'Back propagation' diaporamas de présentation

Applying back propagation to shape recognition

Applying back propagation to shape recognition. Presented by Amir Mahdi Azadi Farzane Salami Osame Ghavidel Ahmad Lahuti. outline. BP algorithm IDP Shape recognition with matlab code Face recognision with matlab code. Introduction.

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CS 9633 Machine Learning

CS 9633 Machine Learning. Neural Networks. Adapted from notes by Tom Mitchell http://www-2.cs.cmu.edu/~tom/mlbook-chapter-slides.html. Neural networks. Practical method for learning Real-valued functions Discrete-valued functions Vector-valued functions Robust in the presence of noise

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E xamples of classification methods

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Presented by: Sameer Kulkarni Dept of Computer & Information Sciences University of Delaware

Presented by: Sameer Kulkarni Dept of Computer & Information Sciences University of Delaware. Phase Ordering. Optimization??. does it really work??. No. of optimizations. O64 = 264 (on last count) JikesRVM = 67. Search space. Consider a hypothetical case where we apply 40 optimizations

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Neural Networks

Neural Networks. A neural network is a network of simulated neurons that can be used to recognize instances of patterns. NNs learn by searching through a space of network weights http://www.cs.unr.edu/~sushil/class/ai/classnotes/glickman/1.pgm.txt.

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Shimin Chen Big Data Reading Group

Map-Reduce for Machine Learning on Multicore C. Chu, S.K. Kim, Y. Lin, Y.Y. Yu, G. Bradski, A.Y. Ng, K. Olukotun (NIPS 2006). Shimin Chen Big Data Reading Group. Motivations. Industry-wide shift to multicore No good framework for parallelize ML algorithms

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Soft computing

Soft computing. Lecture 7 Multi-Layer perceptrons. Why hidden layer is needed. Problem of XOR for simple perceptron. X 2. (0,1). (1,1). Class 1. Class 2. Class 1. Class 2. (0,0). (1,0). X 1. In this case it is not possible to draw descriminant line. Minimization of error.

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Literature Review Development of Online Tool Conditioning Monitoring System By:Vikender Panwar

Literature Review Development of Online Tool Conditioning Monitoring System By:Vikender Panwar MEng -Mechanical Engineering(260548815) Submitted to: Prof Helmi Attia Mechanical Engineering Department McGill University. Tool Failure.

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Relevant Previous Algorithms

Relevant Previous Algorithms. Linear discrimination LMS Gradient descent. Features. Simple but powerful Nonlinear functions Multilayers Heuristic. Feedforward Operation Example. y, Perceptron. Fourier’s Theorem. Kolmogorov. Universal expressive power.

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Neural Network Basics

Neural Network Basics. Anns are analytical systems that address problems whose solutions have not been explicitly formulated Structure in which multiple nodes communicate with each other through synapses that interconnect them

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Neural N etwork

Neural N etwork. Contents. Diagram of a Neuron The Simple Perceptron Multilayer Neural Network What is Hidden Layer? Why do we Need a Hidden Layer? How do Multilayer Neural Networks Learn?. Weight. Output Signals. Input signals. w 1. x 1. w 2. Neuron. Y. x 2. w n. x 3.

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Overview of Back Propagation Algorithm

Overview of Back Propagation Algorithm. Shuiwang Ji. A Sample Network. Forward Operation. The general feed-forward operation is:. Back Propagation Algorithm. The hidden to output weights can be learned by minimizing the error

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Artificial vs Biological Neural Networks: models and debates

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Transmission loss allocation using ANN

Transmission loss allocation using ANN. OBJECTIVES. To allocate loss in the transmission line To implement Artificial Neural Network(back propagation). Transmission line loss. Loss occurs due to current flowing in line.

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Branch Prediction with Neural-Networks: Hidden Layers and Recurrent Connections

Branch Prediction with Neural-Networks: Hidden Layers and Recurrent Connections. Andrew Smith CSE Dept. June 10, 2004. Outline. What is a Perceptron? Learning? What is a Feed-Forward Network? Learning? What is a Recurrent Network Learning? How to do it on hardware???

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CS 182 Sections 101 - 102

CS 182 Sections 101 - 102. bad puns alert!. Eva Mok (emok@icsi.berkeley.edu) Feb 11, 2004. (http://www2.hi.net/s4/strangebreed.htm). Announcements. a3 part 1 is due tonight (submit as a3-1) The second tester file is up, so pls. start part 2. The quiz is graded (get it after class).

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Back Propagation and Representation in PDP Networks

Back Propagation and Representation in PDP Networks. Psychology 209 February 6, 2013. Homework 4. Part 1 due Feb 13 Complete Exercises 5.1 and 5.2.

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Classification by Back Propagation

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A Self-Organized Network inspired by Immune Algorithm

A Self-Organized Network inspired by Immune Algorithm. 2002??????????????. M. Rahmat WIDYANTO?? ??? Hirota Laboratory Computational Intelligence & Systems Science Tokyo Institute of Technology. Contents. Theoretical Section Application Section

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