Natural Language Generation (NLG): Natural Language Generation is a subset of AI that focuses on generating human-like text or narratives. It’s commonly used in content generation and report writing.

Natural Language Interface: A natural language interface allows users to interact with software or AI systems using natural language commands or queries, making it user-friendly.

Natural Language Processing (NLP): A field of AI that focuses on enabling computers to understand, interpret, generate, and respond to human speech and text. It’s used in chatbots, language translation, and sentiment analysis.

Natural Language Understanding (NLU): Refers to the AI’s ability to comprehend and interpret human language beyond syntax, including context, semantics, and intent.

Natural User Interface (NUI): An interface that allows users to interact with a computer or device in a natural and intuitive way, often using gestures, voice, or touch.

Nearshore Outsourcing: Nearshore outsourcing is a business strategy that involves contracting AI development or other services to companies in neighboring or nearby countries to reduce costs and enhance collaboration.

Network Architecture: In the context of AI, network architecture refers to the design and structure of a neural network, including the arrangement of layers and nodes.

Neural Network: A neural network is a computational model inspired by the human brain’s structure and function. It’s used in machine learning to solve complex tasks, such as image recognition and speech processing.

Neural Network Pruning: A technique used to reduce the size of a neural network by removing unnecessary connections or nodes, making it more efficient for deployment.

Neural Network Weights: Neural network weights are parameters that are adjusted during training to enable the network to make accurate predictions. They determine the strength of connections between neurons.

Neural Style Transfer: An AI technique that combines the content of one image with the artistic style of another image to create visually appealing artworks or images.

Neuroevolution: An AI technique that uses evolutionary algorithms to optimize neural networks. It’s often employed to develop neural networks for specific tasks.

Neuromarketing: The application of neuroscience and AI to understand and influence consumer behavior, often used in advertising and market research.

Neuromorphic Computing: Neuromorphic computing is a type of AI hardware or architecture that mimics the structure and function of the human brain. It’s designed for efficient and brain-inspired computation.

NLP Models: Refers to specialized AI models, such as BERT or GPT-3, designed for natural language processing tasks, including text generation, sentiment analysis, and language translation.

Node: In the context of AI and machine learning, a node typically refers to a unit or processing element in a neural network, part of the network’s structure.

Noisy Data: Noisy data contains errors, inconsistencies, or irrelevant information. Handling noisy data is a common challenge in AI, as it can negatively impact model performance.

No-Code AI: Refers to AI platforms or tools that allow users to build and deploy AI applications without writing code. It simplifies AI development for non-technical users.

Non-Deterministic Algorithm: A non-deterministic algorithm is one that can produce different results for the same input, introducing an element of randomness. This can be useful in AI for exploring various solutions.

Non-Supervised learning: A type of machine learning where models are trained on unlabeled data to find patterns, clusters, or representations without explicit supervision.

Normalization: Data preprocessing method in AI that scales and transforms data to have a standard mean and variance, aiding in model training and convergence.

Numerical Optimization: Algorithms and techniques used to find the optimal values of parameters in mathematical functions, a crucial component in machine learning model training.


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