'Hidden units' diaporamas de présentation

Review Questions

Review Questions. 1. Minimalism is A) little makes lots B) lots make little C) small ensemble D) electronic. 2. Process music maker A) Glen Ford B) Steve Reich C) Iannis Xenakis D) Don Rickles. 3. Procedural music is A) fast B) algorithmic C) slow D) microphonic.

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Smart Home Technologies

1.45k views • 74 slides

Smart Home Technologies

938 views • 74 slides

An overview of deep learning

An overview of deep learning. Geoffrey Hinton University of Toronto & Canadian Institute for Advanced Research & G o o g l e. A brief history of deep neural networks. How to recognize patterns: First convert raw input into feature activations.

1.05k views • 69 slides

Training Neural Networks

Training Neural Networks. Robert Turetsky Columbia University rjt72@columbia.edu Systems, Man and Cybernetics Society IEEE North Jersey Chapter December 12, 2000. Objective. Introduce fundamental concepts in Artificial Neural Networks Discuss methods of training ANNs

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The next generation of neural networks

1. The next generation of neural networks. Geoffrey Hinton Canadian Institute for Advanced Research & University of Toronto. 2. The main aim of neural networks. People are much better than computers at recognizing patterns. How do they do it?

1.11k views • 47 slides

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 ???? Use gradient descent to tune network parameters to best fit a training set of input-output pairs

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Neural Networks

Neural Networks. Background. - Neural Networks can be : - Biological models - Artificial models - Desire to produce artificial systems capable of sophisticated computations similar to the human brain. Biological analogy and some main ideas.

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Stages in NN development

Stages in NN development. Specification. What is the problem we are trying to tackle? What is the performance required? Do you need an artificial neural network? i.e. will a simple statistical technique such as regression give acceptable performance?. Data.

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Chapter 6: Backpropagation Nets

Chapter 6: Backpropagation Nets. Architecture : at least one layer of non-linear hidden units Learning : supervised, error driven, generalized delta rule Derivation of the weight update formula (with gradient descent approach) Practical considerations Variations of BP nets Applications.

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Boltzman Machines

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Artificial Neural Networks

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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For Friday

For Friday. No reading Take home exam due Exam 2. For Monday. Read chapter 22, sections 1-3 FOIL exercise due. Model Neuron (Linear Threshold Unit). Neuron modelled by a unit ( j ) connected by weights, w ji , to other units ( i ): Net input to a unit is defined as:

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CSC 2535 Lecture 8 Products of Experts

CSC 2535 Lecture 8 Products of Experts. Geoffrey Hinton. How to combine simple density models. mixing proportion. Suppose we want to build a model of a complicated data distribution by combining several simple models. What combination rule should we use?

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Multi Layer NN and Bit-True Modeling of These Networks SILab presentation Ali Ahmadi

Multi Layer NN and Bit-True Modeling of These Networks SILab presentation Ali Ahmadi September 2007. Outline. Review structures of Single Layer Neural Networks Introduction to Multi Layer Perceptron (MLP) Neural Network Error Back-Propagation Learning Algorithm

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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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Dougal Sutherland, 9/25/13

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The Pentium Goes to Vegas

The Pentium Goes to Vegas. Training a Neural Network to Play BlackJack. Paul Ruvolo and Christine Spritke. Goals. Investigate result based learning Develop strategy for a highly random game Train network to play effectively without explicitly teaching the rules of the game. Strategy.

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CSC321: 2011 Introduction to Neural Networks and Machine Learning Lecture 7: Learning in recurrent networks

CSC321: 2011 Introduction to Neural Networks and Machine Learning Lecture 7: Learning in recurrent networks. Geoffrey Hinton. Recurrent networks. A feed-forward neural net just computes a fixed sequence of non-linear learned transformations to convert an input patter into an output pattern.

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Minds and Machines

Minds and Machines. Summer 2011 Tuesday, 8/2.

263 views • 15 slides