AI Glossary & Dictionary for “P”
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 “P”:
Padding: Adding extra pixels or values around data, particularly images, to preserve dimensions during convolution operations.
Parameter: A variable internal to a model whose value is learned during training.
Partial Derivative: The derivative of a multivariable function with respect to one variable while holding others constant; used in gradient computation.
Pattern Recognition: The ability of an algorithm to detect patterns and regularities in data.
Perceptron: A simple type of neural network that makes binary classifications based on a weighted sum of inputs.
Performance Metric: A measure such as accuracy, AUC or ROAS used to evaluate how well a model or campaign performs.
Personalization: Customizing content, products or experiences to individual users using AI‑driven insights.
Pipeline: A series of data processing steps that transform raw data into features, feed it into a model and output predictions.
Policy: In reinforcement learning, a strategy that specifies the action a model should take in each state.
Pool: In convolutional networks, the operation that down‑samples feature maps by summarizing nearby values.
Position Embedding: Adding positional information to token embeddings so that models can understand order in sequences.
Predictive Analytics: Using statistical techniques and machine learning to forecast future outcomes such as purchase intent or churn.
Pre‑Processing: Preparing data before modelling, including cleaning, scaling and encoding.
Pre‑Training: Initializing a model by training it on a large general dataset before fine‑tuning on a specific task.
Probabilistic Model: A model that incorporates randomness and uncertainty into its predictions, providing distributions rather than point estimates.
Programmatic Advertising: Automated buying and selling of digital advertising inventory in real time via software platforms.
Prompt Engineering: Crafting prompts to elicit desired outputs from generative models; small changes can significantly influence responses.
Propensity Modeling: Predicting the likelihood of a particular customer action, such as buying a product, using historical data and machine learning.
Pruning: Reducing the size of neural networks by removing unimportant weights or connections to improve efficiency.
This concludes the AI Glossary & Dictionary for “P”.