TMCnet

Data Normalization vs. Standardization: What's the Difference and When to Use Each

Data Normalization vs. Standardization is one of the most foundational yet often misunderstood topics in machine learning and ...
The New York Times

How ‘Stranger Things’ Defined the Era of the Algorithm

A nostalgic hit built out of vintage pop-culture references captured the “If you liked that, you’ll like this” spirit of Netflix. In the summer of 2016, streaming TV was still figuring itself out.
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 ...
GitHub

Orthogonal Gradient Descent (OGD) for Continual Learning

This repository explores the concept of Orthogonal Gradient Descent (OGD) as a method to mitigate catastrophic forgetting in deep neural networks during continual learning scenarios. Catastrophic ...
Scientific Research Publishing

Bottou, L. (2010) Large-Scale Machine Learning with Stochastic Gradient Descent ...

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 ...
来自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 ...
IEEE

Classifying Personality Traits from Text Data: A Machine Learning Approach Using Stochastic ...

Abstract: Currently, In recent years, machine learning approaches have attracted much interest from academic researchers and organizations in various fields, including psychology. Several researchers ...