'Linear activation function' diaporamas de présentation

Kasin Prakobwaitayakit Department of Electrical Engineering Chiangmai University

EE749 Neural Networks An Introduction. Kasin Prakobwaitayakit Department of Electrical Engineering Chiangmai University. Background. - Neural Networks can be : - Biological models - Artificial models - Desire to produce artificial systems capable of

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Neural Networks

Neural Networks. Pattern Recognition. Humans are very good at recognition. It is easy for us to identify the Dalmatian dog in the image This recognition capability would be very difficult to implement in a program. Biological Neurons.

1.23k views • 93 slides

Neural Networks

Neural Networks. Pattern Recognition. Humans are very good at recognition. It is easy for us to identify the Dalmatian dog in the image This recognition capability would be very difficult to implement in a program. Biological Neurons.

1.1k views • 90 slides

Artificial Intelligence Chapter 20.5: Neural Networks

Artificial Intelligence Chapter 20.5: Neural Networks. Michael Scherger Department of Computer Science Kent State University. Contents. Introduction Simple Neural Networks for Pattern Classification Pattern Association Neural Networks Based on Competition Backpropagation Neural Network.

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Analyzing OS Fingerprints using Neural Networks and Statistical Machinery

Analyzing OS Fingerprints using Neural Networks and Statistical Machinery. Javier Burroni - Carlos Sarraute Core Security Technologies EUSecWest/core06 conference. OUTLINE. 1. Introduction 2. DCE-RPC Endpoint mapper 3. OS Detection based on Nmap signatures 4. Dimension reduction and training.

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Using Neural Networks for remote OS Identification

Using Neural Networks for remote OS Identification. Javier Burroni - Carlos Sarraute Core Security Technologies PacSec/core05 conference. OUTLINE. 1. Introduction 2. DCE-RPC Endpoint mapper 3. OS Detection based on Nmap signatures 4. Dimension reduction and training.

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Radial-Basis Function Networks

Radial-Basis Function Networks. A function is radial basis (RBF) if its output depends on (is a non-increasing function of) the distance of the input from a given stored vector. RBFs represent local receptors, as illustrated below, where each green point is a stored vector used in one RBF.

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Personal Recognition Using Hand Shape and Texture

Personal Recognition Using Hand Shape and Texture. Ajay Kumar , Member, IEEE , and David Zhang , Senior Member, IEEE. Introdution. This paper proposes a new bimodal biometric system using feature-level fusion of hand shape and palm texture . Fingerprint iris palmprint voice

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Advanced information retreival

Advanced information retreival. Chapter 02: Modeling - Neural Network Model. Neural Network Model. A neural network is an oversimplified representation of the neuron interconnections in the human brain: nodes are processing units edges are synaptic connections

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Derivation of a Learning Rule for Perceptrons

Neural Networks. Single Layer Perceptrons. x 1. w k 1. x 2. w k 2. ?. w km. x m. Derivation of a Learning Rule for Perceptrons. Adaline (Adaptive Linear Element). Widrow [1962]. Goal:. Neural Networks. Single Layer Perceptrons. Least Mean Squares (LMS).

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Derivation of a Learning Rule for Perceptrons

Neural Networks. Single Layer Perceptrons. x 1. w k 1. x 2. w k 2. ?. w km. x m. Derivation of a Learning Rule for Perceptrons. Adaline (Adaptive Linear Element). Widrow [1962]. Goal:. Neural Networks. Single Layer Perceptrons. Least Mean Squares (LMS).

312 views • 19 slides

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.

254 views • 15 slides