Application Of Gradient Descent Method In Machine Learning

For the optimization of loss function in machine learning,the basic iterative steps of gradient descent method and its variant algorithm are discussed,and gradient descent method is used to minimize the cost function.The optimal solution is solved by iterative method.Based on the implementation of MATLAB program,linear regression model and logistic regression classification model are analyzed.By comparing the convergence speed and complexity of the algorithm,the result show that choosing the better optimization algorithm according to the different data sets can make work faster.In this paper,the application of gradient descent method in machine learning is studied.The main content is as follows:In the first chapter,the research background and research progress of the related machine learning algorithms are introduced,and the main results of this paper are given.In the second chapter,the theoretical basic knowledge used in this study is mainly introduced.Firstly,the optimization theory,gradient descent method and Newton method are introduced,then the linear model is introduced,and finally the learning algorithm of logistic regression model is described,including the generalized multinomial logistic regression.The theoretical basis knowledge is for the following research.In the third chapter,the classical optimization algorithm in optimization theory and linear model are adopted in this chapter.linear regression using the form of square loss function,the loss function of logistic regression model is trained by maximum log-likelihood estimation.Based on linear regression and logistic regression model,the example of gradient descent algorithm is analyzed.In particular,the convergence rate of the batch gradient descent method and the random gradient descent method is compared.At the same time,the application conditions,advantages and disadvantages of different optimization methods are analyzed.In the last chapter,the content of this paper is summarized and then the further prospects for this paper is made.