TUTORIAL ON ARTIFICIAL NEURAL NETWORK

Abstract

It is undoubtful that artificial intelligence (AI) is being the trend of computer science and this trend is still ongoing in the far future even though technologies are being developed suddenly fast because computer science does not reach the limitation of approaching biological world yet. Machine learning (ML), which is a branch of AI, is a spearhead but not a key of AI because it sets first bricks to build up an infinitely long bridge from computer to human intelligence, but it is also vulnerable to environmental changes or input errors. There are three typical types of ML such as supervised learning, unsupervised learning, and reinforcement learning (RL) where RL, which is adapt progressively to environmental changes, can alleviate vulnerability of machine learning but only RL is not enough because the resilience of RL is based on iterative adjustment technique, not based on naturally inherent aspects like data mining approaches and moreover, mathematical fundamentals of RL lean forwards swing of stochastic process. Fortunately, artificial neural network, or neural network (NN) in short, can support all three types of ML including supervised learning, unsupervised learning, and RL where the implicitly regressive mechanism with high order through many layers under NN can improve the resilience of ML. Moreover, applications of NN are plentiful and multiform because three ML types are supported by NN; besides, NN training by backpropagation algorithm is simple and effective, especially for sample of data stream. Therefore, this study research is an introduction to NN with easily understandable explanations about mathematical aspects under NN as a beginning of stepping into deep learning which is based on multilayer NN. Deep learning, which is producing amazing results in the world of AI, is undoubtfully being both spearhead and key of ML with expectation that ML improved itself by deep learning will become both spearhead and key of AI, but this expectation is only for ML researchers because there are many AI subdomains are being invented and developed in such a way that we cannot understand exhaustedly. It is more important to recall that NN, which essentially simulates human neuron system, is appropriate to the philosophy of ML that constructs an infinitely long bridge from computer to human intelligence.