This document provides an overview of artificial neural networks (ANN). It describes the basic components and functions of biological neurons that inspired ANN. There are three main types of learning in ANN: supervised, unsupervised, and reinforcement learning. Supervised learning involves computing the output, adjusting weights using backpropagation and gradient descent, and repeating until the desired output is achieved. ANN have broad applications in areas like function approximation, classification, control systems, and medical diagnosis. While still limited compared to the biological brain, ANN can efficiently solve problems that were previously unsolvable.

1. In the name of ALLAH the most
beneficent the most merciful.

2. Artificial Neural
Network(ANN)
Presented By:
Malik Masood Ahmad
MSCS-F14-M039

3. The Biological Brain
 Neurons: Fundamental information-
processing units of the brain.
 Neurons contain axons (the transmission
lines) and dendrites, (the receptive zones).
 Electrical signal flows from dendrites to
axon.

4. Basic Idea of ANN
The neuron receives signals from
other neurons, collects the input
signals, and transforms the
collected input signal
The single neuron then transmits
the transformed signal to other
neurons

5. Three major learning
paradigms
 Supervised learning
 Unsupervised learning
 Reinforcement learning

6. Supervised learning
Compute
Output
Adjust Weights
Stop
Is
Desired
Output
Achieve
d
Yes
No

7. Feed forward network

8. Compute Output

9. Adjust weights/Back propagation
Gradient descent
Error
Gradient

10. Adjust weights/Back propagation

11. Results/Examples

12. Computational power
 The universal approximation theorem states
that a feed-forward network with a single
hidden layer containing a finite number
of neurons can approximate continuous
functions, under mild assumptions on the
activation function.
 A specific recurrent architecture with rational
valued weights (as opposed to full
precision real number-valued weights) has the
full power of a Universal Turing Machine.[1]
[1]. Work by Hava Siegelmann and Eduardo D. Sontag

13. Matlab code

14. Time Complexity
O(V.E)

15. Broader Applications:
 Function approximation
 Fitness approximation and modelling.
 Classification.
 Novelty detection and sequential decision
making.
 Data processing, including filtering, clustering
and compression.
 Robotics, including directing manipulators.
 Control

16. Practical implementations:
 Vehicle control, Process control, Natural
resources management.
 Game-playing and Decision making.
 Radar systems, Face identification, Object
recognition.
 Medical diagnosis, financial applications.

17. End of the Story
 “ Artificial neural networks are still far
away from biological neural networks ,
but what we know today about artificial
neural networks is sufficient to solve many
problems that were previously unsolvable
or inefficiently solvable at best. ”