what is neural network....???
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what is neural network....??? Presentation Transcript

  • 1. Introduction to Neural Networks Presented by: Hafiz Syed Adnan Ahmed
  • 2. Introduction • Artificial Neural Network is based on the biological nervous system as Brain • It is composed of interconnected computing units called neurons • ANN like human, learn by examples
  • 3. Why Artificial Neural Networks? There are two basic reasons why we are interested in building artificial neural networks (ANNs): • Technical viewpoint: Some problems such as character recognition or the prediction of future states of a system require massively parallel and adaptive processing. • Biological viewpoint: ANNs can be used to replicate and simulate components of the human (or animal) brain, thereby giving us insight into natural information processing. 3
  • 4. Science: Model how biological neural systems, like human brain, work? • How do we see? • How is information stored in/retrieved from memory? • How do you learn to not to touch fire? • How do your eyes adapt to the amount of light in the environment? • Related fields: Neuroscience, Computational Neuroscience, Psychology, Psychophysiology, Cognitive Science, Medicine, Math, Physics. 4
  • 5. Real Neural Learning • Synapses change size and strength with experience. • Hebbian learning: When two connected neurons are firing at the same time, the strength of the synapse between them increases. • “Neurons that fire together, wire together.” 5
  • 6. Biological Neurons • Human brain = tens of thousands of neurons • Each neuron is connected to thousands other neurons • A neuron is made of: • The soma: body of the neuron • Dendrites: filaments that provide input to the neuron • The axon: sends an output signal • Synapses: connection with other neurons – releases certain quantities of chemicals called neurotransmitters to other neurons 6
  • 7. Modeling of Brain Functions 7
  • 8. Modelling a Neuron in i j W j , ia j • • • • • aj wj,I inI aI g :Activation value of unit j :Weight on the link from unit j to unit i :Weighted sum of inputs to unit i :Activation value of unit i :Activation function
  • 9. What is an artificial neuron ? • Definition : Non linear, parameterized function with restricted output range y n 1 y f w0 wi xi i 1 w0 x1 x2 x3
  • 10. Simple Neuron X1 Inputs X2 Output Xn b
  • 11. An Artificial Neuron synapses neuron i x1 x2 Wi,1 Wi,2 … … xi Wi,n xn n net input signal net i (t ) wi , j (t ) x j (t ) j 1 output x i (t ) f i ( net i ( t ))
  • 12. Activation functions 20 18 16 Linear 14 12 10 y 8 6 x 4 2 0 0 2 4 6 8 10 12 14 16 18 20 2 1.5 Logistic1 1 0.5 0 y -0.5 1 -1 exp( x) -1.5 -2 -10 -8 -6 -4 -2 0 2 4 6 8 10 2 Hyperbolic tangent 1.5 1 0.5 y -1 -1.5 -2 -10 -8 -6 -4 -2 0 2 4 6 8 10 exp( x ) exp( x) exp( x ) 0 -0.5 exp( x)
  • 13. How do NNs and ANNs work? • Information is transmitted as a series of electric impulses, so-called spikes. • The frequency and phase of these spikes encodes the information. • In biological systems, one neuron can be connected to as many as 10,000 other neurons. • Usually, a neuron receives its information from other neurons in a confined area 13
  • 14. Navigation of a car • Done by Pomerlau. The network takes inputs from a 34X36 video image and a 7X36 range finder. Output units represent “drive straight”, “turn left” or “turn right”. After training about 40 times on 1200 road images, the car drove around CMU campus at 5 km/h (using a small workstation on the car). This was almost twice the speed of any other non-NN algorithm at the time. 14
  • 15. Automated driving at 70 mph on a public highway Camera image 30 outputs for steering 4 hidden units 30x32 pixels as inputs 30x32 weights into one out of four hidden unit
  • 16. Computers vs. Neural Networks “Standard” Computers Neural Networks one CPU highly parallel processing fast processing units units slow processing reliable units unreliable units static infrastructure infrastructure dynamic 16
  • 17. Neural Network Input Layer Hidden 1 Hidden 2 Output Layer
  • 18. Network Layers The common type of ANN consists of three layers of neurons: a layer of input neurons connected to the layer of hidden neuron which is connected to a layer of output neurons.
  • 19. Architecture of ANN • Feed-Forward networks Allow the signals to travel one way from input to output • Feed-Back Networks The signals travel as loops in the network, the output is connected to the input of the network
  • 20. Comparison of Brains and Traditional Computers • 200 billion neurons, 32 trillion synapses • Element size: 10-6 m • Energy use: 25W • Processing speed: 100 Hz • Parallel, Distributed • Fault Tolerant • Learns: Yes • Intelligent/Conscious: Usually • 1 billion bytes RAM but trillions of bytes on disk • Element size: 10-9 m • Energy watt: 30-90W (CPU) • Processing speed: 109 Hz • Serial, Centralized • Generally not Fault Tolerant • Learns: Some • Intelligent/Conscious: Generally No
  • 21. Neural Networks (Applications) • Face recognition • Time series prediction • Process identification • Process control • Optical character recognition • Adaptative filtering • Etc…
  • 22. And Finally…. “If the brain were so simple that we could understand it then we’d be so simple that we couldn’t”
  • 23. Introduction is End of Neural Networks