Paula Matuszek Fall 2016 Neural Nets, the very short version 2 A neural net consists of layers of nodes, or neurons, each of which has an activation
Robust Regression. Robust Data Mining Techniques By Boonyakorn Jantaranuson
Robust Regression Robust Data Mining Techniques By Boonyakorn Jantaranuson Outline Introduction OLS and important terminology Least Median of Squares (LMedS) M-estimator Penalized least squares What is
Introduction to Multilayer Perceptrons
An Introduction to Multilayered Neural Networks Introduction to Multilayer Perceptrons Marco Gori University of Siena Outline of the course Motivations and biological inspiration Multilayer perceptrons:
Machine Learning. Topic 5: Linear Discriminants. Bryan Pardo, EECS 349 Machine Learning, 2013
Machine Learning Topic 5: Linear Discriminants Bryan Pardo, EECS 349 Machine Learning, 2013 Thanks to Mark Cartwright for his extensive contributions to these slides Thanks to Alpaydin, Bishop, and Duda/Hart/Stork
arxiv: v1 [cs.lg] 25 Jan 2018
![arxiv: v1 [cs.lg] 25 Jan 2018](/thumbs/81/82931634.jpg "arxiv: v1 [cs.lg] 25 Jan 2018")
A New Backpropagation Algorithm without Gradient Descent arxiv:1802.00027v1 [cs.lg] 25 Jan 2018 Varun Ranganathan Student at PES University varunranga1997@hotmail.com January 2018 S. Natarajan Professor
Lecture 2 Notes. Outline. Neural Networks. The Big Idea. Architecture. Instructors: Parth Shah, Riju Pahwa
Instructors: Parth Shah, Riju Pahwa Lecture 2 Notes Outline 1. Neural Networks The Big Idea Architecture SGD and Backpropagation 2. Convolutional Neural Networks Intuition Architecture 3. Recurrent Neural
Review on Methods of Selecting Number of Hidden Nodes in Artificial Neural Network
Available Online at www.ijcsmc.com International Journal of Computer Science and Mobile Computing A Monthly Journal of Computer Science and Information Technology IJCSMC, Vol. 3, Issue. 11, November 2014,
Overlapping Swarm Intelligence for Training Artificial Neural Networks
Overlapping Swarm Intelligence for Training Artificial Neural Networks Karthik Ganesan Pillai Department of Computer Science Montana State University EPS 357, PO Box 173880 Bozeman, MT 59717-3880 k.ganesanpillai@cs.montana.edu
Artificial Intelligence
Artificial Intelligence AI & Machine Learning & Neural Nets Marc Toussaint University of Stuttgart Winter 2018/19 Motivation: Neural networks became a central topic for Machine Learning and AI. But in
Pattern Classification Algorithms for Face Recognition
Chapter 7 Pattern Classification Algorithms for Face Recognition 7.1 Introduction The best pattern recognizers in most instances are human beings. Yet we do not completely understand how the brain recognize
Transactions on Information and Communications Technologies vol WIT Press, ISSN
A comparison of methods for customer classification Maria Celia S.Lopes, Myrian C.A.Costa & Nelson F.F.Ebecken COPPE/Federal University ofrio de Janeiro Caixa Postal 68506 CEP 21945-970 Rio de Janeiro
Mini-project 2 CMPSCI 689 Spring 2015 Due: Tuesday, April 07, in class
Mini-project 2 CMPSCI 689 Spring 2015 Due: Tuesday, April 07, in class Guidelines Submission. Submit a hardcopy of the report containing all the figures and printouts of code in class. For readability
Predict the box office of US movies
Predict the box office of US movies Group members: Hanqing Ma, Jin Sun, Zeyu Zhang 1. Introduction Our task is to predict the box office of the upcoming movies using the properties of the movies, such
Neural network based Numerical digits Recognization using NNT in Matlab
Neural network based Numerical digits Recognization using NNT in Matlab ABSTRACT Amritpal kaur 1, Madhavi Arora 2 M.tech- ECE 1, Assistant Professor 2 Global institute of engineering and technology, Amritsar
Deep Learning with R. Francesca Lazzeri Data Scientist II - Microsoft, AI Research
with R Francesca Lazzeri - @frlazzeri Data Scientist II - Microsoft, AI Research Agenda with R What is Demo Better understanding of R DL tools Fundamental concepts in Forward Propagation Algorithm Activation
An introduction to neural networks for beginners. By Dr Andy Thomas Adventures in Machine Learning
An introduction to neural networks for beginners By Dr Andy Thomas Adventures in Machine Learning Table of Contents Introduction... 2 Who I am and my approach... 2 The code, pre-requisites and installation...
Deep Learning With Noise
Deep Learning With Noise Yixin Luo Computer Science Department Carnegie Mellon University yixinluo@cs.cmu.edu Fan Yang Department of Mathematical Sciences Carnegie Mellon University fanyang1@andrew.cmu.edu
Machine Learning 13. week
Machine Learning 13. week Deep Learning Convolutional Neural Network Recurrent Neural Network 1 Why Deep Learning is so Popular? 1. Increase in the amount of data Thanks to the Internet, huge amount of
6. Backpropagation training 6.1 Background
6. Backpropagation training 6.1 Background To understand well how a feedforward neural network is built and it functions, we consider its basic first steps. We return to its history for a while. In 1949
Yuki Osada Andrew Cannon
Yuki Osada Andrew Cannon 1 Humans are an intelligent species One feature is the ability to learn The ability to learn comes down to the brain The brain learns from experience Research shows that the brain
THE NEURAL NETWORKS: APPLICATION AND OPTIMIZATION APPLICATION OF LEVENBERG-MARQUARDT ALGORITHM FOR TIFINAGH CHARACTER RECOGNITION
International Journal of Science, Environment and Technology, Vol. 2, No 5, 2013, 779 786 ISSN 2278-3687 (O) THE NEURAL NETWORKS: APPLICATION AND OPTIMIZATION APPLICATION OF LEVENBERG-MARQUARDT ALGORITHM
Supervised Learning with Neural Networks. We now look at how an agent might learn to solve a general problem by seeing examples.
Supervised Learning with Neural Networks We now look at how an agent might learn to solve a general problem by seeing examples. Aims: to present an outline of supervised learning as part of AI; to introduce
Machine Learning in Biology
Università degli studi di Padova Machine Learning in Biology Luca Silvestrin (Dottorando, XXIII ciclo) Supervised learning Contents Class-conditional probability density Linear and quadratic discriminant
Fall 09, Homework 5
5-38 Fall 09, Homework 5 Due: Wednesday, November 8th, beginning of the class You can work in a group of up to two people. This group does not need to be the same group as for the other homeworks. You
Deep Neural Networks with Flexible Activation Function
Deep Neural Networks with Flexible Activation Function Faculty of Information Engineering, Informatics, and Statistics Master in Artificial Intelligence and Robotics Candidate Alberto Marinelli 1571560
CS 354R: Computer Game Technology
CS 354R: Computer Game Technology AI Fuzzy Logic and Neural Nets Fall 2018 Fuzzy Logic Philosophical approach Decisions based on degree of truth Is not a method for reasoning under uncertainty that s probability
A neural network that classifies glass either as window or non-window depending on the glass chemistry.
A neural network that classifies glass either as window or non-window depending on the glass chemistry. Djaber Maouche Department of Electrical Electronic Engineering Cukurova University Adana, Turkey
SEMANTIC COMPUTING. Lecture 8: Introduction to Deep Learning. TU Dresden, 7 December Dagmar Gromann International Center For Computational Logic
SEMANTIC COMPUTING Lecture 8: Introduction to Deep Learning Dagmar Gromann International Center For Computational Logic TU Dresden, 7 December 2018 Overview Introduction Deep Learning General Neural Networks
More on Learning. Neural Nets Support Vectors Machines Unsupervised Learning (Clustering) K-Means Expectation-Maximization
More on Learning Neural Nets Support Vectors Machines Unsupervised Learning (Clustering) K-Means Expectation-Maximization Neural Net Learning Motivated by studies of the brain. A network of artificial
CS 6501: Deep Learning for Computer Graphics. Training Neural Networks II. Connelly Barnes
CS 6501: Deep Learning for Computer Graphics Training Neural Networks II Connelly Barnes Overview Preprocessing Initialization Vanishing/exploding gradients problem Batch normalization Dropout Additional
Deep Learning. Practical introduction with Keras JORDI TORRES 27/05/2018. Chapter 3 JORDI TORRES
Deep Learning Practical introduction with Keras Chapter 3 27/05/2018 Neuron A neural network is formed by neurons connected to each other; in turn, each connection of one neural network is associated
RIMT IET, Mandi Gobindgarh Abstract - In this paper, analysis the speed of sending message in Healthcare standard 7 with the use of back
Global Journal of Computer Science and Technology Neural & Artificial Intelligence Volume 13 Issue 3 Version 1.0 Year 2013 Type: Double Blind Peer Reviewed International Research Journal Publisher: Global
Homework 5. Due: April 20, 2018 at 7:00PM
Homework 5 Due: April 20, 2018 at 7:00PM Written Questions Problem 1 (25 points) Recall that linear regression considers hypotheses that are linear functions of their inputs, h w (x) = w, x. In lecture,
MODELLING OF ARTIFICIAL NEURAL NETWORK CONTROLLER FOR ELECTRIC DRIVE WITH LINEAR TORQUE LOAD FUNCTION
MODELLING OF ARTIFICIAL NEURAL NETWORK CONTROLLER FOR ELECTRIC DRIVE WITH LINEAR TORQUE LOAD FUNCTION Janis Greivulis, Anatoly Levchenkov, Mikhail Gorobetz Riga Technical University, Faculty of Electrical
Optimization. Industrial AI Lab.
Optimization Industrial AI Lab. Optimization An important tool in 1) Engineering problem solving and 2) Decision science People optimize Nature optimizes 2 Optimization People optimize (source: http://nautil.us/blog/to-save-drowning-people-ask-yourself-what-would-light-do)