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

In the current era of big data, the volume of information continues to grow at an unprecedented rate, giving rise to the crucial need for efficient ...

Their proposed method, outlined in a paper published in Nature Machine Intelligence, was found to be significantly more powerful than classical data compression algorithms. "In January 2023, when ...

EPFL researchers have developed a machine learning approach to compressing image data with greater accuracy than learning-free computation methods, with applications for retinal implants and other ...

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

Learn how to build and deploy a machine-learning data model in a Java-based production environment using Weka, Docker, and REST.

Data Dependency: Machine learning models require vast amounts of high-quality data, which can be difficult and expensive to obtain. Poor or biased data leads to poor model performance and biased ...

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 ...