presented by:
Shikhir Kadian
Kapil Kanugo
Mohin Vaidya
Jai Balani

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Artificial Neural Networks
Introduction to Artificial Neural Networks
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
1. Pattern recognition (old friends, hand-written characters)
2. Content addressable recall
3. Approximate, common sense reasoning (driving, playing piano, baseball player)
• These tasks are often ill-defined, experience based, hard to apply logic
What is an (artificial) neural network?
A set of nodes (units, neurons, processing elements) 1.Each node has input and output
2.Each node performs a simple computation by its node function
• Weighted connections between nodes
• Connectivity gives the structure/architecture of the net
• What can be computed by a NN is primarily determined by the connections and their weights
• What can a ANN do?
• Compute a known function
• Approximate an unknown function
• Pattern Recognition
• Signal Processing
• Learn to do any of the above
Biological neural activity
• Each neuron has a body, an axon, and many dendrites
1. Can be in one of the two states: firing and rest.
2. Neuron fires if the total incoming stimulus exceeds the threshold
• Synapse: thin gap between axon of one neuron and dendrite of another.
1. Signal exchange
Synaptic strength/efficiency
Backpropagation Algorithm
• Training Set
A collection of input-output patterns that are used to train the network
• Testing Set
A collection of input-output patterns that are used to assess network performance
• Learning Rate-η
A scalar parameter, analogous to step size in numerical integration, used to set the rate of adjustments