AI Glossary & Dictionary for “S”
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 “S”:
Sampling (of data)
A technique for selecting representative subsets of data for training or analysis. Like taste-testing a few spoonfuls of soup to check the flavor of the whole pot.
Scaling
In machine learning, adjusting feature values to a consistent range for better model performance. Picture standardizing all measurements to the same unit system for fair comparison.
Schedule
A predefined plan for adjusting learning parameters during training. Similar to a workout plan that progressively increases intensity over time.
Self-Attention
A mechanism that allows AI models to weigh the importance of different parts of input data. Imagine a reader who knows which parts of a sentence are most crucial for understanding its meaning.
Self-Supervised Learning
A training approach where models generate their own supervision from unlabeled data. Picture a student figuring out patterns in a textbook without having an answer key.
Semantic Segmentation
A technique that assigns meaning to each pixel in an image. Like coloring different parts of a photo based on what they represent – sky, trees, buildings, etc.
Sensitivity
In AI, the measure of a model’s ability to correctly identify positive cases. Similar to a medical test’s ability to detect a specific condition when it’s present.
Sequence Model
A model designed to work with ordered data like text or time series. Picture a system that understands how words in a sentence relate to each other based on their order.
Sequential Data
Data where the order of elements matters. Like understanding a story where the sequence of events is crucial to its meaning.
Sigmoid Function
An activation function that maps inputs to values between 0 and 1. Imagine a dimmer switch that smoothly transitions between off and full brightness.
Similarity Metric
A measure of how alike two items are in a meaningful way. Picture a system that can tell how similar two songs are based on their musical features.
Singular Value Decomposition
A matrix factorization technique that breaks down complex data relationships. Like separating a complex painting into its basic shapes, colors, and patterns.
Skip Connection
A neural network connection that bypasses one or more layers. Similar to taking a shortcut that connects different parts of a building directly.
Softmax
A function that converts numbers into probabilities that sum to one. Picture converting exam scores into percentage chances of being the highest grade.
Sparse Matrix
A data structure mostly filled with zeros, storing only non-zero values. Like a spreadsheet tracking social connections where most people don’t know each other.
Spatial Transformer
A module that helps neural networks focus on relevant parts of input images. Imagine a smart magnifying glass that automatically zooms in on important details.
Specificity
A measure of an AI model’s ability to correctly identify negative cases. Like a security system’s accuracy in avoiding false alarms.
Spectral Clustering
A technique that groups data points using eigenvalues of similarity matrices. Picture organizing music into genres based on subtle patterns in their sound frequencies.
Speech Recognition
Technology that converts spoken language into text. Like having a highly skilled transcriptionist who can accurately write down everything being said.
Stacked Autoencoder
Multiple autoencoders combined to learn increasingly complex data representations. Similar to an artist learning to capture scenes with increasing levels of detail.
Stacking
An ensemble technique that combines predictions from multiple AI models. Picture a panel of experts each making predictions, with another expert weighing their opinions.
Standardization
A process that adjusts data to have zero mean and unit variance. Like normalizing test scores so they can be fairly compared across different exams.
State
In reinforcement learning, the current situation or configuration an agent observes. Picture a game where the state includes the position of all pieces on the board.
Statistical Learning
In AI, a framework for learning patterns from data using statistical principles. Like discovering the rules of grammar by analyzing many examples of proper writing.
Stochastic Gradient Descent
An optimization algorithm that uses random samples to update model parameters. Imagine learning to cook by adjusting recipes based on feedback from random taste testers.
Style Transfer
A technique for applying the artistic style of one image to another. Like teaching an AI to repaint a photograph in the style of a famous artist.
Supervised Learning
A learning approach where models train on labeled examples. Picture a student learning from problems with provided solutions.
Support Vector Machine
A classification algorithm that finds the best boundary between different categories. Like drawing the clearest possible line between two groups in a crowd.
Synthetic Data
Artificially generated data that mimics real data characteristics. Similar to creating realistic practice scenarios for training purposes.
This concludes our AI Glossary & Dictionary for “S.”
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