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 an AI Glossary & Dictionary for “S”:
Sampling: Selecting a subset of data from a larger dataset to make statistical inferences or train models.
Scaling: Adjusting numerical data into a specified range to improve learning stability.
Schedule: In optimization, a plan for adjusting the learning rate over the course of training.
Self‑Attention: A mechanism that allows models to weigh the importance of different parts of an input sequence relative to each other.
Self‑Supervised Learning: Learning from data without explicit labels by creating proxy tasks, such as predicting the next word in a sentence.
Semantic Segmentation: Assigning a class label to each pixel in an image to delineate objects and backgrounds.
Sentiment Analysis: Using NLP techniques to determine the emotional tone of text, such as positive, negative or neutral.
Sequence Model: Models designed to handle sequential data, such as time series or language, by capturing order and context.
Sequential Data: Data where order matters, such as clickstreams, sensor readings or sentences.
Sigmoid Function: An activation function that maps input values into a range between 0 and 1.
Similarity Metric: A measure that quantifies how alike two data points are, used in clustering and recommendation.
Singular Value Decomposition: A matrix factorisation technique used for dimensionality reduction and latent semantic analysis.
Skip Connection: A direct connection that bypasses one or more layers in a neural network, helping to combat vanishing gradients.
Softmax: An activation function that converts a vector of values into probabilities that sum to 1.
Specificity: The proportion of true negatives correctly identified by a model, important for evaluating classifiers.
Speech Recognition: Converting spoken language into text using AI models and signal processing.
Standardization: Transforming data to have zero mean and unit variance.
State: In reinforcement learning, the current situation that the agent observes and uses to decide actions.
Statistical Learning: Methods that use statistics to infer relationships from data and make predictions.
Stochastic Gradient Descent: An iterative optimisation algorithm that updates model parameters using random subsets (batches) of data.
Style Transfer: Recombining the style of one image with the content of another using neural networks, useful for creative campaigns.
Supervised Learning: Training models on labelled data where the correct output is provided for each input.
Support Vector Machine: A supervised learning algorithm that finds the optimal hyperplane separating classes in the feature space.
Synthetic Data: Data artificially generated to augment training sets or protect privacy while preserving statistical properties.
This concludes the AI Glossary & Dictionary for “S”.