AI Glossary & Dictionary for “G”
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 “G”:
GAN (Generative Adversarial Network): A framework where two neural networks – a generator and a discriminator – compete, leading to the creation of realistic synthetic data.
Gaussian Distribution: A common continuous probability distribution characterised by its bell shape; many models assume normality in errors.
Generalization: The ability of a model to perform well on new, unseen data rather than only on its training set.
Generative AI: A class of AI models that can produce new content such as text, images, video or code by learning underlying patterns in data
Generative Pre‑Training: Training a model on a large corpus of unlabelled data to learn general features before fine‑tuning on specific tasks.
Genetic Algorithm: An optimization method inspired by natural selection that evolves solutions by selection, crossover and mutation.
Geo‑Targeting: Serving ads or content to users based on their geographic location, often combined with machine‑learning models to refine targeting.
Gini Impurity: A metric used in decision trees to measure how often a randomly chosen element would be incorrectly classified.
Global Optimization: Searching for the best solution across the entire parameter space rather than settling for a local optimum.
GPU Acceleration: Using graphics processing units to speed up parallelizable computations in AI workflows.
Gradient Descent: An algorithm for minimizing a function by iteratively moving in the direction of the steepest decrease.
Graph Neural Network: A neural architecture designed to work directly with graph‑structured data, such as social networks or knowledge graphs.
Greedy Algorithm: A strategy that makes the locally optimal choice at each step with the hope of finding a global optimum.
Grid Search: Systematically searching through a parameter space to find the best combination of hyperparameters for a model.
Ground Truth: The accurate, verified data used to train and evaluate models, often collected through human annotation.
Growth Hacking: Experimenting across marketing channels and product development to rapidly grow a customer base, increasingly supported by AI analytics.
This concludes the AI Glossary & Dictionary for “G”.