Python has become the most popular data science and machine learning programming language. But in order to obtain effective data and results, it’s important that you have a basic understanding of how ...

Machine learning requires a lot of computing power; early adopters simply lacked the infrastructure to make it cost-effective.

This book is edited by Li Hui and Chen Yanyan, with associate editors Yang Yu, Gao Yong, Zhang Qiaosheng, Bi Ye, and Liu Dengzhi. It is rich in content, covering 32 theories and 32 practical cases, ...

Machine learning at the edge has reached a point where it’s no longer enough to focus solely on the neural network’s first steps in the world—the deployment phase is here.

The question isn't whether a machine learning model will systematically discriminate against people -- it's who, when, and how.

More organizations are recognizing the benefits of machine learning and AI to improve inventory forecasting accuracy and optimize stock replenishment.

This guide provides a simple definition for deep learning that helps differentiate it from machine learning and AI along with eight practical examples of how deep learning is used today.

Transposit Joins Grammarly and SignalFire in Virtual Discussion on Human-in-the-loop DevOps and Machine Learning – Practical Lessons to Optimize Your Team and Your Data ...

Machine learning in healthcare is a growing area of application of artificial intelligence in medicine. It is used in many areas covering classification and prediction problems. Artificial ...

A Seattle startup is smoothing out the rough spots of putting trained machine learning programs into production.

An overview of artificial intelligence (AI) and machine learning (ML) technology, including a description of how machines can be designed to learn on their own, through supervised and unsupervised ...

"Our work focused on groundwater monitoring as a case study, but quantum machine learning has broad applications in any field requiring fast, detailed analysis of large datasets.