Run-time or run-time computing refers to the type of computing in which multiple computing tasks occur simultaneously or at overlapping times. These tasks can be performed by individual computers, specific applications, or across the network. Run-time computing is often used in Big Data environments to process very large data sets. Careful coordination between systems and across Big Data architectures regarding task scheduling, data exchange, and memory assignment is required to operate efficiently and effectively.
Feature Engineering is one of the most labor-intensive and creative phases of the machine learning process. This process involves the transformation of raw data into more meaningful and processable properties. The basic principles of Feature Engineering include using domain knowledge, data discovery, understanding the nature of data, and problem-oriented thinking.
It places analytics into a workflow or application at the point of need and allows users to take immediate action without having to leave the app to gain more information to make a decision.
Transformer is a model that has revolutionized the world of artificial intelligence and deep learning. Used especially in natural language processing (NLP) tasks, it has achieved extraordinary success in tasks such as machine translation, text summarization, text generation and question-and-answer systems thanks to its ability to better grasp the meaning of texts.
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