Artificial Neural Networks Lect1: Introduction & neural computation

1. CS407 Neural Computation
Lecturer: A/Prof. M. Bennamoun

2. General Information
Lecturer/Tutor/Lab Demonstrator:
– A/Prof. M. Bennamoun
Timetable:
– Lectures: Mondays 10am-11:45am CS-Room
1.24
– Tutorials: fortnightly starting in week 3
– Laboratories: starting in week 3
– Consultations: Thursdays 2-3pm.
Assessment:
– Project- week 9-13: 40%
– Final Exam- 2 hours : 60%.
Recommendations: Familiarize yourselves with
Matlab and its neural network toolbox.

3. Text Books
M. Hassoun, Fundamentals of Artificial Neural Networks, MIT
press.
– A thorough and mathematically oriented text.
S. Haykin, “Neural Networks: A Comprehensive foundation” -
– An extremely thorough, strongly mathematically grounded, text
on the subject.
– “If you have strong mathematical analysis basics and you love
Neural Networks then you have found your book”
L. Fausett, “Fundamentals of Neural Networks”
– “clear and useful in presenting the topics, and more importantly,
in presenting the algorithms in a clear simple format which
makes it very easy to produce a computer program implementing
these algorithms just by reading the book”
Phil Picton, “Neural Networks”, Prentice Hall.
– A simple introduction to the subject. It does not contain algorithm
descriptions but includes other useful material.

4. Today’s Lecture
Motivation + What are ANNs ?
The Brain
Brain vs. Computers
Historical overview
Applications
Course content

5. Motivations: Why ANNSMotivations: Why ANNS
“Sensors” or “power of the brain”“Sensors” or “power of the brain”
TransverseSagittal

6. Computer vs. BrainComputer vs. Brain
Computers are good at: 1/ Fast arithmetic and 2/ Doing
precisely what the programmer programs them to do
Computers are not good at: 1/ Interacting with noisy data or
data from the environment, 2/ Massive parallelism, 3/ Fault
tolerance, 4/ Adapting to circumstances

7. Brain and Machine
• The Brain
– Pattern Recognition
– Association
– Complexity
– Noise Tolerance
• The Machine
– Calculation
– Precision
– Logic

8. The contrast in
architecture
• The Von Neumann architecture
uses a single processing unit;
– Tens of millions of operations per
second
– Absolute arithmetic precision
• The brain uses many slow
unreliable processors acting in
parallel

9. Features of the Brain
• Ten billion (1010) neurons
• On average, several thousand connections
• Hundreds of operations per second
• Die off frequently (never replaced)
• Compensates for problems by massive
parallelism

10. The biological inspiration
• The brain has been extensively studied by
scientists.
• Vast complexity and ethics (which limit
extent of research) prevent all but
rudimentary understanding.
• Even the behaviour of an individual neuron
is extremely complex

11. • Single “percepts” distributed among many
neurons
• Localized parts of the brain are responsible for
certain well-defined functions (e.g. vision,
motion).
• Which features are integral to the brain's
performance?
• Which are incidentals imposed by the fact of
biology?
The biological inspiration

12. Do we want computers which get confused, and makeDo we want computers which get confused, and make
mistakes,,…?mistakes,,…?
Where can neural network systems help?
•where we can't formulate an algorithmic solution.
•where we can get lots of examples of the behaviour we require.
•where we need to pick out the structure from existing data.
•http://www.cs.stir.ac.uk/~lss/NNIntro/InvSlides.html

13. What are “Artificial neural
networks”?http://webopedia.internet.com/TERM/n/neural_network.html
ANN’s are a type of artificial intelligence that
attempts to imitate the way a human brain
works. Rather than using a digital model, in
which all computations manipulate zeros and
ones, a neural network works by creating
connections between processing elements,
the computer equivalent of neurons. The
organization and weights of the connections
determine the output.

14. What are Artificial Neural
Networks? …
(i) Hardware inspired by biological neural
networks, e.g. human brain
(ii) Parallel, Distributed Computing Paradigm
(i.e. Method)
(iii) Algorithm for learning by example
(iv) Tolerant to errors in data and in hardware
(v) Example of a complex system built from
simple parts

15. Sims, history & areas of use…
Strictly speaking, a neural network implies a non-
digital computer, but neural networks can be
simulated on digital computers.
The approach is beginning to prove useful in
certain areas that involve recognizing complex
patterns, such as voice recognition and image
recognition.

16. Definition of an ANN
An ANN is a massively parallel distributed processor
that has a natural propensity for storing exponential
knowledge and making it available for use. It
resembles the brain in 2 respects
knowledge is acquired by the network thru a learning process.
Interconnection strengths known as synaptic weights are used
to store the knowledge.
Other terms/names
• connectionist
• parallel distributed processing
• neural computation
• adaptive networks..

17. Simple explanation – how NNs
workhttp://www.zsolutions.com/light.htm
Neural Networks use a set of processing elements (or
nodes) loosely analogous to neurons in the brain
(hence the name, neural networks). These nodes are
interconnected in a network that can then identify
patterns in data as it is exposed to the data. In a sense,
the network learns from experience just as people do
(case of supervised learning).
This distinguishes neural networks from traditional
computing programs, that simply follow instructions
in a fixed sequential order.

18. Simple explanation – how NNs
workhttp://www.zsolutions.com/light.htm
The structure of
a feed forward
neural network
The bottom layer represents the input layer,
in this case with 5 inputs labelled X1 through
X5. In the middle is something called the
hidden layer, with a variable number of
nodes. It is the hidden layer that performs
much of the work of the network. The output
layer in this case has two nodes, Z1 and Z2
representing output values we are trying to
determine from the inputs. For example, we
may be trying to predict sales (output) based
on past sales, price and season (input).

19. Simple explanation – hidden
layerhttp://www.zsolutions.com/light.htm
Each node in the hidden layer is fully connected to
the inputs. That means what is learned in a hidden
node is based on all the inputs taken together. This
hidden layer is where the network learns
interdependencies in the model. The following
diagram provides some detail into what goes on
inside a hidden node (see more details later).
Simply speaking a weighted sum is performed: X1
times W1 plus X2 times W2 on through X5 and
W5. This weighted sum is performed for each
hidden node and each output node and is how
interactions are represented in the network.
Each summation is then transformed using a
nonlinear function before the value is passed on to
the next layer.
More on the
Hidden Layer

20. Where does the NN get the
weights? (case of supervised learning)
http://www.zsolutions.com/light.htm
Again, the simple explanation... The network is
repeatedly shown observations from available data
related to the problem to be solved, including both
inputs (the X1 through X5 in the diagram above) and
the desired outputs (Z1 and Z2 in the diagram). The
network then tries to predict the correct output for
each set of inputs by gradually reducing the error.
There are many algorithms for accomplishing this,
but they all involve an interactive search for the
proper set of weights (the W1-W5) that will do the
best job of accurately predicting the outputs.

21. Historical Overview 40’s, 50’s 60’s
(i) McCulloch & Pitts (1943) - Threshold neuron
McCulloch & Pitts are generally recognised as the
designers of the first neural network
(ii) Hebb (1949) - first learning rule
(iii) Rosenblatt (1958) - Perceptron & Learning Rule
(iv) Widrow & Huff (1962) - Adaline

22. Historical Overview 70’s
(v) Minsky & Papert (1969) – Perceptron Limitations
described, Interest wanes (death of ANNs)
(vi) 1970’s were quiet years

23. Historical Overview 80’s 90’s
(vii) 1980’s: Explosion of interest
(viii) Backpropagation discovered (Werbos ‘74, Parker
‘85, Rumelhart ‘86)
(ix) Hopfield (1982): Associative memory, fast
optimisation
(x) Fukushima (1988): neocognitron for character
recognition

24. Signal Processing, e.g. Adaptive Echo Cancellation
Pattern Recognition, e.g. Character Recognition
Speech Synthesis (e.g. Text-to-Speech) & recognition
Forecasting and prediction
Control & Automation (neuro-controllers) e.g. Broom-
Balancing
Radar interpretation
Interpreting brain scans
Stock market prediction
Associative memory
Optimization, etc…
For more reference, see the Proceedings of the IEEE, Special Issue on Artificial
Neural Network Applications, Oct. 1996, Vol. 84, No. 10
Applications of ANNs

25. A simple example
0
1
Pattern
classifier
2
4
3
5
7
6
8
9
We want to classify each input pattern as one of
the 10 numerals (3 in the above figure).

26. ANN sol’n
ANNs are able to perform an accurate classification
even if the input is corrupted by noise. Example:

27. Artificial Neural Networks
(Taxonomy)
These neurons connected together will form a network.
These networks (ANNs) differ from each other, according to 3
main criteria:
(1) the properties of the neuron or cell (Threshold, and
Activation Function)
(2) The architecture of the network or topology and
(3) the learning mechanism or learning rule (Weight
Calculation), and the way they are updated: Update rule, e.g.
synchronous, continuous.
And of course the type of implementation: Software, Analog
hardware, digital hardware

28. Topology/Architecture
There are 3 main types of topologies:
Single-Layer Feedforward Networks
Multilayer Feedforward Networks
Recurrent Networks.

29. Network Architecture
Multiple layer
fully connected
Single layer
Unit delay
operator
Recurrent network
without hidden units
inputs
outputs
{
}
Recurrent network
with hidden units

30. Topics to be covered
Background information
Threshold gates
Multilayer networks
Classification problem
Learning process
Correlation matrix
Perceptron learning rule
Supervised learning
Multi-layer perceptron (MLP): Backpropagation alg.
Unsupervised learning

31. Topics to be covered (…)
Hebbian learning rule, Oja’s rule
Competitive learning
Instar/Outstar Networks (ART-Adaptive Resonance
Theory)
Self-organizing feature maps (Kohonen’s nets)
Hopfield netws, stochastic neurons
Boltzmann machines & their applications
Recurrent neural nets & temporal NNs

32. Assumed Background
Basic linear Algebra
Basic differential calculus
Basic combinatorics
Basic probability

33. References:
1. ICS611 Foundations of Artificial Intelligence, Lecture
notes, Univ. of Nairobi, Kenya: Learning –
http://www.uonbi.ac.ke/acad_depts/ics/course_material-
1. Berlin Chen Lecture notes: Normal University, Taipei,
Taiwan, ROC. http://140.122.185.120-
2. Lecture notes on Biology of Behaviour, PYB012- Psychology,
by James Freeman, QUT.
3. Jarl Giske Lecture notes: University of Bergen Norway,
http://www.ifm.uib.no/staff/giske/
4. Denis Riordan Lecture notes, Dalhousie
Univ.:http://www.cs.dal.ca/~riordan/
5. Artificial Neural Networks (ANN) by
David Christiansen:
http://www.pa.ash.org.au/qsite/conferences/conf2000/
moreinfo.asp?paperid=95

34. References:
•Jin Hyung Kim, KAIST Computer Science Dept.,
CS679 Neural Network lecture notes
http://ai.kaist.ac.kr/~jkim/cs679/detail.htm
tp://ai.kaist.ac.kr/~jkim/cs679/detail.htm