ln020 - Artificial Neural Networks(ANNs Biologically...

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Artificial Neural Networks (ANNs) Biologically inspired computational model: (1) Simple computational units (neurons). (2) Highly interconnected - connectionist view (3) Vast parallel computation, consider: Human brain has ~10 11 neurons Slow computational units, switching time ~10 -3 sec (compared to the computer >10 -10 sec) Yet, you can recognize a face in ~10 -1 sec This implies only about 100 sequential, computational neuron steps - this seems too low for something as complicated as recognizing a face Parallel processing ANNs are naturally parallel - each neuron is a self-contained computational unit that depends only on its inputs.
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The Perceptron A simple, single layered neural “network” - only has a single neuron. However, even this simple neural network is already powerful enough to perform classification tasks. Chap 17 (Alex)
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Computational Unit Multiplication Sum Transfer Function Inputs Bias Output Weights Transfer Function: sgn( k
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This note was uploaded on 10/03/2011 for the course CSC 592 taught by Professor Staff during the Spring '11 term at Rhode Island.

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ln020 - Artificial Neural Networks(ANNs Biologically...

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