Introduction to Neural Networks and Example Applications in HCI Nick Gentile

Apr 14, 2012

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Overview. Introduction to neural networks Example Applications in HCINETtalkCognitive Text-editingFiltering MessagesLearning a User's Interests by Observing their behaviorDiscussion. Neural Networks. The brainCerebral cortexMemory, perceptual awareness, thinking, language and consciousness

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Introduction to Neural Networks and Example Applications in HCI Nick Gentile

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Introduction to Neural Networks

Introduction to Neural Networks. http://playground.tensorflow.org/. Outline. Perceptrons Perceptron update rule Multi-layer neural networks Training method Best practices for training classifiers After that: convolutional neural networks. Recall: “Shallow” recognition pipeline.

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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:

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Introduction to Neural Networks

Introduction to Neural Networks. Neural Networks in the Brain. Human brain “computes” in an entirely different way from conventional digital computers. The brain is highly complex, nonlinear, and parallel.

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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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Neural Networks Applications

Neural Networks Applications. V ersatile learners that can be applied to nearly any learning task: classification numeric prediction unsupervised pattern recognition

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Introduction to Neural Networks

Pattern Recognition and Machine Learning 2006. Introduction to Neural Networks. Debrup Chakraborty. To be covered today. Introduction Perceptron Algorithm Multilayered Perceptrons. HAYKIN , S., "Neural Networks: A Comprehensive Foundation," Prentice Hall, Upper Saddle River, NJ, 1999.

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Introduction to Neural Networks

Introduction to Neural Networks. John Paxton Montana State University Summer 2003. Chapter 3: Pattern Association. Aristotle’s observed that human memory associates similar items contrary items items close in proximity items close in succession (a song). Terminology and Issues.

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Introduction to Neural Networks

Introduction to Neural Networks. Freek Stulp. Overview. Biological Background Artificial Neuron Classes of Neural Networks Perceptrons Multi-Layered Feed-Forward Networks Recurrent Networks Conclusion. Biological Background. Neuron consists of: Cell body Dendrites Axon Synapses.

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Introduction to Neural Networks

Introduction to Neural Networks. John Paxton Montana State University Summer 2003. Chapter 2: Simple Neural Networks for Pattern Classification. x 0. 1. w 0. w 0 is the bias f(y in ) = 1 if y in >= 0 f(y in ) = 0 otherwise ARCHITECTURE. y. w 1. x 1. w n. x n. Representations.

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Introduction To Neural Networks

Introduction To Neural Networks. Prof. George Papadourakis, Ph.D. Part I Introduction and Architectures. Introduction To Neural Networks.

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Introduction to Neural Networks

Introduction to Neural Networks. Gianluca Pollastri, Head of Lab School of Computer Science and Informatics and Complex and Adaptive Systems Labs University College Dublin gianluca.pollastri@ucd.ie. Credits. Geoffrey Hinton, University of Toronto.

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Introduction to Neural Networks

Introduction to Neural Networks. Recurrent Neural Networks. Recurrent Neural Networks. Recurrent neural networks are neural networks with feedback loops. Why recurrent neural networks? – A new approach to problem solving (via neurodynamics).

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Introduction to Neural Networks

適応システム理論ガイダンス. Introduction to Neural Networks. Kenji Nakayama Kanazawa University, JAPAN. PPT ファイルの入手方法 http://leo.ec.t.kanazawa-u.ac.jp/~nakayama/ Education 授業科目　 Lecture Subjects 適応システム理論平成２２年度　ガイダンス PPT ファイル. Neural Networks. Network Structures Multi-layer Network

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Introduction to Neural Networks

Introduction to Neural Networks. Simon Durrant Quantitative Methods December 15th. A Typical Artificial Neural Network. A neural network consists of neurons connected by weights . Inputs get multiplied by the weights and summed before entering neurons.

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Introduction to Neural Networks

Introduction to Neural Networks. Marina Brozovi ć. References: talks by Matyas Bartok; Imperial College Goeffrey Hinton, U of Toronto Diana Spears, U of Wayoming

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Introduction to Neural Networks

Introduction to Neural Networks. John Paxton Montana State University Summer 2003. Chapter 5: Adaptive Resonance Theory. 1987, Carpenter and Grossberg ART1: clusters binary vectors ART2: clusters continuous vectors. General.

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Introduction to Neural Networks

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Introduction to Neural Networks

Introduction to Neural Networks. Chapter 1 Introduction. Introduction. Why ANN Some tasks can be done easily (effortlessly) by humans but are hard by conventional paradigms on Von Neumann machine with algorithmic approach Pattern recognition (old friends, hand-written characters)

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Introduction to Neural Networks

Introduction to Neural Networks. John Paxton Montana State University Summer 2003. Textbook. Fundamentals of Neural Networks: Architectures, Algorithms, and Applications Laurene Fausett Prentice-Hall 1994. Chapter 1: Introduction. Why Neural Networks? Training techniques exist.

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Introduction to Neural Networks

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