AI Glossary & Dictionary for “Z”
Find the Flux+Form AI glossary & dictionary to help you make sense of common AI terms. Below you can find a AI Glossary & Dictionary for “Z”:
Zero-Shot Learning
Zero-shot learning is the ability to recognize things never seen during training. Picture a system that can identify a zebra after learning about horses and stripes separately.
Zero Padding – Zero padding is the process of adding extra zeros to data, commonly used in digital signal processing and deep learning. In convolutional neural networks (CNNs), it helps maintain spatial dimensions after applying filters, preserving important edge features. In signal processing, zero padding increases resolution in Fourier transforms, improving frequency analysis. It prevents data loss, enhances model accuracy, and ensures compatibility between different-sized inputs while reducing edge effects in convolution operations.
ZIP Loss – ZIP (Zero-Inflated Poisson) loss is a statistical loss function designed for modeling count data with excessive zeros. It combines Poisson distribution for count predictions and a separate model for excess zeros, making it useful in scenarios like insurance claims, biological data, and defect detection. ZIP loss improves predictive accuracy by accounting for both regular count occurrences and inflated zero patterns, addressing overdispersion problems in data with an unusual number of zero-valued observations.
Zipf’s Law – Zipf’s Law is a principle in linguistics, economics, and data science stating that in a given dataset, the frequency of an element is inversely proportional to its rank. In natural language, the most common word appears twice as often as the second most common, three times as often as the third, and so on. This power-law distribution applies to word frequency, city populations, and web traffic, shaping insights into data organization and ranking.
Zone Analysis – Zone analysis is a spatial data assessment technique used in urban planning, security, and business intelligence to segment areas based on specific attributes. It evaluates geographical, demographic, or economic factors to identify patterns, risks, or opportunities. In AI and machine learning, it helps analyze image regions, heatmaps, and object detection zones. By dividing areas into meaningful sections, zone analysis optimizes decision-making in real estate, transportation, crime mapping, and environmental monitoring applications.
Browse AI Terms by Letter
A B C D E F G H I J K L M N O P Q R S T U V W X Y Z