Introduction Course Name Cs 101 Student ID BC

Introduction Course Name: Cs 101 Student ID: BC 090403070 Student Name: Tahir Ramzan Study Program: BS information Technology Contact; tahirramzan 1@yahoo. com (+923155515767) Need any help in any CS course feel free to ask…!!!

Topic Name Neural Network Learning Paradigm with Applications Topic Introduction In the artificial intelligence field, artificial neural networks have been applied successfully to speech recognition, image analysis and adaptive control, in order to construct software agents (in computer and video games) or autonomous robots. Most of the currently employed artificial neural networks for artificial intelligence are based on statistical estimations, classification optimization and control theory. The cognitive modeling field involves the physical or mathematical modeling of the behavior of neural systems; ranging from the individual neural level (e. g. modeling the spike response curves of neurons to a stimulus), through the neural cluster level (e. g. modeling the release and effects of dopamine in the basal ganglia) to the complete organism (e. g. behavioral modeling of the organism's response to stimuli). Artificial intelligence, cognitive modeling, and neural networks are information processing paradigms inspired by the way biological neural systems process data.

Description of Neural Network Neural networks, as used in artificial intelligence, have traditionally been viewed as simplified models of neural processing in the brain, even though the relation between this model and brain biological architecture is debated, as it is not clear to what degree artificial neural networks mirror brain function. A subject of current research in computational neuroscience is the question surrounding the degree of complexity and the properties that individual neural elements should have to reproduce something resembling animal cognition. Historically, computers evolved from the von Neumann model, which is based on sequential processing and execution of explicit instructions. On the other hand, the origins of neural networks are based on efforts to model information processing in biological systems, which may rely largely on parallel processing as well as implicit instructions based on recognition of patterns of 'sensory' input from external sources. In other words, at its very heart a neural network is a complex statistical processor (as opposed to being tasked to sequentially process and execute). Neural coding is concerned with how sensory and other information is represented in the brain by neurons. The main goal of studying neural coding is to characterize the relationship between the stimulus and the individual or ensemble neuronal responses and the relationship among electrical activity of the neurons in the ensemble.

Advantages & disadvantages of Neural Networks Advantages Capable of handling large number of features Capable of detecting complex relationships Can solve numerous complex and miscellaneous tasks Easy to theorize Identify all possible connections Large amount of academic study Need less formal arithmetical training Speedy and fast calculation Several training algorithms are accessible Disadvantages Computing one input path needs calculating the network Can’t resolve all machine learning difficulties Difficult to implement trial and error for choosing many neurons Experimental nature of model improvement Greater computational load Not a magic stick Problematic to interpret Slow training time In industrial robotics they need a large variety of training for real-world operation

Conclusion Today’s Computers and Technology orb can learn and achieve a lot from neural networks. Neural networks capability to learn from examples makes them great and influential. Moreover it is not essential to create an algorithm to complete a particular job. Have no need to know the inner parameters of the task. Furthermore neural networks are well-matched for systems due to their fast reaction and calculating times as to their consistent structure. They also add great values to other fields of medical science like neurology and psychology; Neural networks used to design different parts of organisms and to inspect the inner structures of the brain. Possibly the great inspiring feature which also seems to be promising is, in the future conscious networks might come. Many experts and scientists claim that, consciousness is a mechanical property and it seems possible to create conscious neural networks. In conclusion, nevertheless neural networks have a massive potential but human kind can only take the great benefits, as soon as these networks are combined with computing.