1. Fundamental
ARTIFICIAL NEURAL NETWORK
Session 1st
TOPICS & ILLUSTRATION
Azhari, Dr
@ 2010
Computer Science, UGM

2. Human Neural a b

3. a b d c

4. Neuron in simple 2D imageb

5. Modelling Computation of a Neuronb

6. How to model a neuron to recognize of “OR”
Example 1
recognize of “OR”
How to model a neuron to recognize of “OR” OR
----
FALSE FALSE --> FALSE
TRUE FALSE --> TRUE
FALSE TRUE --> TRUE
TRUE TRUE --> TRUE

7.    Example 1 OR Real world Neuron model 1 OR 1 1 X1 X2 Z w1 w2
How to model a neuron to recognize of “OR”
1 OR 1 1
Real world
  X1 X2 Z OR w1 w2 
Neuron model
w1, w2 harus dicari berdasarkan nilai w1, w2 awal yang diberikan

8. illustration to train of “OR” neuron

9. Test: artificial neuron “OR” model
TRUE OR TRUE
TRUE

10. Use others initial weights, threshold, learning rate
TRAIN again
Use others initial weights, threshold, learning rate
Weights result of training

11. Domain result of weights for “OR” neuron

12.    Example 2 AND Real world Neuron model 1 AND 1 X1 w1 Z w2 X2
How to model a neuron to recognize of “AND”
1 AND 1
Real world
  X1 X2 Z
AND w1 w2 
Neuron model
w1, w2 harus dicari berdasarkan nilai w1, w2 awal yang diberikan

13. Result for stable weight
Train of “AND” neuron
Result for stable weight

14. How to model a neuron to recognize of “XOR”
Example 3
recognize of “XOR”
How to model a neuron to recognize of “XOR”
XOR
---
FALSE FALSE --> FALSE TRUE FALSE --> TRUE FALSE TRUE --> TRUE TRUE TRUE --> FALSE

15. “XOR” by Multilayer neural netwok
In order to solve XOR, we must create a multi-layered Perceptron with two inputs, a “hidden” layer, and an output.
This hidden layer amounts to having multiple perceptrons attack the same problem, i.e. if OR is true (perceptron #1) and AND is not true (perceptron #2), then fire the XOR output.

16. “XOR” by Multilayer neural netwok

17. “XOR” by Multilayer neural netwok

20. “XOR” by Multilayer neural netwok
One solution for training a multi-layered neural network is known as backpropogation.
Certainly, there are other options for training a neural network.

21. ANN model how to Recognize Apple

22. The goals of neural network computation
To understand how the brain actually works
Its very big and very complicated and made of yukky stuff that dies when you poke it around
To understand a new style of computation
Inspired by neurons and their adaptive connections
Very different style from sequential computation
should be good for things that brains are good at (e.g. vision)
Should be bad for things that brains are bad at (e.g. 23 x 71)
To solve practical problems by using novel learning algorithms
Learning algorithms can be very useful even if they have nothing to do with how the brain works

23. Simple Programming for ANN Backpropagation
ANN Topics
Architecture ANN
Activation Functions
Learning Methods
Simple Programming for ANN
Backpropagation