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 ...
Visual Studio Magazine

Tsetlin Machine Binary Classification Using C#

The goal of a machine learning binary classification problem is to predict a variable that has exactly two possible values. For example, you might want to predict the sex of a company employee (male = ...
IEEE

FPGA-Based Implementation Using Graph Spectral Features for Multiclass Brain Tumor Classification

Abstract: The growth of brain tumors (BTs) is often highly aggressive, resulting in low survival rates. Therefore, early and accurate detection is critical for improving patient outcomes. Classifying ...
IEEE

GGBDCA:Scene graph generation based on Global Gradient Balanced Distribution and Compound Attention

Abstract: Scene Graph Generation (SGG) is a key task in computer vision, aimed at automatically extracting objects and their relationships from images. Despite significant advances in SGG, the ...
Scientific Research Publishing

Ruder, S. (2017) An Overview of Gradient Descent Optimization Algorithms. arXiv: 1609.04747.

ABSTRACT: As drivers age, roadway conditions may become more challenging, particularly when normal aging is coupled with cognitive decline. Driving during lower visibility conditions, such as ...
insideHPC

RMSprop Optimization Algorithm for Gradient Descent with Neural Networks

A big part of AI and Deep Learning these days is the tuning/optimizing of the algorithms for speed and accuracy. Much of today’s deep learning algorithms involve the use of the gradient descent ...