Навчання нейронних мереж на основі алгоритму градієнтного спуску

Abstract

This paper explains the concept of gradient descent, a popular optimization algorithm used in machine learning to find the optimal parameters of a model. The paper introduces the concept of a cost function, which is used to measure the difference between the predicted and actual values in a machine learning model. The aim of gradient descent is to minimize this cost function by finding the optimal set of model parameters. To illustrate the concept of gradient descent, the paper uses a simple example of a linear regression model. Overall, the paper provides a clear and concise explanation of the key concepts and steps involved in the gradient descent algorithm, making it a useful resource for anyone interested in understanding machine learning optimization techniques.