Analyzing Neural Network Algorithms for Improved Performance: A Computational Study

Abstract

Vehbi Ramaj, Rame Elezaj and Elvir Cajic

Machine learning is an area of artificial intelligence that deals with the development of algorithms and models for automatically detecting patterns and making inferences from data. Neural networks are one of the most popular machine learning models that simulate the learning process of the brain and are widely used in various fields such as pattern recognition, prediction and control. Matlab is a popular programming language in the field of machine learning due to its ease of use and numerous libraries that contain the implementation of various machine learning algorithms.

In this paper, we will present the simulation of machine learning in neural networks using different algorithms in Matlab. We will describe several algorithms such as feedforward neural network, convolutional neural network and deep neural network. Also, we will show how these algorithms are applied in practice using different datasets. Finally, we will compare the performance of different algorithms and analyze their advantages and disadvantages.

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