'Input layer' diaporamas de présentation

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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Lecture 11

Lecture 11. Hybrid intelligent systems: Neural expert systems and neuro-fuzzy systems. Introduction Neural expert systems Neuro-fuzzy systems ANFIS: Adaptive Neuro-Fuzzy Inference System Summary. Introduction.

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Learning and Perceptrons

Learning and Perceptrons. CIS 479/579 Bruce R. Maxim UM-Dearborn. Momentum and Friction. When human players use a mouse to aim Momentum turns the view more than they expect for large angles (the ballistic mouse thing) Friction slows down the turn for small angles

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Bioinspired Computing Lecture 4

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Spatial Analysis for Resource Management

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S tock T rend P rediction using Neural N etworks

S tock T rend P rediction using Neural N etworks. Seminar Presentation Marco Witzmann (11V101009) Ravi Vishvakarma (09005075) Rishabh Singhal (09005005) Supervisor Prof. Pushpak Bhattacharyya. Outline. Introduction Price Fluctuation Classical Methods Fundamental Analysis

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Lecture 8

Lecture 8. Artificial neural networks: Unsupervised learning. Introduction Hebbian learning Generalised Hebbian learning algorithm Competitive learning Self-organising computational map: Kohonen network Summary. Introduction.

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Will neural network work for my problem? Character recognition neural networks Prediction neural networks

Lecture 14. Knowledge engineering: Building neural network based systems. Will neural network work for my problem? Character recognition neural networks Prediction neural networks Classification neural netrorks with competitiv e learning Summary.

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Data Mining - CSE5230

CSE5230/DMS/2004/7. Data Mining - CSE5230. Classifiers 3 Feedforward Neural Networks. Lecture Outline. Why study neural networks? What are neural networks and how do they work? History of artificial neural networks (ANNs) Applications and advantages Choosing and preparing data

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Artificial Neural Networks

Artificial Neural Networks. Outline. Biological Motivation Perceptron Gradient Descent Least Mean Square Error Multi-layer networks Sigmoid node Backpropagation. Biological Neural Systems. Neuron switching time : > 10 -3 secs Number of neurons in the human brain: ~10 10

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Nonlinear Empirical Models

Nonlinear Empirical Models. Neural Network Models of Process Behaviour. generally modeling input-output behaviour empirical models - no attempt to model physical structure estimated from plant data. Neural Networks. structure motivated by physiological structure of brain

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Unsupervised learning (II)

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Linear Separability

3. 3. 3. x 2. x 2. x 2. 2. 2. 2. 1. 1. 1. -3. -3. -3. -2. -2. -2. -1. -1. -1. 1. 1. 1. 2. 2. 2. 3. 3. 3. x 1. x 1. x 1. -1. -1. -1. -2. -2. -2. -3. -3. -3. Linear Separability. Input space in the two-dimensional case (n = 2):. 1. 1. 1. 0. 0. 0.

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Image Compression Using Neural Networks

Image Compression Using Neural Networks. Vishal Agrawal (Y6541) Nandan Dubey (Y6279). Overview. Introduction to n eural networks Back Propagated (BP) neural network Image compression using BP neural network Comparison with existing image compression techniques. What is a Neural Network?.

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Introduction to Neural Networks

Introduction to Neural Networks. John Paxton Montana State University Summer 2003. Chapter 6: Backpropagation. 1986 Rumelhart, Hinton, Williams Gradient descent method that minimizes the total squared error of the output. Applicable to multilayer, feedforward, supervised neural networks.

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COGNITIVE NEUROSCIENCE

COGNITIVE NEUROSCIENCE. Note. Please read book to review major brain structures and their functions Please read book to review brain imaging techniques See also additional slides available on class website. Cognitive Neuroscience.

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T MVA

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Self-Organizing Maps (Kohonen Maps)

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Radial Basis Functions

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Lecture 9 MLP (I): Feed-forward Model

Lecture 9 MLP (I): Feed-forward Model. Outline. Multi-Layer Perceptron Structure Feed Forward Model XOR Example MLP Applications. Multi-Layer Perceptron Structure . A Three Layer Feed-forward Multi-Layer Perceptron. Two Layer Perceptron XOR Gate . Decision Boundaries of XOR.

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