Sign up for our newsletter and get the latest big data news and analysis.

Search Results for: machine learning

Best of arXiv.org for AI, Machine Learning, and Deep Learning – September 2021

In this recurring monthly feature, we will filter all the recent research papers appearing in the arXiv.org preprint server for subjects relating to AI, machine learning and deep learning – from disciplines including statistics, mathematics and computer science – and provide you with a useful “best of” list for the month.

Low Code Data Science is Not the Same as Automated Machine Learning

[SPONSORED POST] In this special guest feature, Rosaria Silipo, Ph.D., Principal Data Scientist at KNIME, discusses the difference between automated Machine Learning and low code tools for data science. Low code tools are becoming more and more popular. They allow to perform certain tasks without the need of coding. They are based on visual programming, that is on a drag & drop interface to build a pipeline of blocks, each block implementing a dedicated task.

Working Smarter: Leveraging Machine Learning to Optimize CO2 Adsorption

In a recent study published in Environmental Science and Technology, a collaborative research team from Korea University and the National University of Singapore employed a machine learning-based approach that may guide the development of future porous carbon synthesis strategies. The scientists noted that there are three core factors influencing the CO2 adsorption properties in BWDPCs: the elemental composition of the porous solid, its textural properties, and the adsorption parameters at which it operates, such as temperature and pressure. However, how these core factors should be prioritized when developing BWDPCs has remained unclear, until now.

The Next Wave of Cognitive Analytics: Graph Aware Machine Learning

In this contributed article, editorial consultant Jelani Harper discusses a number of compelling and timely topics including manifold learning, graph embeddings, and cognitive computing.

Why Even Marketers Should Understand the Value of Machine Learning

In this contributed article, Dr. Peter Day, Chief Technology Officer at Quantcast, discusses how leading marketers are making use of widely available machine learning and AI-powered solutions that can do the hard work of spotting signals in the noise. In doing so, they are elevating the quality of their decision making and driving better results.

Best of arXiv.org for AI, Machine Learning, and Deep Learning – August 2021

In this recurring monthly feature, we will filter all the recent research papers appearing in the arXiv.org preprint server for subjects relating to AI, machine learning and deep learning – from disciplines including statistics, mathematics and computer science – and provide you with a useful “best of” list for the month.

How to Organize Data Labeling for Machine Learning: Practical Approaches

In this contributed article, AI and computer vision enthusiast Melanie Johnson discusses how prior organization of data labeling for a machine learning project is key to success. Organizing data labeling for machine learning is not a one sitting job, yet a single error by a data labeler may cost you a fortune. Now, you probably wonder how do I get high-quality datasets without investing so much time and money?

Best of arXiv.org for AI, Machine Learning, and Deep Learning – July 2021

In this recurring monthly feature, we will filter all the recent research papers appearing in the arXiv.org preprint server for subjects relating to AI, machine learning and deep learning – from disciplines including statistics, mathematics and computer science – and provide you with a useful “best of” list for the month.

Book Review: Mathematics for Machine Learning

“Mathematics for Machine Learning” by Marc Peter Deisenroth, A. Aldo Faisal, and Cheng Soon Ong, published by Cambridge University Press, is an excellent way to learn the math behind the models. This review shall highlight all the ways this book is special among the competition. Of all the books I’ve reviewed thus far, this is my favorite. Read on to learn why.

Best of arXiv.org for AI, Machine Learning, and Deep Learning – June 2021

In this recurring monthly feature, we will filter all the recent research papers appearing in the arXiv.org preprint server for subjects relating to AI, machine learning and deep learning – from disciplines including statistics, mathematics and computer science – and provide you with a useful “best of” list for the month.