Glossary

**Naive Bayes**: A family of probabilistic classifiers based on applying Bayes' theorem with the assumption of independence between the features, often used in text classification tasks.

**Nash Equilibrium**: A concept in game theory where no player can benefit by changing their strategy while the other players keep their strategies unchanged, often used in economics and multi-agent systems.

**Natural Language Processing (NLP)**: A field of artificial intelligence focused on the interaction between computers and humans through natural language, involving tasks like translation, sentiment analysis, and speech recognition.

**Neural Network**: A computational model inspired by the human brain, consisting of layers of interconnected nodes (neurons) that process input data to generate outputs, often used in deep learning tasks.

**Neural Architecture Search (NAS)**: The process of automating the design of neural network architectures, often using machine learning techniques to optimize the architecture for a specific task.

**Nesterov Accelerated Gradient (NAG)**: An optimization technique that improves upon momentum by incorporating the future gradient into the momentum term, helping to accelerate convergence in gradient descent algorithms.

**Non-Negative Matrix Factorization (NMF)**: A group of algorithms in linear algebra where a matrix is factorized into two matrices with the constraint that all elements in the resulting matrices are non-negative, often used in text mining and image processing.

**Normalization**: The process of scaling individual data samples to have a mean of zero and a standard deviation of one, or to a specific range, often used to improve the performance and stability of machine learning algorithms.

**Neural Style Transfer**: A technique in deep learning that applies the style of one image to the content of another, often used in creative applications like generating artistic images.

**Natural Gradient Descent**: An optimization algorithm that adjusts the gradient descent step by considering the geometry of the parameter space, often leading to faster convergence in certain machine learning models.

**Noisy Student Training**: A semi-supervised learning technique where a model is trained on both labeled and unlabeled data, using noise to regularize the model and improve robustness.

**Nonlinear Activation Function**: A function applied to the output of a neuron in a neural network to introduce nonlinearity, enabling the network to learn complex patterns; examples include ReLU, tanh, and sigmoid functions.

**Node Embedding**: A technique used to represent nodes in a graph as vectors in a continuous vector space, capturing the structural properties of the graph for tasks like node classification and link prediction.

**Nearest Neighbor Algorithm**: A type of algorithm, such as k-nearest neighbors (KNN), that classifies data points based on the closest training examples in the feature space.

**Numerical Stability**: The property of an algorithm that ensures small changes in input or intermediate steps do not cause large changes in the output, often crucial in training machine learning models.

**Natural Gradient**: A gradient that takes into account the curvature of the parameter space, leading to more efficient updates in certain optimization problems, especially in deep learning.

**Newton's Method**: An optimization algorithm that uses second-order derivatives (Hessian matrix) to find the roots of a function, often used for finding local minima or maxima in machine learning.

**Noise Injection**: A regularization technique where noise is added to the input data, the weights, or the gradients during training, helping to prevent overfitting and improve generalization.

**Numerical Differentiation**: The process of estimating the derivative of a function using finite differences, often used in optimization when analytical derivatives are difficult to obtain.

**Normal Distribution**: A continuous probability distribution characterized by its bell-shaped curve, symmetric about the mean, often used in statistics and machine learning to model random variables.

**Neural Collaborative Filtering**: A deep learning-based approach to recommendation systems that models complex user-item interactions by combining neural networks with collaborative filtering techniques.

**Negative Sampling**: A technique used in training word embeddings and other models where a subset of negative examples is sampled for each positive example, reducing the computational cost of training.

**Null Hypothesis**: A default hypothesis that there is no effect or no difference, often tested against an alternative hypothesis in statistical hypothesis testing.

**Named Entity Recognition (NER)**: A task in natural language processing that involves identifying and classifying entities such as names, dates, and locations within a text.

**Neural Ordinary Differential Equations (Neural ODEs)**: A class of models where the forward pass of a neural network is defined by a differential equation, allowing for continuous-time modeling and memory-efficient computation.

**Natural Evolution Strategies (NES)**: A family of optimization algorithms that use a natural gradient estimated from samples to optimize black-box functions, often used in reinforcement learning.

**Non-Convex Optimization**: The process of optimizing a non-convex function, which may have multiple local minima and maxima, making the optimization more challenging than convex optimization.

**Normal Equation**: A closed-form solution to the linear regression problem, obtained by setting the gradient of the loss function to zero and solving for the model parameters.

**Nested Cross-Validation**: A model validation technique that involves two loops of cross-validation: an inner loop for hyperparameter tuning and an outer loop for model evaluation, providing an unbiased estimate of model performance.

**Naive Forecast**: A simple forecasting method that uses the most recent observation as the forecast for future observations, often used as a baseline in time series analysis.

**Non-Maximum Suppression (NMS)**: A technique used in object detection to remove redundant bounding boxes by selecting the box with the highest confidence score and suppressing all other overlapping boxes.

**Numerical Optimization**: A branch of mathematical optimization that deals with the optimization of functions with continuous variables, often used in training machine learning models.

**Neural Turing Machine (NTM)**: A neural network architecture that combines a neural network with an external memory, allowing the model to perform tasks requiring memory, such as sequence processing and algorithm learning.

**Network Embedding**: The process of learning low-dimensional representations of nodes or edges in a network, often used in social network analysis, recommendation systems, and graph-based machine learning tasks.

**Natural Image Statistics**: The study of the statistical properties of images from the natural world, often used in computer vision and image processing to develop models that mimic human visual perception.

**Nonlinear Least Squares**: A form of regression analysis used to fit a model to data by minimizing the sum of squared differences between observed and predicted values, where the model is nonlinear in its parameters.

**Newton-Raphson Method**: An iterative numerical method used to find roots of a real-valued function, often applied in optimization and machine learning for finding maximum likelihood estimates.

**Non-Negative Matrix Factorization (NMF)**: A dimensionality reduction technique where a matrix is factorized into two lower-rank matrices with non-negative elements, often used in text mining and image analysis.

**Nesterov Momentum**: A variant of the momentum optimization technique that anticipates the future position of the parameters, leading to faster convergence in gradient-based optimization algorithms.