CAP4630 : Neural Networks

Jul 26, 2012

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Outline. NN HistoryAn overview of NNNetwork architecturesNeuronsLearning algorithmsApplications. Introduction . Why ANNSome tasks can be done easily (effortlessly) by humans but are hard by conventional paradigms on Von Neumann machine with algorithmic approachPattern recognition (old friends

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CAP4630 : Neural Networks

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1. CAP4630 : Neural Networks

Artificial Neural Networks and AI

Artificial Neural Networks and AI. Artificial Neural Networks provide… A new computing paradigm A technique for developing trainable classifiers, memories, dimension-reducing mappings, etc A tool to study brain function. Converging Frameworks.

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Neural Networks

Neural Networks. Mundhenk and Itti , 2008. What are neural networks. Neural Networks are diverse and do many things Some are meant to solve AI problems such as classification. Some are meant to simulate the workings of the brain and the nervous system for biological research. .

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Artificial Neural Networks

Artificial Neural Networks. Dan Simon Cleveland State University. Neural Networks. Artificial Neural Network (ANN) : An information processing paradigm that is inspired by biological neurons

2.14k views • 44 slides

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

695 views • 35 slides

Recurrent neural networks (I)

Recurrent neural networks (I). Architectures Totally recurrent networks Partially recurrent networks Dynamics of recurrent networks Continuous time dynamics Discrete time dynamics Associative memories Solving optimization problems. Recurrent neural networks . Architecture

645 views • 34 slides

Neural Networks

Neural Networks. Teacher: Elena Marchiori R4.47 elena@cs.vu.nl. Assistant: Kees Jong S2.22 cjong@cs.vu.nl. Neural Net types. Basics of neural network theory and practice for supervised and unsupervised learning. Most popular Neural Network models : architectures

548 views • 36 slides

Introduction to Neural Networks

Introduction to Neural Networks. What are connectionist neural networks?. Connectionism refers to a computer modeling approach to computation that is loosely based upon the architecture of the brain Many different models, but all include:

723 views • 46 slides

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?.

945 views • 24 slides

Learning with Neural Networks

Learning with Neural Networks. Artificial Intelligence CMSC 25000 February 19, 2002. Agenda. Neural Networks: Biological analogy Review: single-layer perceptrons Perceptron: Pros & Cons Neural Networks: Multilayer perceptrons Neural net training: Backpropagation Strengths & Limitations

605 views • 24 slides

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

509 views • 40 slides

Neural Networks in Data Mining “An Overview”

Neural Networks in Data Mining “An Overview”. Mahdi Nasereddin Ph.D. Pennsylvania State University School of Information Sciences and Technology. Agenda. Introduction Data Mining Techniques Neural Networks for Data Mining? Neural Networks Classification Neural Networks Prediction

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Neural Networks

Neural Networks. William Lai Chris Rowlett. What are Neural Networks?. A type of program that is completely different from functional programming. Consists of units that carry out simple computations linked together to perform a function

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Neural Networks in Financial Analysis

Neural Networks in Financial Analysis. By Rajesh Amradi (08005041) Nikhil Prakash (09026015) Under the guidance of Prof. Pushpak Bhattacharya. Outline. Introduction to Neural Networks Neural Networks in Finance Time Series Analysis Stock Market Analysis Capital Budgeting and Risk

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Neural Networks Primer

Neural Networks Primer. Dr Bernie Domanski The City University of New York / CSI 2800 Victory Blvd 1N-215 Staten Island, New York 10314 drbernie@optonline.net http://domanski.cs.csi.cuny.edu. What is a Neural Network?. Artificial Neural Networks – (ANN)

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Neural networks

Neural networks. Introduction Fitting neural networks Going beyond single hidden layer Brief discussion of deep learning. Neural network. K-class classification: K nodes in top layer Continuous outcome: Single node in top layer. Neural network. K-class classification.

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Artificial Neural Networks

Artificial Neural Networks. Biointelligence Laboratory Department of Computer Engineering Seoul National University. Contents. Introduction Perceptron and Gradient Descent Algorithm Multilayer Neural Networks Designing an ANN for Face Recognition Application. Introduction.

961 views • 64 slides

WK1 - Introduction

WK1 - Introduction. CS 476: Networks of Neural Computation WK1 – Introduction Dr. Stathis Kasderidis Dept. of Computer Science University of Crete Spring Semester, 2009. Contents. Course structure and details Basic ideas of Neural Networks Historical development of Neural Networks

574 views • 36 slides

Neural Networks

Neural Networks. Marcel Jiřina. Introduction. Neural networks and their use to classification and other tasks ICS AS CR Theoretical computer science Neural networks , genetic alg. and n onlinear methods Numeric algorithms .. 1 mil. eq. Fuzzy sets, approximate reasoning, possibility th.

723 views • 23 slides

Neural networks

Neural networks. Neural networks. Neural networks are made up of many artificial neurons. Each input into the neuron has its own weight associated with it illustrated by the red circle.

849 views • 50 slides

God

God. NEURAL NETWORKS. M. Alborzi, Ph. D. Petroleum University of Technology October, 2001. OUTLINE. Neural Networks Defined Why Neural Networks Pattern Recognition Neural Networks Application Areas A Brief History of Neural Networks Training Neural Networks

666 views • 25 slides

Intro to Neural Networks

Supervised Learning: Perceptrons and Backpropagation. Intro to Neural Networks. Neural Network ==. Connectionist /ism== Parallel Distributed Processing (PDP). Neural Networks assume. Intelligence is emergent. 1943 - McCullough Pitts Artificial Neuron.

736 views • 55 slides

PREDICTING INTERPLANETARY SHOCKS USING NEURAL NETWORKS

PREDICTING INTERPLANETARY SHOCKS USING NEURAL NETWORKS. A.İbrahim PALA METU Aerospace Engineering Department Senior Student 21.12.2005. OUTLINE. WHAT IS AN INTERPLANETARY SHOCK? WHAT IS A NEURAL NETWORK? HOW THE NEURAL NETWORKS ARE USED TO PREDICT THE INTERPLANETARY SHOCKS? CONCLUSION.

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