Neural networks introduction


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Neural networks introduction

  1. 1. Neural Networks Introduction
  2. 2. Contents1. Introduction2. Biological Neural network3. Artificial Neural Network4. Comparison5. Benefits6. Neuron model7. Multilayer Neural Network8. Learning.9. Conclusion
  3. 3. Introduction Neural Network is a mathematical model of what goes in our mind to perform particular task or function. It is implemented using electronic components or simulated software. It is a system with many elements connected together.
  4. 4. Biological Neural Network Soma: body of the cell that houses the nucleus, in which the neurons main genetic information can be found Dendrites: receptivezones that receivemessages
  5. 5. Biological Neural Network (Cont,) Synapses are elementary structural and functional units that mediate the interactions between neurons. Axon: transmission line that sends messages.
  6. 6. Artificial Neural Network An artificial neural network (ANN) is a system composed of many simple processing elements (Neurons) operating in parallel whose function is determined by network structure, connection strengths (Weights), and the processing performed at computing element or nodes.
  7. 7. Difference Between Human Behavior and Neural Network BehaviorHuman Behavior Neural Network Behaviorremember certain things completely, Once we create a neural network, wepartially depend on capacity for train it to become expert in an area.learning.If we do not practice what we learned, Once fully trained, a neural net will notwe start to forget. forget.The first 10 processes may be If the results are repeatable it will beaccurate, but later we may start to accurate.make mistakes in the process. Faster inprocessing data and information
  8. 8. Benefits Nonlinearity: Perform operations that linear programming can’t. Fault tolerance: When one element fail NN continue without reduce parallelism. Adaptivity: NN has a capability to adapt their synaptic weights to changes in the surrounding environment. Contextual information: Every neuron in the network is potentially affected by the global activity of all other neurons
  9. 9. Neuron Model Neuron is a simple processing unit. The sole purpose of a Neuron is to receive electrical signals, accumulate them and see further if they are strong enough to pass forward. Single neuron is useless. It is the complex connection between them (weights) which makes brains capable of thinking and having a sense of consciousness.
  10. 10. Neuron Model (Cont,) Neuron Consist of: Inputs (Synapses): input signal. Weights (Dendrites): determines the importance of incoming value. Output (Axon): output to other neuron or of NN.
  11. 11. Neuron Model (Cont,) Output: is calculated by inputs which are multiplied by weights, and then computed by a mathematical function which determines the activation of the neuron. Activation Function:
  12. 12. Neuron Model (Cont,) Example: Output when single perceptron (neuron) is used. input output 00 0 01 1 10 1 11 1 The dark blue dotsrepresents values of trueand the light blue dotrepresents a value ofFalse.
  13. 13. Neuron Model (Cont,) Example: Output when 2 perceptron (neuron) is used.
  14. 14. Multilayer Neural Network This network is feed-forward, means the values are propagated in one direction only. Input layer: takes the inputs and forwards it to hidden layer Middle layer: Without this layer, network would not be capable ofsolving complex problems. Output layer: This layerconsists of neurons which outputthe result Weights: for every neuron thereAre weights that for every inputTo it.
  15. 15. Learning updating network architecture and connection weights so that network can efficiently perform a task.Basic Learning Procedure run an input pattern through the function calculate the error (desired value – actual value) update the weights according to learning rate and error move onto next patternOverfittingOccure when NN memorize patterns and loose the ability of generalization. Problem is when to stop learning
  16. 16. Learning Paradigm Supervised The correct answer is provided for the network for every input pattern Weights are adjusted regarding the correct answer. Unsupervised Does not need the correct output the system itself recognize the correlation and organize patterns into categories accordingly. Hybrid A combination of supervised and unsupervised, Some of the weights are provided with correct output while the others are automatically corrected.
  17. 17. Conclusion Neural Network is a modeling for human brain Neuron is the basic unit of NN To adapt NN to perform operation we want, it has to be trained Most practical form of NN is the one that has multilayer Try to avoid overfitting
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