Introduction to artificial neural network

Department of Information Technology 1Soft Computing (ITC4256 )
Introduction to
Artificial neural network
Dr. C.V. Suresh Babu
Professor
Department of IT
Hindustan Institute of Science & Technology

Discussion Topics
• Introduction
• Characteristics
• Learning methods
• Taxonomy
• Evolution of neural networks
• Basic models
• Important technologies
• Applications

Action Plan
• Introduction to Artificial Neural Network (ANN)
- Introduction
- Biological Neuron Model
- Terminology
- Artificial Neural Network
- ANN Model
- Advantages of ANN
• Quiz at the end of session
3

Action Plan
- Introduction
- Biological Neuron Model
- Terminology
- Artificial Neural Network
- ANN Model
- Advantages of ANN

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

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.

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

ALVINN

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

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

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.

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

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.

Quiz - Questions
1. Dendrites does the following
a) process inputs b) accepts inputs c) converts inputs to outputs d) none
2. Neural networks are based on simulated
a) neurons b) axons c) dendrites d) somas
3. ANN system has property of
a) definite learning b) digital learning c) continuous learning d) none
4. Synapse relates to
a) connection b) weight c) node d) node output
5. ------ are the electrochemical contact between the neurons.
a) synapses b) axon c) soma d) dendrites

Quiz - Answers
1. Dendrites does the following
b) accepts inputs
2. Neural networks are based on simulated
a) neurons
3. ANN system has property of
c) continuous learning
4. Synapse relates to
a) connection
5. ------ are the electrochemical contact between the neurons.
a) synapses

Action Plan
- Characteristics
- Learning Process
- Learning Methods
- Supervised Learning
- Unsupervised Learning
- Reinforcement Learning

Characteristics
• Mathematical model
• Neurons
• Information are stored in neurons.
• Input signals
• Ability to learn
• Computational power is based on neurons.

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.”

Learning Methods

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

Supervised Learning

Unsupervised Learning

Reinforcement Learning

Reinforcement Learning (Cont…)

Types of Reinforcement Learning
• Positive reinforcement learning
• Negative reinforcement learning

Comparison of the 3 Learning Methods

Quiz - Questions
1. It is neurally implemented
a) mathematical model b) analytical model c) both a & b d) none
2. The neural network is stimulated by an
a) algorithm b) axon c) environment d) none
3. --------- learning can also be referred as classification
a) unsupervised b) reinforcement c) machine d) supervised
4. Learning process is independent in --------- learning.
5. ------ model is based on continuous learning.

Quiz - Answers
1. It is neurally implemented
a) mathematical model
2. The neural network is stimulated by an
c) environment
3. --------- learning can also be referred as classification
d) supervised
4. Learning process is independent in --------- learning.
a) unsupervised
5. ------ model is based on continuous learning.
b) reinforcement

Action Plan
- Taxonomy of Neural Networks
- Evolution of Neural Networks
- Basic Models of ANNs
• Assignment – 1: Illustrate the basic models of ANNs in detail with neat
diagrams.

Taxonomy of Neural Networks

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

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!

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.

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.

Basic Models of Artificial Neural Networks
• Models are based on three entities
1. The model’s synaptic interconnections.
2. The training or learning rules adopted for updating and adjusting the
connection weights.
3. Their activation functions.

Quiz - Questions
1. Radial basis function network is a --------- neural network.
a) feed backward b) feed forward c) feed outward d) feed inward
2. From which year evolution of neural networks started?
a) 1942 b) 1943 c) 1944 d) 1945
3. What is the name of the artificial intelligence specimen created by Ross King
and his team?
a) Sofia b) Eva c) Eve d) Evan
4. Chatbot is an application of --------.
a) Big data b) Data Science c) Blockchain d) Artificial neural network
5. Basic ANN models are not based on one of the following entities
a) activation functions b) connection weights c) synaptic interconnections
d) connection breakage

Quiz - Answers
1. Radial basis function network is a --------- neural network.
b) feed forward
2. From which year evolution of neural networks started?
b) 1943
3. What is the name of the artificial intelligence specimen created by Ross King
and his team?
c) Eve
4. Chatbot is an application of --------.
d) Artificial neural network
5. Basic ANN models are not based on one of the following entities
d) connection breakage

Introduction to artificial neural network

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