This document provides an overview of neural networks, including their history, components, connection types, learning methods, applications, and comparison to conventional computers. It discusses how biological neurons inspired the development of artificial neurons and neural networks. The key components of biological and artificial neurons are described. Connection types in neural networks include static feedforward and dynamic feedbackward connections. Learning methods include supervised, unsupervised, and reinforcement learning. Applications span mobile computing, forecasting, character recognition, and more. Neural networks learn by example rather than requiring explicitly programmed algorithms.

1. Presented By: R.Geethika Ramani
13JT1A0524
3rd CSE
NEURAL NETWORKS

2. Contents:
• Introduction
• History of Neural networks
• Working of Biological neuron
• Working of Artificial neuron
• Connection types
• Topologies
• Learning methods of neurons
• Applications
• Neural networks versus conventional computers
• Merits
• De-merits
• Conclusion

3. Introduction:
• An Artificial Neural Network is an information processing paradigm that is inspired
by the way biological nervous systems, such as the brain, process information.
• ANN is composed of a large number of highly interconnected processing elements
(neurones) working in unison to solve specific problems.
• ANNs, like people, learn by example. An ANN is configured for a specific
application, such as pattern recognition or data classification, through a learning
process.

4. History of Neural Networks:
• The history of neural networks begins before the invention
computer.i.e., in 1943.
• The first neural network construction is done by neurologists for
understanding the working of neurons.
• Later technologists are also interested in this networks.
• In recent years, the importance of neural networks was observed.

5. Working of Biological neuron:
• A biological neuron contains mainly four parts. They are dendrites, cell body, axon
and synapse.

6. Working of Artificial neuron:
• An artificial neuron also contains dendrites, cell body, axon and synapse.
• In artificial neural networks, the inputs are taken only when threshold value is
satisfied. Otherwise inputs are not taken by the neuron.
• There are two modes of neurons such as, training mode and using mode.
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7. Connections types in Neural networks:
• Neurons are interconnected with each other, for the transferring the data.
• There are two types of hierarchies for connecting the neurons.
1. Static connection
2. Dynamic connection

8. 1. Static(feed forward):
• The feedforward neural network was the first and most simple type of
artificial neural network.​
• In this network, the information will moves in one direction only.

9. 2. Dynamic(feed backward):
• Feed backward is advanced than feed forward.
• In feed backward, looping mechanism is introduced.

10. Topologies in Neural networks:
• Topology defines how a neuron in neural network connected with
another neurons.
• There are three types topologies that every neural network must
follow the one of the following:
1. single-level topology
2. multi-level topology
3. recurrent topology

11. 1. single-level:
• The simplest kind of neural network is a single-layer network, which consists of
equal no.of input and output nodes.

12. 2. multi-level:
• In multi-level, each neuron in one layer has directed connections to the neurons
of the subsequent layer

13. 3. recurrent:
• A recurrent neural network (RNN) is a class of artificial neural networks where
connections between units form a directed cycles.

14. Learning methods of Neuron:
• Neurons in neural networks will learn about the working pattern of the new
task.
• Next time, when the same task is given to perform, it automatically
generates output without wasting of time.
• There are three types of learning methods.they are
1. supervised learning
2. unsupervised learning
3. reinforcement learning

15. 1. Supervised learning:
• In supervised learning, each example is a pair consisting of an input object
along with a desired output value.
2. Unsupervised learning:
• In unsupervised learning, there is no desired output is supplied with the
input.
3. reinforcement learning:
• Reinforcement learning is an area of machine learning inspired by behaviorist
psychology, concerned with how software agents ought to take actions in an
environments as to maximize some notion of cumulative reward.

16. Applications:
• Mobile computing
• Forecasting
• Character recognition
• Traveling salesman problem
• Medical diagnosis
• Quality control
• Data mining
• Game development
• Pattern recognition.

17. Neural networks versus conventional computers:
• To perform a task in conventional computer, we need to write algorithm.
But in neural networks, process information is in a similar way of the
human brain. The network is composed of a large number of highly
interconnected processing elements(neurones) working in parallel to
solve a specific problem. Neural networks learn by example.

18. Merits:
• No need to write any algorithms.
• Work by learning.
• Work will be automatically shared.
• Robust.
• Neural networks works efficiently.

19. De-merits:
• Needs to understand before working with neural networks.
• Requires high processing time for large neural networks.
• Noisy data.
• Takes large time for connecting neurons.

20. Conclusion:
• The computing world has a lot to gain from neural networks. Their ability to
learn by example makes them very flexible and powerful. Furthermore there is
no need to devise an algorithm in order to perform a specific task.

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