This document provides an introduction to neural networks, including their basic components and types. It discusses neurons, activation functions, different types of neural networks based on connection type, topology, and learning methods. It also covers applications of neural networks in areas like pattern recognition and control systems. Neural networks have advantages like the ability to learn from experience and handle incomplete information, but also disadvantages like the need for training and high processing times for large networks. In conclusion, neural networks can provide more human-like artificial intelligence by taking approximation and hard-coded reactions out of AI design, though they still require fine-tuning.

1. Neural NetworkGuided byChapala MaharanaSubmitted By-Nilmani SinghBranch-CSERegd. No.-0601289251

2. Contents.Introduction to Neural Network.Neurons.Activation Function.Types of Neural Network.Learning In Neural Networks.Application of Neural Network.Advantages of Neural Network.Disadvantages Of Neural Network

3. Neural NetworksA method of computing, based on the interaction of multiple connected processing elements.

4. A powerful technique to solve many real world problems.

5. The ability to learn from experience in order to improve their performance.

6. Ability to deal with incomplete informationBasics Of Neural NetworkBiological approach to AI

7. Developed in 1943

8. Comprised of one or more layers of neurons

9. Several types, we’ll focus on feed-forward and feedback networksNeuronsBiologicalArtificial

10. Neural Network NeuronsReceives n-inputsMultiplies each input by its weightApplies activation function to the sum of resultsOutputs result

11. Activation FunctionsControls when unit is “active” or “inactive”Threshold function outputs 1 when input is positive and 0 otherwiseSigmoid function = 1 / (1 + e-x)

12. Types of Neural NetworksNeural Network types can be classified based on following attributes:Connection Type - Static (feedforward) - Dynamic (feedback) Topology - Single layer - Multilayer - Recurrent Learning Methods - Supervised - Unsupervised - Reinforcement

13. Classification Based On Connection TypesStatic(Feedforward)

14. Dynamic(Feedback)Classification Based On Topology Recurrent

15. Single layer

16. Multilayer(unit delay operator z-1implies dynamic system)

17. Classification Based On Learning MethodSupervisedUnsupervisedReinforcementSupervised learning Each training pattern: input + desired output

18. At each presentation: adapt weights

19. After many epochs convergence to a local minimumUnsupervised LearningNo help from the outsideNo training data, no information available on the desired outputLearning by doingUsed to pick out structure in the input:ClusteringReduction of dimensionality  compressionExample: Kohonen’s Learning Law

20. Reinforcement learningTeacher: training data

21. The teacher scores the performance of the training examples

22. Use performance score to shuffle weights ‘randomly’

23. Relatively slow learning due to ‘randomness’Neural Network ApplicationsPattern recognition

24. Investment analysis

25. Control systems & monitoring

26. Mobile computing

27. Marketing and financial applications

28. Forecasting – sales, market research, meteorologyAdvantages:A neural network can perform tasks that a linear program can not.

29. When an element of the neural network fails, it can continue without any problem by their parallel nature.

30. A neural network learns and does not need to be reprogrammed.

31. It can be implemented in any application.

32. It can be implemented without any problemDisadvantages:The neural network needs training to operate.

33. The architecture of a neural network is different from the architecture of microprocessors therefore needs to be emulated.

34. Requires high processing time for large neural networks.ConclusionsNeural networks provide ability to provide more human-like AITakes rough approximation and hard-coded reactions out of AI design (i.e. Rules and FSMs)Still require a lot of fine-tuning during development

35. ReferencesNeural Networks, Fuzzy Logic, and Genetic Algorithm ( synthesis and Application) S.Rajasekaran, G.A. VijayalakshmiPai, PHI Neuro Fuzzy and Soft Computing, J. S. R. JANG,C.T. Sun, E. Mitzutani, PHINeural Netware, a tutorial on neural networksSweetser, Penny. “Strategic Decision-Making with Neural Networks and Influence Maps”, AI Game Programming Wisdom 2, Section 7.7 (439 – 46)Russell, Stuart and Norvig, Peter. Artificial Intelligence: A Modern Approach, Section 20.5 (736 – 48)

36. THANK YOU

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