'Perceptrons' presentation slideshows

IAI REVISION 2012

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Neural Networks

Neural Networks. NNs are a study of parallel and distributed processing systems (PDPs) the idea is that the representation is distributed across a network structure an individual node itself does not have meaning, or does not represent a concept, unlike a semantic network

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Probabilistic Machine Learning Approaches to Medical Classification Problems Chuan LU

Probabilistic Machine Learning Approaches to Medical Classification Problems Chuan LU. Jury: Prof. L. Froyen, chairman Prof. J. Vandewalle Prof. S. Van Huffel, promotor Prof. J. Beirlant Prof. J.A.K. Suykens, promotor Prof. P.J.G. Lisboa Prof. D. Timmerman Prof. Y. Moreau

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Statistical Learning Methods

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Neural Networks

Neural Networks. CIS 479/579 Bruce R. Maxim UM-Dearborn. Neural Networks. Can be thought of as arithmetic constraint networks Tend to be designed in layers Input layer Hidden layers (1 or more) Output layer. Neural Network Types. Feed-forward

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Classification / Regression Neural Networks 2

Classification / Regression Neural Networks 2. Neural networks. Topics Perceptrons structure training expressiveness Multilayer networks possible structures activation functions training with gradient descent and backpropagation expressiveness. Neural network application.

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Page 1

Constraints as Prior Knowledge. Ming-Wei Chang, Lev Ratinov, Nicks Rizzolo, Dan Roth Department of Computer Science University of Illinois at Urbana-Champaign. AAAI-08. Page 1. Tasks of Interest.

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Biological and Artificial Neuron

Neural Networks. Learning Processes. Biological and Artificial Neuron. Weights, need to be determined. Biological neuron. Bias, need to be determined. Artificial neuron. Neural Networks. Learning Processes. Application of Neural Networks. Function approximation and prediction

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Chapter 11 Neural Networks

Chapter 11 Neural Networks. Chapter 11 Contents (1). Biological Neurons Artificial Neurons Perceptrons Multilayer Neural Networks Backpropagation. Chapter 11 Contents (2). Recurrent Networks Hopfield Networks Bidirectional Associative Memories Kohonen Maps Hebbian Learning

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CS344: Introduction to Artificial Intelligence

CS344: Introduction to Artificial Intelligence. Pushpak Bhattacharyya CSE Dept., IIT Bombay Lecture 15, 16: Perceptrons and their computing power 6 th and 7 th Feb, 2012. Threshold functions n # Boolean functions (2^2^n) #Threshold Functions (2 n2 ) 1 4 4 2 16 14

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Michael Mainelli

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INFO331 Machine learning. Neural networks. Supervised learning in neural networks.MLP and BP

INFO331 Machine learning. Neural networks. Supervised learning in neural networks.MLP and BP. (Text book: section 2.11, pp.146-155; section 3.7.3., pp.218-221); section 4.2, pp.267-282;catch-up reading: pp.251-266). Machine learning. Issues in machine learning

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An Intro to Nerual Networks

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Tuesday, October 12, 1999 William H. Hsu Department of Computing and Information Sciences, KSU

Lecture 14. Midterm Review. Tuesday, October 12, 1999 William H. Hsu Department of Computing and Information Sciences, KSU http://www.cis.ksu.edu/~bhsu Readings: Chapters 1-7, Mitchell Chapters 14-15, 18, Russell and Norvig. Lecture 0: A Brief Overview of Machine Learning.

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CS 461: Machine Learning Lecture 4

CS 461: Machine Learning Lecture 4. Dr. Kiri Wagstaff wkiri@wkiri.com. Plan for Today. Solution to HW 2 Support Vector Machines Neural Networks Perceptrons Multilayer Perceptrons. Review from Lecture 3. Decision trees Regression trees, pruning, extracting rules Evaluation

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Announcements

Announcements. 1. Textbook will be on reserve at library 2. Topic schedule change; modified reading assignment: This week: Linear discrimination, evaluating classifiers Extra reading: T. Fawcett, An introduction to ROC analysis, Sections 1-4 (linked from class web page )

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Neural Networks

Neural Networks. Perceptrons. Multi-layer Perceptron. MLP. Single Perceptron Unit. y. X 0 =. -1. x n. x 2. x 1. MLP. O 3. 2-Layer Perceptron Network. Hidden units. -1. O 1. O 2. -1. -1. x 2. x 1. Multilayer Layer Perceptron. y. 2-Layer Perceptron Network. W 02.

285 views • 9 slides