Scientific Research Publishing

Ruder, S. (2016) An Overview of Gradient Descent Optimization Algorithms. arXiv Preprint.

ABSTRACT: Artificial deep neural networks (ADNNs) have become a cornerstone of modern machine learning, but they are not immune to challenges. One of the most significant problems plaguing ADNNs is ...
IEEE

Learning-Enhanced Riemannian Gradient Descent Method for Transmit–Receive Joint Design Toward ISRJ Suppression

Abstract: The interrupted sampling repeater jamming (ISRJ) can create false targets that obscure real targets, leading to radar target detection failures. This study investigates the ISRJ ...
Scientific Research Publishing

Bottou, L. (2010) Large-Scale Machine Learning with Stochastic Gradient Descent. Proceedings of COMPSTAT 2010, Paris, 22-27 August 2010, 177-186.

ABSTRACT: This paper investigates the application of machine learning techniques to optimize complex spray-drying operations in manufacturing environments. Using a mixed-methods approach that combines ...
Forbes

Workplace Learning Has An Attitude Problem—And How To Fix It

Forbes contributors publish independent expert analyses and insights. CEO & Co-author of Mastery - Why Deeper Learning is Essential... For all the talk about the importance of lifelong learning for ...
Hosted on MSN

Linear Regression Gradient Descent ¦ Machine Learning ¦ Explained Simply

Understand what is Linear Regression Gradient Descent in Machine Learning and how it is used. Linear Regression Gradient Descent is an algorithm we use to minimize the cost function value, so as to ...
Wired

Pioneers of Reinforcement Learning Win the Turing Award

In the 1980s, Andrew Barto and Rich Sutton were considered eccentric devotees to an elegant but ultimately doomed idea—having machines learn, as humans and animals do, from experience. Decades on, ...