This document discusses neural networks and artificial intelligence. It defines artificial intelligence as machines programmed to think like humans, and neural networks as computational models inspired by the human brain. The document explains that neural networks are used in artificial intelligence to help machines solve complex problems. It then provides details on the basic structure and learning mechanisms of neural networks, describing how networks are composed of interconnected neurons that can learn from examples to perform tasks like pattern recognition.

1. NEURAL
NETWORKS
AND ARTIFICIAL
INTELLIGENCE

2. FEATURE
WE COVORED
WHAT IS ARTIFICIAL
INTELLIGENCE
WHAT IS NEURAL
NETWORK
RELATION BETWEEN AI
AND ANN
MECHANISM OF ANN

3. ARTIFICIAL
INTELLIGENCE
Artificial intelligence (AI) refers to simulated
intelligence in machines. These machines are
programmed to "think" like a human and mimic
the way a person acts. The ideal characteristic of
artificial intelligence is its ability to rationalize
and take actions that have the best chance of
achieving a specific goal, although the term can
be applied to any machine that exhibits traits
associated with a human mind, such as learning
and solving problems.02

4. NEURAL
NETWORK
An Artificial Neural
Network (ANN) is an
information processing
paradigm that is inspired
by biological nervous
systems.
It is composed of a large
number of highly
interconnected processing
elements called neurons.
An ANN is configured for a
specific application, such as
pattern recognition or data

5. ARTIFICIAL
INTELLIGENCE
VS
Neural networks, called
artificial neural network when
used in the the field of artificial
intelligence. Artificial neural
networks are the neural
networks which try to replicate
the neural networks in human
brain as a form of
computational model. These
computational model can be
used to solve problems in the
field of artificial intelligence.
NEURAL
NETWORK

6. WHY USE
NEURAL
NETWORKS
Ability to derive meaning
from complicated or
imprecise data
Extract patterns and detect
trends that are too complex
to be noticed by either
humans or other computer
techniques
Adaptive learning
Real Time Operation

7. WHY USE
NEURAL
NETWORKS
Conventional
computers use an
algorithmic approach,
but neural networks
works similar to
human brain and
learns by example.

8. LEARNING
PROCESS
A typical neuron
collects signals from
others through a host
called dendrites
The neuron sends out
spikes of electrical
activity through an
axon.
How the Human
Brain Learns?

9. LEARNING
PROCESS At the end of each
branch, a structure
called synapse
converts the ellectric
activity into
electrical effect.
When a neuron
receives electric
effect, influence other
neuron.
How the Human
Brain Learns?

10. A SIMPLE
NEURON
Takes the Inputs .
Calculate the summation
of the Inputs .
Compare result with its
knowledge.

11. FIRING
RULES
The firing rule is an
important concept in
neural networks and
accounts for their
high flexibility
A firing rule
determines how one
neuron calculates
whether a neuron
should fire for any
input pattern

12. HOW
IT WORK
For example, a 3-input
neuron is taught to
output 1 when the input
(X1,X2 and X3) is 111 or
101 and to output 0
when the input is 000
or 001.

13. STRUCTURE
OF
ANN
feed farward
Feedback networks

14. NATURAL LANGUAGE GENERATION
SPEECH RECOGNITION
AI-OPTIMISED HARDWARE
BIOMETRICS
VIRTUAL AGENTS
DECISION MANAGEMENT
MACHINE LEARNING PLATFORMS
USES

15. SOCIAL
IMPECT
In medical sector AI use for developed
more effective medicine, try to detect
disease and some AI successfully did it.
Business sector also worked with AI to
make analysis market, their product,
customer behavior, also for decision
making.

16. MEET
THE TEAM
CONCEPT
DESIGN
EDITING
CONTENT
DESIGN
PLANNING
CORRECTION
DESIGN
PLANNING
MEHEDI SHYAMA FATEMATUZZOHARA