1. NEURAL NETWORKS Lab 1
dr Zoran Ševarac
FON, 2013.

2. WHAT IS NEURAL NETWORK Brain inspired mathematical models
Biological and artificial NN

3. Biological and artificial neuron
Basic parts: body(soma), dendrites(inputs), axons (outputs), synapses (connections)

4. SUPERVISED LEARING Neural network
Basic principle: iterative error minimization
Desired output d(x)
Error
Neural network
Input X
Actual output y(x)
e(x) = d(x) – y(x)

5. ADALINE Linearna transfer function Linear combination of inputsw1 w2 w3 y
Linearna transfer function
Linear combination of inputs
y = w1u1+w2u2+…+wnun,
Learning algorithm: Least Mean Squares

6. PERCEPTRON Step transfer functiony1 y2 y3
Step transfer function
Perceptron learning - first algorithm for learning nonlinear systems
Only for linear separable problems

7. Multi Layer Perceptron
Basic perceptrn extended with one or more layers of hidden neurons between input and output layer
Differentiable neuron transfer functions (tanh, sigmoid)
Using Backpropagation algorithm for learning, which is bassed on LMS algorithm
Able to solve complex problems

8. NEUROPH FRAMEWORK

9. Osnovni koncepti

11. Neuroph – learning rules class diagram

12. Creating and training neural networks in Java
// create new perceptron network
NeuralNetwork neuralNetwork = new Perceptron(2, 1, TransferFunction.STEP);
// create training set
DataSet trainingSet=new DataSet(2, 1);
// add training data to training set
trainingSet.addRow(new DataSetRow(“0 0”, “0”));
trainingSet. addRow(new DataSetRow(“0 1”, “1”));
trainingSet. addRow(new DataSetRow(“1 0”, “1”));
trainingSet. addRow(new DataSetRow(“1 1”, “1”));
// learn the training set
neuralNetwork.learn(trainingSet);
// save the trained network into file
neuralNetwork.save(“or_perceptron.nnet”);

13. Demo
Neuroph Studio: learning basic logic functions

14. LINKS AND BOOKS http://is.fon.rs/neuronske_mreze
Neural Networks - A Systematic Introduction , besplatna online knjiga
Introduction to Neural Computation