AI Fundamentals

Semantic Search improves search accuracy by understanding user intent and contextual meaning, delivering more relevant results than keyword-based systems.

Sora AI is an advanced AI platform enabling natural language understanding, automation, and intelligent insights across diverse industry applications and...

TPU (Tensor Processing Unit) is Google's specialized hardware accelerator designed to speed up machine learning tasks and deep learning model computations.

A Test is a procedure to evaluate and validate system functionality, quality, or performance, ensuring expected behavior and detecting defects early.

Test Data is the dataset used to evaluate system performance and accuracy, essential for validating models, software, or algorithms effectively.

Tokenization is the process of splitting text into smaller units called tokens, essential for natural language processing and text analysis tasks.

Training data is the dataset used to teach machine learning models by example, enabling them to learn patterns and perform accurate predictions.

Transfer Learning applies pre-trained models to new tasks, boosting efficiency and accuracy when data is limited for the target problem.

Turing refers to Alan Turing's foundational concepts in computing, including the Turing Machine and Turing Test, pivotal in AI and computer science.

A vector database stores and searches high-dimensional vector embeddings, enabling efficient similarity queries for AI and machine learning data.

Zero-shot learning enables models to classify unseen categories without training examples by leveraging semantic knowledge and attribute-based reasoning.