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.
4. A powerful technique to solve many real world problems.
5. The ability to learn from experience in order to improve their performance.
10. Neural Network Neurons Receives n-inputs Multiplies each input by its weight Applies activation function to the sum of results Outputs result
11. Activation Functions Controls when unit is “active” or “inactive” Threshold function outputs 1 when input is positive and 0 otherwise Sigmoid function = 1 / (1 + e-x)
33. The architecture of a neural network is different from the architecture of microprocessors therefore needs to be emulated.
34.
35. References Neural 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, PHI Neural Netware, a tutorial on neural networks Sweetser, 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)