INTRODUCTION

Course Name Student ID Discipline

Introduction to computing xxxxxx xxxxxx xxxxx

NEURAL NETWORK 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. They are mostly used for calculation actions when the networks

have database of examples for use. There are many types of Neural Networks, which have unique

strengths.

BRIEF DESCRIPTION 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 groups of neurons that perform a specific physiological function in laboratory analysis.

Artificial neural networks are made up of interconnecting artificial neurons (programming constructs that mimic the properties of biological neurons). Artificial neural networks may either be used to gain an understanding of biological neural networks, or for solving artificial intelligence problems without necessarily creating a model of a real biological system.

Easy to conceptualize Capable of detecting complex relationships. Large amount of academic research used extensively in industry for many years Provide high speed calculations. Can handle large number of feathers. Can solve any machine learning problem.

ADVANTAGES AND DISADVANTAGES OF NEURAL NETWORKS

Advantages

Disadvantages Neural networks are too much of a black box this makes them difficult to train. There are alternatives that are simpler, faster, easier to train, and perform better. Can not resolve all problems of learning machine. Neural networks are not probabilistic. Neural networks are not a substitute for understanding your problem.

CONCLUSIONThe 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've been applied successfully to computer vision, speech recognition, and natural language processing.

Perhaps the most exciting aspect of neural networks is the possibility that some day 'consious' networks might be produced. There is a number of scientists arguing that conciousness is a 'mechanical' property and that 'consious' neural networks are a realistic possibility.

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 with computing.