AI Glossary & Dictionary for “L”
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 “L”:
Label — A label is the correct output or category assigned to training data. Picture an answer key to a test – it’s the known correct answer that helps the model learn what’s right and wrong.
Label Smoothing — Label smoothing prevents models from becoming overconfident by slightly adjusting training labels. This is similar to teaching a student that even when they’re right, they should maintain some humility.
Labeled Data — Labeled data consists of examples paired with their correct answers or categories. Imagine a collection of photos with captions telling you what’s in each image.
Lambda Architecture — Lambda architecture processes data using both batch and stream processing methods. This resembles a restaurant that handles both reservations and walk-ins simultaneously.
Language Model — A language model predicts the probability of sequences of words or generates text. Much like a writing assistant who can predict the most likely next word in a sentence based on context.
Latent Dirichlet Allocation — Latent Dirichlet Allocation discovers topics in text documents by finding patterns of word co-occurrence. Consider it as organizing a library by noticing which books often use similar words.
Latent Feature — A latent feature is a hidden characteristic that isn’t directly observed but is inferred from data. It’s comparable to deducing someone’s interests from their shopping history.
Latent Space — Latent space is a compressed representation where similar items are close together. Picture a map where similar concepts are placed near each other, even though the similarities might not be obvious initially.
Latent Variable — A latent variable is an unobserved factor that influences observable data. This works like how mood affects someone’s behavior – invisible but influential.
Layer Normalization — Layer normalization standardizes the inputs to each layer in a neural network. Think of it like ensuring every ingredient in a recipe is at the right temperature before mixing.
Lazy Learning — Lazy learning delays processing until prediction time rather than building a model during training. This parallels a student who memorizes everything and only works out the answer when asked a specific question.
Leaky ReLU — Leaky ReLU is an activation function that allows a small gradient when the input is negative. Just as a dam might let a little water through even when closed.
Learning Rate — Learning rate controls how much a model adjusts its parameters during training. Imagine adjusting your step size while walking – too large and you might overshoot, too small and progress is very slow.
Learning Rate Decay — Learning rate decay gradually reduces the learning rate during training. This is akin to taking smaller steps as you get closer to your destination.
Learning Rate Schedule — A learning rate schedule determines how the learning rate changes during training. Similar to having a workout plan that adjusts the intensity over time.
Learning Rule — A learning rule defines how a model updates its parameters based on training data. Picture having a set of guidelines for improving at a skill.
Learning to Learn — Learning to learn, or meta-learning, involves developing strategies to learn new tasks more efficiently. This resembles developing study skills that help you master any subject faster.
Least Squares — Least squares minimizes the sum of squared differences between predicted and actual values. Think of it like finding the best-fit line through a scatter plot of points.
Leave-One-Out — Leave-one-out cross-validation tests a model by training on all but one example and testing on the left-out example. Like a cooking competition where each chef takes turns being the judge.
Linear Algebra — Linear algebra is the mathematics of linear equations and functions used in AI computations. Consider it as a mathematical toolkit for handling multiple variables simultaneously.
Linear Layer — A linear layer applies a linear transformation to its input data. Much like adjusting the volume and balance controls on a stereo system.
Linear Model — A linear model makes predictions using linear combinations of input features. This works like calculating a student’s grade based on weighted scores from different assignments.
Linear Programming — Linear programming finds the optimal solution to problems with linear constraints and objectives. Imagine planning a budget to maximize savings while meeting all necessary expenses.
Linear Regression — Linear regression predicts continuous values by finding the best-fitting straight line through data points. Picture drawing a trend line through scattered points to make predictions.
Linguistic Feature — A linguistic feature is a characteristic of language used in natural language processing. It’s like the building blocks of language – from words to grammar patterns.
LLama — Llama is an open source, large language model developed by Meta.
Local Minimum — A local minimum is a point where the error is lower than nearby points but may not be the lowest possible. This is similar to being in a valley that seems lowest until you explore further.
Local Optima — Local optima are solutions that are optimal within a neighboring set of solutions but may not be globally best. Like reaching what seems to be the highest peak, only to notice taller mountains in the distance.
Local Search — Local search explores nearby solutions to find improvements. This resembles house-hunting by thoroughly exploring one neighborhood before moving to another area.
Localization — Localization identifies the location of objects within images or spatial data. Picture playing a game of “Where’s Waldo?” with multiple objects.
Log Likelihood — Log likelihood measures how well a statistical model fits observed data. It’s comparable to scoring how well a weather forecast matched actual weather patterns.
Logistic Regression — Logistic regression predicts binary outcomes by estimating probabilities. Similar to a doctor assessing symptoms to determine if a patient has a specific condition.
Long Short-Term Memory — Long Short-Term Memory (LSTM) is a neural network architecture that can learn long-term dependencies in sequential data. Think of it like a smart notepad that knows what to remember and what to forget.
Loss Function — A loss function measures how far a model’s predictions are from the true values. Imagine a scoring system that shows how many mistakes were made during training.
Low-Pass Filter — A low-pass filter removes high-frequency components from data. This is like using sunglasses that filter out harsh glare while preserving the main features.
Low-Rank Approximation — Low-rank approximation simplifies complex data by capturing its most important patterns. Picture creating a simple sketch that captures the main features of a detailed painting.
This concludes the AI Glossary & Dictionary for “L.”
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