1. Fundamentals of Neural Networks Dr. Satinder Bal Gupta
Dr. Satinder Bal Gupta, VCE, Rohtak

2. Fundamentals of Neural Networks
Dr. Satinder Bal Gupta, VCE, Rohtak

3. Dr. Satinder Bal Gupta, VCE, Rohtak
Introduction
Dr. Satinder Bal Gupta, VCE, Rohtak

4. Dr. Satinder Bal Gupta, VCE, Rohtak
Why Neural Networks ?
Dr. Satinder Bal Gupta, VCE, Rohtak

5. Dr. Satinder Bal Gupta, VCE, Rohtak
History
Dr. Satinder Bal Gupta, VCE, Rohtak

6. Advantages and disadvantages of neural networks
One major advantage of neural networks is that they complement symbolic AI. For one, neural networks are based upon the brain, and for two, they are based on a totally different philosophy from symbolic AI. For this reason, neural networks have shown many interesting practical applications which are unique to neural networks.
Another major advantage of neural networks is their easy implementation of parallelism since, for example, each neuron can work independently. Generally, developing parallel algorithms for given problems or models (e.g., search, sort, matrix multiplication, etc.) is not easy.
Dr. Satinder Bal Gupta, VCE, Rohtak

7. Advantages of neural networks (cont.)
Other advantages are:
Learning capability. Neural networks can learn by adjusting their weights.
Robustness. For example, neural networks can deal with certain amount of noise in the input. Even if part of a neural network is damaged (perhaps similar to partial brain damage), often it can still perform tasks to a certain extent, unlike some engineering systems, like a computer.
Generalization. A neural network can deal with new patterns which are similar to learned patterns.
Nonlinearity. Nonlinear problems are hard to solve mathematically. Neural networks can deal with any problems that can be represented as patterns.
Dr. Satinder Bal Gupta, VCE, Rohtak

8. Disadvantages of neural networks
First, they have not been able to mimic the human brain or intelligence.
Second, after we successfully train a neural network to perform its goal, its weights have no direct meaning to us. That is, we cannot extract any underlying rules which may be implied from the neural network. A big gap remains between neural networks and symbolic AI. Perhaps this situation is essentially the same for the brain - the brain performs at a high level of intelligence, but when we examine it at the physiological level, we see only electrochemical signals passing throughout the natural neural network. A breakthrough for connecting the micro- and macroscopic phenomena in either area, artificial or natural neural networks, may solve the problem for the other. A solution for either area, however, appears unlikely to come in the near future.
Third, computation often takes a long time, and sometimes it does not even converge. A counter-argument against this common problem of long time training is that even though it may take a month of continuous training, once it is successful, it can be copied to other systems easily and the benefit can be significant.
Fourth, scaling up a neural network is not a simple matter. For example, suppose that we trained a neural network for 100 input neurons. When we want to extend this to a neural network of 101 input neurons, normally we have to start over an entire training session for the new network.
Dr. Satinder Bal Gupta, VCE, Rohtak

9. Biological Neuron Model
Dr. Satinder Bal Gupta, VCE, Rohtak

10. Information Flow in a Neural Cell
Dr. Satinder Bal Gupta, VCE, Rohtak

11. Artificial Neuron Model
Dr. Satinder Bal Gupta, VCE, Rohtak

12. Dr. Satinder Bal Gupta, VCE, Rohtak
Functions
Dr. Satinder Bal Gupta, VCE, Rohtak

13. Model of Artificial Neuron
Dr. Satinder Bal Gupta, VCE, Rohtak

14. Artificial Neuron–Basic Elements
Dr. Satinder Bal Gupta, VCE, Rohtak

15. Dr. Satinder Bal Gupta, VCE, Rohtak
Basic Elements (Cont.)
Dr. Satinder Bal Gupta, VCE, Rohtak

16. Dr. Satinder Bal Gupta, VCE, Rohtak
Example
Dr. Satinder Bal Gupta, VCE, Rohtak

17. Neural Network Architectures
Dr. Satinder Bal Gupta, VCE, Rohtak

18. Single Layer Feed-forward Network
Dr. Satinder Bal Gupta, VCE, Rohtak

19. Multi Layer Feed-forward Network
Dr. Satinder Bal Gupta, VCE, Rohtak

20. Dr. Satinder Bal Gupta, VCE, Rohtak
Recurrent Networks
Dr. Satinder Bal Gupta, VCE, Rohtak

21. Learning Methods in Neural Networks
Dr. Satinder Bal Gupta, VCE, Rohtak

22. Classification of Learning Algorithms
Dr. Satinder Bal Gupta, VCE, Rohtak

23. Learning Methods (Cont.)
Dr. Satinder Bal Gupta, VCE, Rohtak

24. Dr. Satinder Bal Gupta, VCE, Rohtak
Hebbian Learning
Dr. Satinder Bal Gupta, VCE, Rohtak

25. Gradient decent Learning
Dr. Satinder Bal Gupta, VCE, Rohtak

26. Competitive and stochastic Learning
Dr. Satinder Bal Gupta, VCE, Rohtak

27. Applications of Neural Networks
Dr. Satinder Bal Gupta, VCE, Rohtak