Abhey Sharma's presentation discusses neural networks. It defines biological neural networks as networks of real neurons in the brain, and artificial neural networks (ANNs) as artificial systems composed of interconnected nodes modeled after biological neurons. ANNs are configured through learning to perform tasks like pattern recognition. The history of neural networks is reviewed, from early enthusiasm to a period of frustration before recent resurgence. Neural networks offer advantages like adaptive learning, self-organization, fault tolerance, and fast real-time operation, but disadvantages include their "black box" nature, high computational requirements, difficulty incorporating time, and inevitable errors of approximation.

1. PRESENTATION
ON
NEURAL NETWORK
BY -:
Abhey sharma

2. What is a Neural Network
The term neural network was traditionally used to refer to a
network or circuit of biological neurons.The modern usage of
the term often refers to artificial neural networks, which are
composed of artificial neurons or nodes. Thus the term has
two distinct usages:
Biological neural networks are made up of real biological
neurons that are connected or functionally related in a
nervous system. In the field of neuroscience, they are often
identified as groups of neurons that perform a specific
physiological function in laboratory analysis.

3. An ANN is configured for a specific application, such as
pattern recognition or data classification, through a
learning process. Learning in biological systems
involves adjustments to the synaptic connections that
exist between the neurons. This is true of ANNs as
well.

4. HISTORY
• Many important advances have been boosted by the use of
inexpensive computer emulations. Following an initial period
of enthusiasm, the field survived a period of frustration and
disrepute. During this period when funding and professional
support was minimal, important advances were made by
relatively few researchers. These pioneers were able to
develop convincing technology which surpassed the
limitations identified by Minsky and Papert. Minsky and
Papert, published a book (in 1969) in which they summed up
a general feeling of frustration (against neural networks)
among researchers, and was thus accepted by most without
further analysis. Currently, the neural network field enjoys a
resurgence of interest and a corresponding increase in
funding.

5. Why use neural networks?
• Adaptive learning: An ability to learn how to do tasks based
on the data given for training or initial experience.
• Self-Organisation: An ANN can create its own organisation or
representation of the information it receives during learning
time.
• Real Time Operation: ANN computations may be carried out
in parallel, and special hardware devices are being designed
and manufactured which take advantage of this capability.
• Fault Tolerance via Redundant Information Coding: Partial
destruction of a network leads to the corresponding
degradation of performance. However, some network
capabilities may be retained even with major network
damage.

6. ADVANTAGES
• The computing world has a lot to gain from neural networks.
Their ability to learn by example makes them very flexible and
powerful.
• Further more there is no need to devise an algorithm in order
to perform a specific task; i.e. there is no need to understand
the internal mechanisms of that task.
• They are also very well suited for real time systems because of
their fast response and computational times which are due to
their parallel architecture.
• Neural networks also contribute to other areas of research
such as neurology and psychology. They are regularly used to
model parts of living organisms and to investigate the internal
mechanisms of the brain.

7. DISADVANTAGE
• They are black box - that is the knowledge of its internal
working is never known.
• To fully implement a standard neural network architecture
would require lots of computational resources - for example
you might need like 100,000 Processors connected in parallel
to fully implement a neural network that would "somewhat"
mimic the neural network of a cat's brain - or I may say its a
greater computational burden.
• Applying neural network for human-related problems requires
Time to be taken into consideration but its been noted that
doing so is hard in neural networks
• They are just approximations of a desired solution and errors
in them is inevitable.