Labeling: The process of assigning meaningful tags or categories to data, usually done to create a labeled dataset for training machine learning models.
Label Propagation: A semi-supervised learning technique that leverages labeled data to propagate information and labels to unlabeled instances within a graph or network.
Language Model: A type of AI model that is trained to understand and generate human language, often used for tasks like text generation, translation, and sentiment analysis.
Latent Dirichlet Allocation (LDA): A probabilistic model used for topic modeling, where documents are represented as mixtures of topics, and words are assigned to topics.
Latent Space: A multi-dimensional space where data points are mapped by AI models, such as autoencoders or variational autoencoders, enabling meaningful transformations and generation.
Latent Variable: In AI and statistics, a variable that is not directly observed but is inferred through observed variables. Latent variables are used in models like latent factor analysis and generative models.
Learning Rate: A hyperparameter in machine learning algorithms that determines the step size at which the model adjusts its parameters during training. It influences how quickly the model converges to a solution.
Leveraged Learning: An approach that combines machine learning techniques with domain expertise to improve the performance of AI models, particularly in cases with limited data.
Lexical Analysis: The process of analyzing text to identify and categorize words, often used in natural language processing (NLP) for tasks like tokenization and part-of-speech tagging.
Lifelong Learning: An AI paradigm focused on developing models that can continuously learn and adapt to new tasks or information without forgetting previously learned knowledge.
Linear Regression: A basic statistical technique used in machine learning to model the relationship between one or more independent variables and a dependent variable.
Logistic Regression: A type of statistical model that calculates the probability of an input belonging to one of two classes and makes predictions based on a logistic function, commonly used for binary classification tasks.
Low-Code AI: A platform or tool that enables users to create and deploy AI applications with minimal coding or programming knowledge, making AI development more accessible.
Low-Shot Learning: A technique where AI models are trained on a small number of examples for each class, simulating scenarios with limited labeled data.
LSTM (Long Short-Term Memory): A type of recurrent neural network (RNN) architecture designed to capture long-range dependencies in data, commonly used for sequential data like text and speech.
