Chapter 3 ARTIFICIAL NEURAL NETWORKS

May 12, 2013

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2. Outline. 1. Introduction 2. ANN representations3. Perceptron Training4. Multilayer networks and Backpropagation algorithm5. Remarks on the Backpropagation algorithm6. Neural network application development7. Benefits and limitations of ANN8. ANN Applications. 3. INTRODUCTION Biological Motivation.

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Chapter 3 ARTIFICIAL NEURAL NETWORKS

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Artificial Neural Networks

Artificial Neural Networks. Michael Prestia COT 4810 March 18, 2008. The Human Brain. Composed of neurons Neurons send signals to each other. Neurons. Neurons store and transmit information Neurons send messages to one another through a synapse.

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Chapter 7 Artificial Neural Networks

Chapter 7 Artificial Neural Networks. [Artificial] Neural Networks. A class of powerful, general-purpose tools readily applied to: Prediction Classification Clustering Biological Neural Net (human brain) is the most powerful – we can generalize from experience

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Chapter 4: Artificial Neural Networks

Chapter 4: Artificial Neural Networks. Artificial neural network(ANN) General, practical method for learning real-valued, discrete-valued, vector-valued functions from examples BACPROPAGATION 알고리즘 Use gradient descent to tune network parameters to best fit a training set of input-output pairs

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Artificial Neural Networks

Artificial Neural Networks. Torsten Reil torsten.reil@zoo.ox.ac.uk. Outline. What are Neural Networks? Biological Neural Networks ANN – The basics Feed forward net Training Example – Voice recognition Applications – Feed forward nets Recurrency Elman nets Hopfield nets

818 views • 47 slides

Artificial Neural Networks

Artificial Neural Networks. Introduction. Artificial Neural Networks (ANN) Information processing paradigm inspired by biological nervous systems ANN is composed of a system of neurons connected by synapses ANN learn by example Adjust synaptic connections between neurons. History.

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Chapter 7 Artificial Neural Networks

Chapter 7 Artificial Neural Networks. [Artificial] Neural Networks. A class of powerful methods readily applied to: Prediction Classification Clustering Biological Neural Net (human brain) is the most powerful – we can generalize/learn from experience

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Artificial Neural Networks

Artificial Neural Networks. Artificial Neural Networks. The next intelligence. DAYANAND & ABHISHEK ECE [2-1] CMR Engineering College. ABSTRACT. Neural Networks was a result of the discovery of new techniques and developments i n computer hardware technology.

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Artificial Neural Networks

Artificial Neural Networks. Introduction Design of Primitive Units Perceptrons The Backpropagation Algorithm Advanced Topics. Basics. In contrast to perceptrons, multilayer networks can learn not only multiple decision boundaries, but the boundaries may be nonlinear. Output nodes.

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Artificial Neural Networks

Artificial Neural Networks. Torsten Reil torsten.reil@zoo.ox.ac.uk. Example: Voice Recognition. Task: Learn to discriminate between two different voices saying “Hello” Data Sources Steve Simpson David Raubenheimer Format Frequency distribution (60 bins) Analogy: cochlea.

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Artificial Neural Networks

Artificial Neural Networks. Commercial ANNs.

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Artificial neural networks

Artificial neural networks. Ricardo Ñanculef Alegría Universidad Técnica Federico Santa María Campus Santiago. Learning from Natural Systems. Bio-inspired systems Ants colony Genetic algorithms Artificial neural networks The power of the brain Examples: vision, text-processing

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Artificial Neural Networks

Artificial Neural Networks. INTRODUCTION. As we have noted, a glimpse into the natural world reveals that even a small child is able to do numerous tasks at once.

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Artificial Neural Networks

Artificial Neural Networks. Kong Da, Xueyu Lei & Paul McKay. Neural Networks. What is Neural Networks. An Artificial Neural Network is a computational simulation of a biological neural network. composed of a large number of highly interconnected processing elements(neurons )

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Artificial Neural Networks

Artificial Neural Networks. Threshold units Gradient descent Multilayer networks Backpropagation Hidden layer representations Example: Face recognition Advanced topics. Connectionist Models. Consider humans Neuron switching time ~.001 second Number of neurons ~10 10

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Artificial Intelligence Chapter 3 Neural Networks

Artificial Intelligence Chapter 3 Neural Networks. Biointelligence Lab School of Computer Sci. & Eng. Seoul National University. Outline. 3.1 Introduction 3.2 Training Single TLUs Gradient Descent Widrow-Hoff Rule Generalized Delta Procedure 3.3 Neural Networks

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Chapter 3 ARTIFICIAL NEURAL NETWORKS

Chapter 3 ARTIFICIAL NEURAL NETWORKS. HCMC University of Technology Sep. 2008. Outline. 1. Introduction 2. ANN representations 3. Perceptron Training 4. Multilayer networks and Backpropagation algorithm 5. Remarks on the Backpropagation algorithm

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Artificial Neural Networks. The Brain Brain vs. Computers The Perceptron Multilayer networks Some Applications. Artificial Neural Networks. Other terms/names connectionist parallel distributed processing neural computation adaptive networks.. History

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Artificial Neural Networks. Artificial Neural Networks. The basic idea in neural nets is to define interconnected networks of simple units (let's call them "artificial neurons") in which each connection has a weight. Weight w ij is the weight of the i th input into unit j .

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Artificial Neural Networks

Artificial Neural Networks. An Overview and Analysis. Modeling the Human Brain. Artificial Neural Networks concentrate on imitating humans rather than acting as rational agents. The goal in ANNs is to imitate the learning that takes place in the brain.

251 views • 17 slides

Artificial Intelligence Chapter 3 Neural Networks

Artificial Intelligence Chapter 3 Neural Networks. Outline. 3.1 Introduction 3.2 Training Single TLUs Gradient Descent Widrow-Hoff Rule Generalized Delta Procedure 3.3 Neural Networks The Backpropagation Method Derivation of the Backpropagation Learning Rule

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Artificial Intelligence Chapter 3 Neural Networks

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