Optimization of Machine Learning Process Using Parallel Computing

The aim of this paper is to discuss the use of parallel computing in the supervised machine learning processes in order to reduce the computation time. This way of computing has gained popularity because sequential computing is often insufficient for large scale problems like complex simulations or real time tasks. After presenting the foundations of machine learning and neural network algorithms as well as three types of parallel models, the author briefly characterized the development of the experiments carried out and the results obtained. The experiments on image recognition, ran on five sets of empirical data, prove a significant reduction in calculation time compared to classical algorithms. At the end, possible directions of further research concerning parallel optimization of calculation time in the supervised perceptron learning processes were shortly outlined.