advanced learning methodologies
TRANSCRIPT
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ADVANCED LEARNING METHODOLOGIES
IN
ARTIFICIAL NEURAL NETWORKS
P gnana deep
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ADVANCED LEARNING METHODOLOGIESIN
ARTIFICIAL NEURAL NETWORKS
The Incredible Brain.
Mechanical Revolutions.
The Computer. Thinking Process.
Imitating the Brain.
Artificial neural Networks.
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THE BIOLOGICAL NEURON
Brain-the most Complex Machine. Neurons-Chief Components.
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THE BIOLOGICAL NEURON
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THE BIOLOGICAL NEURON
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THE NEURON MODEL
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ARTIFICIAL NEURON MODELS
The McCulloh Pitts Model 1943.
No Learning Algorithm.
Frank Rosenblatts PERCEPTRON.
A Pattern Classification System.
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A PERCEPTRON MODEL
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DISTINGUISHING FEATURES
Pattern Extraction & Trends Detection.
Adaptive learning.
Self-Organization. Real Time Operation.
Fault Tolerance via Redundant
Information Coding.
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LEARNING ASPECTS
The ability to Recognize.
1,2,3 --- Group A.
7,8,9 --- Group B.
Number 4 is Identified as Closer to Group A.
Number 6 is Identified as Closer to Group B.
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ADVANCED LEARNING METHODOLOGIES
Supervised Learning
Unsupervised Learning
Reinforced Learning Competitive Learning
Widrow-Hoff Learning
Hebbian Learning
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SUPERVISED LEARNING
The Biological TEACHER.
The Target
Readjustment Zero Error
APPLICATIONS-Pattern Recognition,
Speech & Gesture recognition.
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REINFORCED LEARNING
The BINARY TEACHER.
Pass/Fail Condition.
If Fail Readjust & Try again.
APPLICATIONS Control Problems, Games.
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COMPETITIVE LEARNING
Several Neurons at the Output Layer.
Inter-Neuron Competition.
Each output Neuron is trained torespond tom a different input Stimulus.
APPLICATIONS Artificial Limbs,
Advanced Cars.
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WIDROW-HOFF LEARNING &
HEBBIAN LEARNING
Continuous adjustments of the weights.
Degree of Correlativity.
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THE WORLDS FIRST BIONIC WOMAN
A NeuroControlled
Bionic Arm that can
be moved just by
Thinking about it.
Natural movement,
greater range of motion
and restores lostfunction.
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CONCLUSION
Development of Physical Systems that areCompatible with the Methodologies.
Increasing the Speed and Iteration rate.
Advancements in Mechatronics.
A Promising Future.
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THANK YOU