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1. Introduction to
Artificial neural network

2. Artificial Neural Networks
• Computational models inspired by the human brain:
– Algorithms that try to mimic the brain.
– Massively parallel, distributed system, made up of simple processing
units (neurons)
– Synaptic connection strengths among neurons are used to store the
acquired knowledge.
– Knowledge is acquired by the network from its environment through a
learning process

3. Biological Neurons
• The human brain is
made up of billions
of simple processing
units – neurons.
• Inputs are received on dendrites, and if the input levels are
over a threshold, the neuron fires, passing a signal through
the axon to the synapse which then connects to another
neuron.

4. Terminology
Biological Terminology Artificial Neural Network Terminology
Neuron Node/Unit/Cell/Neurode
Synapse Connection/Edge/Link
Synaptic Efficiency Connection Strength/Weight
Firing frequency Node output

5. Introduction
• real-valued functions
• discrete-valued functions
• vector-valued functions
Artificial neural networks (ANNs) provide a practical method for learning
Robust to errors in training data
• interpreting visual scenes
• speech recognition
• learning robot control strategies
Successfully applied to such problems as

6. Neural Network Representation
• ALVINN uses a learned ANN to steer an autonomous
vehicle driving at normal speeds on public highways
– Input to network: 30x32 grid of pixel intensities obtained
from a forward-pointed camera mounted on the vehicle
– Output: direction in which the vehicle is steered
– Trained to mimic observed steering commands of a human
driving the vehicle for approximately 5 minutes

7. ALVINN

8. Appropriate problems
ANN learning well-suit to problems which the training data corresponds
to noisy, complex data (inputs from cameras or microphones)
Can also be used for problems with symbolic representations
Most appropriate for problems where
• Instances have many attribute-value pairs
• Target function output may be discrete-valued, real-valued, or a vector of several real-
or discrete-valued attributes
• Training examples may contain errors
• Long training times are acceptable
• Fast evaluation of the learned target function may be required
• The ability for humans to understand the learned target function is not important

9. Artificial Neural Network
• Neural network resembles the
human brain in the following two
ways: -
* A neural network acquires
knowledge through learning.
*A neural network’s knowledge is
stored within the interconnection
strengths known as synaptic weight.

10. Artificial Neural Network Model
Artificial neural network model Showing adjust of neural network

11. General Structure of ANN
Activation
function
g(Si
)
Si
Oi
I1
I2
I3
wi1
wi2
wi3
Oi
Neuron i
Input Output
threshold, t
Input
Layer
Hidden
Layer
Output
Layer
x1
x2
x3
x4
x5
y
Training ANN means learning the
weights of the neurons

12. Artificial Neural Networks (ANN)
X1 X2 X3 Y
1 0 0 0
1 0 1 1
1 1 0 1
1 1 1 1
0 0 1 0
0 1 0 0
0 1 1 1
0 0 0 0

X1
X2
X3
Y
Black box
0.3
0.3
0.3 t=0.4
Output
node
Input
nodes









otherwise
0
true
is
if
1
)
(
where
)
0
4
.
0
3
.
0
3
.
0
3
.
0
( 3
2
1
z
z
I
X
X
X
I
Y

13. Advantages of Artificial Neural Networks
• It involves human
like thinking.
• They handle noisy
or missing data.
• They can work
with large number
of variables or
parameters.
• They provide
general solutions
with good
predictive accuracy.
• System has got
property of
continuous
learning.
• They deal with
the non-linearity in
the world in which
we live.

14. Learning Process
• Definition of learning adapted from Mendel and McClaren:
“Learning is a process by which the free parameters of a neural network are
adapted through a process of stimulation by the environment in which the
network is embedded. The type of learning is determined by the parameter
in which the changes take place.”

15. Learning Methods

16. Learning Methods (Cont…)
There are three major learning methods:
1. Supervised learning
2. Unsupervised learning
3. Reinforcement learning

17. Supervised Learning

18. Supervised Learning

19. Unsupervised Learning

20. Unsupervised Learning

21. Reinforcement Learning

22. Reinforcement Learning (Cont…)

23. Types of Reinforcement Learning
POSITIVE REINFORCEMENT LEARNING NEGATIVE REINFORCEMENT LEARNING

24. Comparison of the 3 Learning Methods

27. Taxonomy of Neural Networks

28. Taxonomy of Neural Networks (Cont…)
• Adaptive Linear Element (ADALINE)
• Multiple ADALINEs (MADALINE)
• Perceptron
• Radial Basis Function Network
• Feed-forward
• Recurrent Neural Networks (RNNs)

32. Neural Networks – In the Past
• A team led by Ross King at the Manchester Institute of Biotechnology
created an artificial intelligence scientist named Eve.
• Such advanced specimens like Eve, advanced chatbots, and autonomous
cars, suggest that the vision for artificial neural networks is actually
shaping up!

33. Neural Networks – In the Present
• One of the applications of artificial neural networks is chatbots.
• Some of the novel applications of neural networks include earthquake
prediction based on the existing seismograms and creating artworks based
on existing iconic paintings by Van Gogh, Picasso, and many more.

34. Neural Networks – In the Future
• Governments and private organizations have realized the true potential of
the future of artificial neural networks.
• The future of artificial neural networks is going to unlock multiple
possibilities in various business sectors.