Machine learning models—especially large-scale ones like GPT, BERT, or DALL·E—are trained using enormous volumes of data.

The research paper, titled "Machine learning models based on hyperspectral imaging for pre-harvest tomato fruit quality monitoring," was published in Computers and Electronics in Agriculture.

Finding relationships between bio-signals and health outcomes is complicated for many reasons, including sorting out irrelevant data.

The potential for machine learning to transform data-intensive businesses is undeniable, but realizing this potential requires more than just an investment in technology.

Methane (CH4) pyrolysis, a reaction that produces hydrogen without emitting carbon dioxide, often utilizes molten media ...

The science of catching liars and detecting deception, the authors show, is leaning more and more on AI and machine learning, which can analyze and interpret different types of data, demonstrating a ...

Strategies to reduce data bias in machine learning Chances are that you’re familiar with the concept of bias. It is widespread, turning up in discussions about scientific discoveries, politics ...

A crucial part of the machine learning lifecycle is managing data drift to ensure the model remains effective and continues to provide business value. Data is an ever-changing landscape, after all.

Machine learning uses algorithms to turn a data set into a model that can identify patterns or make predictions from new data. Which algorithm works best depends on the problem.

Check out these best practices that are designed to help your data preparation initiatives in machine learning.