1. What You Will Learn
From This
Presentation:
 What is Neural Network?
 What is Artificial Network Network?
 What id Difference Between AAN and BNN?
 How Does ANN Work?
 How is the AAN Architecture?
 What Are Types Of AAN?
 What Are The Applications of AAN?

2. What Is Neural Network?
 NeuralNeuron
 Basic Functional Unit

3. What Is Neural
Network?(Contd.)
 Parts Of Neuron:
 Dendrite-receive signal from neurons
 Soma-sums up incoming signals
 Axon-fires signal down the neurons
 Synapse-interconnection-one neuron to other

4. What is Artificial Neural
Network?
 Biologically inspired Simulations
 Used for:
 Clustering
 Classification
 Pattern Recognition

5. Difference Between AAN
and BNN
Parameters BNN ANN
Speed Fast
NanoSecond
s
Slow
Milliseconds
Processing Serial Parallel
Size And
Complexity
Less Size
And
Complexity
More Size
And
Complexity
Storage Replacable Complexe
Interconnec

6. How Is ANN Developed?
 Parallel Processing
 Learn By Pattern
 Essence Program
 Use of application-specific multi-chips.

7. Layers Of ANN
 Input Layer-receive input-outside world
 Output Layer-respond to information-how it learned a
task
 Hidden layer-main layer-all processing-transform input-
output

8. How Is ANN
Developed?(Contd.)
 Activation function-transfer function-output
 Activation:
 Linear
 Non-Linear

9. How Does ANN Actually
Work?
 Large unit work-process information
 Weighted Directed Graph
 Nodes,directed edges with weights
 Information-external world
 Input Multiplied By Corresponding weights
 Weight input-sum=1
 If(sum!=1),basied added

10. How Does ANN Actually
Work?(Contd.)
 Adjusting weights and bias
 Neural Network-trained first
 Training-defined set of rules-learning algorithm

11. Neural Network
Architecture
 Perceptron Model in Neural Networks
 Radial Basis Function Neural Network
 Multilayer Perceptron Neural Network
 Recurrent Neural Network
 Long Short-Term Memory Neural Network (LSTM)
 Hopfield Network
 Convolutional Neural Network
 Modular Neural Network
 Physical Neural Network

12. Types Of Neural Network
Parameters Types
Based On
Connection
Pattern
Feed Forward
Recurrent
Based On
No.of Hidden
Layer
Single Layer
Multilayer
Based on
Nature of
Weight
Fixed
Adaptive

13. What Are Types Of ANN?
 Classification Neural Network
 Prediction Neural Network
 Clustering Neural Network
 Association Neural Network

14. Application Of ANN
 Process modeling and control
 Machine Diagnostics
 Portfolio Management
 Target Recognition
 Medical Diagnosis
 Credit Rating
 Targeted Marketing
 Voice recognition
 Face recognition
 Financial Forecasting
 Intelligent searching
 Fraud detection

15. Advantages Of ANN
 Perform tasks-linear function/program cannot
 Continue parallel processing-even if somethings fail
 Implementated-any application
 Doesnot need to be reprogrammed

16. Limitations of ANN
 Needs training to be performed
 Needs to be emulated
 Require-high processing time

17. Conclusion:
 ANN-very benefial
 Very efficient-neural building-image recogntion
 Requires efficient computing machines

18. At the end of this PPT ,we
haved learned:
 What is ANN?
 How is it Developed?
 What are its Types?
 What its Apllications?

19. Questions

20. THANKYOU