1. Neural Networks Lab 5

2. What Is Neural Networks? Neural networks are composed of simple elements( Neurons) operating in parallel. Neural networks are composed of simple elements( Neurons) operating in parallel. These elements are inspired by biological nervous systems. These elements are inspired by biological nervous systems. We can train a neural network to perform a particular function by adjusting the values of the connections (weights) between elements. We can train a neural network to perform a particular function by adjusting the values of the connections (weights) between elements.

3. Simplification of NN

4. Fields in NN Pattern recognition Pattern recognition Identification Identification Classification Classification Speech Speech Vision Vision control systems control systems

5. Characteristics of NN Can be trained to solve problems Can be trained to solve problems Makes weight and bias changed based on an entire set (batch) of input vectors. Makes weight and bias changed based on an entire set (batch) of input vectors.

6. Neuron Model p : The scalar input w : the scalar weight to form the product wp => adjustable scalar wp : the weighted input f : the transfer function which produces the scalar output a a : the scalar output b: a scalar bias => adjustable scalar n: the sum of the weighted input wp and the bias b

7. Transfer Functions Three of the most commonly used functions !

8. Neuron with Vector Input A neuron with a single R-element input vector is shown below. A neuron with a single R-element input vector is shown below. Here the individual element inputs Here the individual element inputs are multiplied by weights are multiplied by weights

9. Neuron with Vector Input => n = W*p + b

10. Abbreviated Notation

11. A Layer of Neurons

12. The S neuron R input one-layer network

13. Multiple Inputs and Layers Hidden Layers Output Layer