Transcript Course Name

COURSE NAME:
NAME:
STUDENT ID:
DISCIPLINE:
NEURAL NEWORKING LEARNING
PARADIGM WITH APPLICATIONS

THESE NETWORKS ARE DESIGNED AND
INSPIRED FROM HUMAN BRAIN WORKING
AND DESIGN.

THE MODERN USAGE OF THE TERM OFTEN
REFERS TO ARTIFICIAL NEURAL
NETWORKS.

THESE ARE COMPOSED OF ARTIFICIAL
NEURONS OR NODES .

IT WORKS BY MAKING CONNECTIONS
AMONG MANY PROCESSORS IDENTICAL OF
NEURONS.
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T H E Y A R E M O S T LY U S E D F O R C A L C U L AT I O N
A C T I O N S W H E N T H E N E T W O R K S H AV E
D ATA B A S E O F E X A M P L E F O R U S E .
BRIEF DESCRIPTION
 Biological neural networks
Biological neural networks are made up of real biological neurons that are
connected or functionally-related in the peripheral nervous system or the central
nervous system. In the field of neuroscience, they are often identified as group of
neurons that perform a specific physiological function in the laboratory analysis.
 Artificial neural networks
Artificial neural networks are made up of interconnecting artificial neurons.
Artificial neural networks, or for soling artificial intelligence problems without
necessarily creating a model of a real biological system.
ADVANTAGES AND DISADVANTAGES OF
NEURAL NETWORKES
Advantages

Easy to conceptualize.
 Capable of detecting complex relations.
 Large amount of academic research.
 Used extensive in industry speed calculations.
 Can solve any machine learning problem.
Disadvantages
 Neural networks are too much of a black box this makes them difficult to train.
 There are alernative that are simpler, faster, easier to train and perform
better.
 Neural networks are not problistic.
CONCLUSION
 The computing world has a lot to gain from neural networks. Their ability to learn
by example makes them very flexible and powerful.
 Neural networks have grown extremely popular recently in the guise of “ Deep
Belief Networks.”
 They have been applied successfully to computer vision, speech recognition and
neural language processing.
 Neural Networks also contribute to other areas of research such as neurology and
psychology.
 Finally: Neural networks have a massive potential but human kind will only take
the best benefits as these networks are combined wit computing.