26-04-2012, 04:46 PM
Artificial Neural Networks
ann-intro (1).ppt (Size: 596 KB / Downloads: 45)
Neural networks to the rescue
Neural network: information processing paradigm inspired by biological nervous systems, such as our brain
Structure: large number of highly interconnected processing elements (neurons) working together
Like people, they learn from experience (by example)
Neural networks to the rescue
Neural networks are configured for a specific application, such as pattern recognition or data classification, through a learning process
In a biological system, learning involves adjustments to the synaptic connections between neurons
same for artificial neural networks (ANNs)
Where can neural network systems help
when we can't formulate an algorithmic solution.
when we can get lots of examples of the behavior we require.
‘learning from experience’
when we need to pick out the structure from existing data.
A simple perceptron
It’s a single-unit network
Change the weight by an amount proportional to the difference between the desired output and the actual output.
Learning
From experience: examples / training data
Strength of connection between the neurons is stored as a weight-value for the specific connection
Learning the solution to a problem = changing the connection weights
ann-intro (1).ppt (Size: 596 KB / Downloads: 45)
Neural networks to the rescue
Neural network: information processing paradigm inspired by biological nervous systems, such as our brain
Structure: large number of highly interconnected processing elements (neurons) working together
Like people, they learn from experience (by example)
Neural networks to the rescue
Neural networks are configured for a specific application, such as pattern recognition or data classification, through a learning process
In a biological system, learning involves adjustments to the synaptic connections between neurons
same for artificial neural networks (ANNs)
Where can neural network systems help
when we can't formulate an algorithmic solution.
when we can get lots of examples of the behavior we require.
‘learning from experience’
when we need to pick out the structure from existing data.
A simple perceptron
It’s a single-unit network
Change the weight by an amount proportional to the difference between the desired output and the actual output.
Learning
From experience: examples / training data
Strength of connection between the neurons is stored as a weight-value for the specific connection
Learning the solution to a problem = changing the connection weights