lec03-NeuralNetwork

lec03-NeuralNetwork - Artificial Neural Networks Artificial...

Info iconThis preview shows pages 1–7. Sign up to view the full content.

View Full Document Right Arrow Icon
CS464 Introduction to Machine Learning 1 Artificial N eural N etworks Artificial neural networks (ANNs) provide a general, practical method for learning real-valued, discrete-valued, and vector-valued functions from examples. Algorithms such as BACKPROPAGATION gradient descent to tune network parameters to best fit a training set of input-output pairs. ANN learning is robust to errors in the training data and has been successfully applied to problems such as interpreting visual scenes, speech recognition, and learning robot control strategies.
Background image of page 1

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
CS464 Introduction to Machine Learning 2 Biological Motivation The study of artificial neural networks (ANNs) has been inspired in part by the observation that biological learning systems are built of very complex webs of interconnected neurons. Artificial neural networks are built out of a densely interconnected set of simple units, where each unit takes a number of real-valued inputs (possibly the outputs of other units) and produces a single real-valued output (which may become the input to many other units). The human brain is estimated to contain a densely interconnected network of approximately 10 11 neurons, each connected, on average, to 10 4 others. Neuron activity is typically inhibited through connections to other neurons.
Background image of page 2
CS464 Introduction to Machine Learning 3 ALVINN – Neural Network Learning To Steer An Autonomous Vehicle
Background image of page 3

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
CS464 Introduction to Machine Learning 4 Properties of Artificial Neural Networks A large number of very simple, neuron-like processing elements called units , A large number of weighted, directed connections between pairs of units Weights may be positive or negative real values Local processing in that each unit computes a function based on the outputs of a limited number of other units in the network Each unit computes a simple function of its input values, which are the weighted outputs from other units. If there are n inputs to a unit, then the unit's output, or activation is defined by a = g((w1 * x1) + (w2 * x2) + . .. + (wn * xn)). Each unit computes a (simple) function g of the linear combination of its inputs. Learning by tuning the connection weights
Background image of page 4
CS464 Introduction to Machine Learning 5 Appropriate Problems for NN Learning Instances are represented by many attribute-value pairs. The target function output may be discrete-valued, real-valued, or a vector of several real-valued or discrete-valued attributes. The training examples may contain errors. Long training times are acceptable. Fast evaluation of the learned target function may be required. The ability of humans to understand the learned target function is not important.
Background image of page 5

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
CS464 Introduction to Machine Learning 6 Perceptron                          1    if  Σ i=0 n   w i  x >0 o(x ,…,x n )=                         -1    otherwise { Σ x 1 x 2 x n .
Background image of page 6
Image of page 7
This is the end of the preview. Sign up to access the rest of the document.

This note was uploaded on 12/27/2009 for the course CS 464 taught by Professor Demir during the Fall '08 term at Bilkent University.

Page1 / 39

lec03-NeuralNetwork - Artificial Neural Networks Artificial...

This preview shows document pages 1 - 7. Sign up to view the full document.

View Full Document Right Arrow Icon
Ask a homework question - tutors are online