'Hidden layers' diaporamas de présentation

2007 NIPS Tutorial on: Deep Belief Nets

2007 NIPS Tutorial on: Deep Belief Nets. Geoffrey Hinton Canadian Institute for Advanced Research & Department of Computer Science University of Toronto. Some things you will learn in this tutorial.

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Supervised Learning Artificial Neural Networks Support Vector Machines

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Learning Deep Energy Models

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Neural Networks

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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.

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Geoffrey Hinton

CSC2535: Advanced Machine Learning Lecture 6a Convolutional neural networks for hand-written digit recognition. Geoffrey Hinton. Use many different copies of the same feature detector with different positions. Could also replicate across scale and orientation ( tricky and expensive)

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Introduction to Neural Networks

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

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Statistical Methods for Remote Sensing

Statistical Methods for Remote Sensing. Curt Mobley with much assistance from Wayne Slade, who will gladly answer all questions pertaining to neural networks. Maine 2007. Inverse Problems.

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Financial Time Series Forecasting by Neural Network

Financial Time Series Forecasting by Neural Network. Palash Soni Y7027272. What is a Time Series. A time series is a sequence of data points, measured typically at successive times spaced at uniform time intervals. Example:- The Dow Jones Industrial Average. Financial Time Series.

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Additional NN Models

Additional NN Models. Reinforcement learning (RL) Basic ideas: Supervised learning: (delta rule, BP) sample(x, f(s)) to learn f(.) precise error can be determined and is used to drive the learning. Unsupervised learning: (competitive, BM)

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Machine Learning: Connectionist

McCulloch-Pitts Neuron Perceptrons Multilayer Networks Support Vector Machines Feedback Networks Hopfield Networks. Machine Learning: Connectionist. Classification Pattern Recognition Memory Recall Prediction Optimization Noise Filtering. Uses. Input signals, x i Weights, w i

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Artificial Neural Networks

Artificial Neural Networks. An Introduction. Outline. Introduction Biological and artificial neurons Perceptrons (problems) Backpropagation network Training Other ANNs (examples in HEP). Introduction - What are ANNs?. Artificial Neural Networks:

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CS344: Introduction to Artificial Intelligence

CS344: Introduction to Artificial Intelligence. Pushpak Bhattacharyya CSE Dept., IIT Bombay Lecture 18,19: Sigmoid neuron 13 th and 14 th Feb , 2012. FFNN Example- XOR. = 0.5. w 1 =1. w 2 =1. ? Calculation of XOR. x 1 x 2. x 1 x 2. Calculation of . x 1 x 2. = 1. w 1 =-1.

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Dirty Deeds done Cheap

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Lesson 9 (Nov. 10)

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Re search G roup on I ntelligent M achines

National School of Engineers of Sfax. REsearch Group on Intelligent Machines. Re search G roup on I ntelligent M achines. A neural network model for complex handwriting generation movements.

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Advanced Computer Vision

Advanced Computer Vision. Lecture 05 Roger S. Gaborski. Artificial Neural Network Classifier. Multilayer Neural Network Backward Error Propagation MATLAB: command line GUI. Procedure. Collect data Create the network Configure the network Initialize the weights and biases

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