Download Artificial Neural Networks (ANN*s)

Artificial Neural Networks
(ANN’s)
Jacob Drilling
&
Justin Brown
What is an Artificial Neural Network?
• A computational model inspired by animals’
central nervous systems.
• Composed of connected processing nodes
(neurons).
• They are capable of machine learning and are
exceptional in pattern recognition.
• A Network is application specific.
History
•Warren McCulloch and Walter Pitts
• Threshold Logic
•Frank Rosenblatt
• Perceptron
•Marvin Minsky and Seymour Papert
• The Society of Mind Theory
•Paul Werbos
• Backpropagation
•David E. Rumelhart and James McClelland
Biological Neural Networks
• A human neuron has three parts: the cell
body, the axon and dendrites.
• The process of sending a signal...
Artificial Networks
● The Artificial model is comprised of many
processing nodes (neurons).
● Nodes are highly connected with weighted
paths.
● It has 3 layers:
○ Input
○ Hidden
○ Output
Artificial Networks
● Each node does its own
processing.
● Nodes output according to
their activation function.
● Initial weights are random.
● Back Propagation Algorithm
“teaches” by changing
weights.
Types
• Function
a. Feed Forward
b. Feed Back
• Structure
a. Bottleneck
b. Deep learning
Current Uses
• Recognition
• Image
• Speech
• Pattern
• Character
• Compression
• Image
• Audio/Video
• ALVINN - Driverless car
Feed Forward Algorithm
• Input -> Output
• Each neuron must
sum the weighted
products from the
previous layer.
• Output using
activation function.
Back Propagation
• Output -> Input
• Training Algorithm
• Calculates Error in the
output layer
• Propagates Error
backwards to change
weights
Criticism/Negative Aspects
• Large amounts of computing power and
storage are needed
• Cost efficiency
• Human abilities
• Instinct
• Logic
Character Recognition
1. Image Processing
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Character Recognition
2. Input data in the ANN
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N = 10
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Character Recognition
2. Input data in the ANN
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N = 10
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