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Course Name: Name: Student ID: Discipline:

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.

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  1. Course Name: Name: Student ID: Discipline:

  2. 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. They are mostly used for calculation actions when the networks have database of example for use.

  3. 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.

  4. 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.

  5. 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.

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