Stochastic Gradient Descent Algorithms: Topics of Machine Learning

Introduction to Stochastic Gradient Descent Our previous articles investigated the importance of linear regression and batch gradient descent in machine learning modeling, as well as the intricacies of Gradient Descent models. With respect to gradient descent, here we investigate linear regression as it relates to stochastic gradient descent machine learning. Before embarking on this discussion,Continue reading “Stochastic Gradient Descent Algorithms: Topics of Machine Learning”

Batch Gradient Descent Algorithms: Topics of Machine Learning

Introduction to Batch Gradient Descent Our previous article investigated the importance of linear regression in machine learning modeling, as well as the intricacies of Gradient Descent models. With respect to gradient descent, here we investigate linear regression as it relates to batch gradient descent machine learning. Before embarking on this discussion, we would like toContinue reading “Batch Gradient Descent Algorithms: Topics of Machine Learning”

Linear Regression and Academic Literature: 100 Days of Code (3/100)

Day 3 Of 100 Days of Code Our third day of this 100 Days of Code was filled with in depth analysis of linear regression and elaboration on gradient descent algorithms. What should first be noted is the fact that we successfully published two deeply interwoven articles. The first analyzed in depth linear regression algorithms,Continue reading “Linear Regression and Academic Literature: 100 Days of Code (3/100)”

Gradient Descent Model Machine Learning: Topics of Machine Learning

Introduction to Gradient Descent Our previous article investigated the importance of linear regression in machine learning modeling, but here we focus on implementing this technique. In particular, we investigate linear regression as it relates to gradient descent machine learning. Before embarking on this discussion, we would like to provide a brief overview on the analysesContinue reading “Gradient Descent Model Machine Learning: Topics of Machine Learning”

An Introduction to the MNIST Data Set for Machine Learning: Topics of Machine Learning

An Introduction to the MNIST Data Set The present article elaborates on the essential features of the MNIST data set for training machine learning models. Within this Topics of Machine Learning series, we elaborated extensively on the various machine learning model categories that exist. Our first article to this series provided an overview to allContinue reading “An Introduction to the MNIST Data Set for Machine Learning: Topics of Machine Learning”

How to Use Directional Derivatives For Machine Learning: Topics of Partial Derivatives

Introduction By this point in our series on partial derivates, we not only have expanded upon the idea of multivariate equations, but also introduced the taking of partial derivatives and their associated tangent planes/linear approximations. If you have not referenced these items, we highly recommend it, for as we proceed in our application of theseContinue reading “How to Use Directional Derivatives For Machine Learning: Topics of Partial Derivatives”