Moving past speculation: How deterministic CPUs deliver predictable AI performance

As David Patterson observed in 1980, “A RISC potentially gains in speed merely from a simpler design.” Patterson’s principle of simplicity underpins a new alternative to speculation: A deterministic, ...
diginomica

From data streaming to AI context - how Confluent is re-defining its role in the generative AI era

Confluent is positioning itself as the "context layer for enterprise AI" with new capabilities that aim to solve the problem ...
TheServerSide

Practice Exams for Google's Data Practitioner Associate Certification

One important point: avoid the Google Cloud Associate Data Practitioner exam dumps. You should aim to get certified honestly, not by memorizing someone else’s GCP Data Practitioner braindump.
IEEE

Data Representation and Preprocessing Effects on S-Parameter Modeling of High-Speed Channels Using Machine Learning

Abstract: In this paper, the effects of data representations and preprocessing on machine learningbased S-parameter modeling of high-speed channels are investigated. Using a transformer network as a ...
InfoQ

Dreamer 4: Learning to Achieve Goals from Offline Data through Imagination Training

A monthly overview of things you need to know as an architect or aspiring architect. Unlock the full InfoQ experience by logging in! Stay updated with your favorite authors and topics, engage with ...
IEEE

A Self-Adaptive Data Preprocessing Pipeline for Machine Learning: An Automated and Dynamic Approach

Abstract: The optimization and generalization of performance of a machine learning model is profoundly influenced by efficient data preprocessing. A machine's learning model does not perform to its ...