- 3D Printing: A process for creating three-dimensional solid objects from a digital model. The object is built up layer by layer using a variety of materials, such as plastic, metal, or ceramic, and can be used for prototyping, manufacturing, or even creating custom items.
- 5G: Fifth generation of cellular mobile communications, designed to provide faster and more reliable data transmission over cellular networks.
- Actor-Critic Method: A reinforcement learning algorithm that uses two networks, an actor network to generate actions and a critic network to evaluate the actions, allowing for faster convergence and better stability.
- Adam Optimization: A combination of gradient descent and gradient-based hyperparameter optimization that uses an adaptive learning rate and momentum, allowing for faster convergence and better generalization.
- Adversarial Examples: Input samples specifically crafted to fool a machine learning model, highlighting its limitations and vulnerabilities.
- AI (Artificial Intelligence): A branch of computer science that aims to create intelligent machines that can perform tasks that typically require human intelligence, such as visual perception, speech recognition, decision-making, and language translation. AI systems use algorithms, machine learning, and other techniques to learn and perform tasks, and can be applied to various fields, such as healthcare, finance, and robotics.
- Anchor Point: A point in vector graphics used to control the shape and position of a path.
- API (Application Programming Interface): A set of protocols and tools for building software applications, allowing different components to communicate and exchange data.
- API (Application Programming Interface): A set of protocols, routines, and tools for building software applications. An API specifies how software components should interact, and allows for the creation of reusable software components that can be used by other applications or systems. APIs are widely used for integrating software systems, and for allowing third-party developers to access the functionality of an application.
- Artificial Intelligence (AI): A field of computer science that focuses on creating machines that can perform tasks that typically require human intelligence, such as speech recognition, decision making and problem solving.
- Attention Mechanism: A technique for allowing neural networks to focus on different parts of the input at different times, used to improve performance on sequential data tasks.
- Attention Mechanism: A technique used in deep learning to allow a model to focus on the most relevant parts of the input, allowing for improved performance on tasks with long sequences.
- Autoencoder: A type of neural network used for unsupervised learning, where the network is trained to reconstruct its inputs, allowing for feature extraction and dimensionality reduction.
- Autoencoders: A type of neural network that is trained to reconstruct its input data, used for dimensionality reduction and unsupervised representation learning.
- Autoencoders: A type of neural network that learns to compress and reconstruct data, used for unsupervised representation learning and data generation.
- Backup: A copy of data or a system that is stored in a separate location for the purpose of recovering it in case the original data or system is lost, damaged, or otherwise unavailable. Backups are an important part of a disaster recovery plan, and can be stored on physical media, such as tapes or disks, or in the cloud.
- Base Point: A fixed point used as a reference for calculations or measurements.
- Batch Normalization: A technique for improving the stability and performance of neural networks by normalizing the activations of each layer.
- Batch Normalization: A technique used in deep learning to normalize the activations of a layer, allowing for faster and more stable training.
- Batch Normalization: A technique used in deep learning to normalize the activations of a network across a batch of samples, improving the stability and generalization of the model.
- Bluetoot: A wireless communication technology used for exchanging data over short distances.
- Boltzmann Machines: A type of generative probabilistic model that uses a set of binary units to model the distribution of data.
- Break-even Point: The point at which total cost and total revenue are equal.
- Breakpoint: A point in a graph or curve where a sudden change occurs.
- Bullet Point List All Point Terminology and Related Definitions.
- Center Point: The central or middle point of a circle, sphere or other symmetrical shape.
- Cloud Computing: A delivery model for IT services and resources in which data and applications are stored and run on remote servers and accessed over the internet.
- Cloud Computing: A delivery model for IT services and resources, where the computing resources, such as servers, storage, and applications, are provided over the Internet and accessed on-demand. Cloud computing enables organizations to reduce their IT infrastructure costs, improve scalability and agility, and access a wider range of services and capabilities.
- Computer Vision: A field of AI that focuses on enabling machines to interpret and understand visual information, such as images and videos.
- Containerization: A method of packaging and deploying software in containers, providing a way to isolate applications and manage their dependencies.
- Control Point: A point in a curve or spline used to define the shape of the curve. -Waypoint: A point on a map or GPS navigation system that marks a specific location or route.
- Convolutional Neural Network (CNN): A type of deep learning architecture commonly used in computer vision tasks, that applies a series of filters to an image to extract features.
- Convolutional Neural Network (CNN): A type of neural network architecture designed for image and signal processing tasks, characterized by convolutional layers and pooling layers.
- Convolutional Neural Networks (ConvNets or CNNs): A type of neural network designed for image and other grid-like data processing, using convolution operations to learn local features and pooling operations to reduce the spatial dimensions.
- Convolutional Neural Networks (ConvNets or CNNs): A type of neural network that is specifically designed to process image and other grid-structured data, using convolutional and pooling layers to extract features.
- Cross-Validation: A technique for evaluating machine learning models by splitting the data into training and validation sets, using the training set to fit the model, and evaluating the model on the validation set.
- Cross-validation: A technique used in model selection to evaluate the performance of a model by splitting the dataset into training and validation sets and measuring the performance on the validation set.
- CSS (Cascading Style Sheets): A style sheet language used to describe the look and formatting of a document written in HTML (Hypertext Markup Language) or XML (Extensible Markup Language). CSS provides a way to separate the content of a document from its presentation, allowing for more flexible and efficient web design.
- Cusp: A point on a curve where the curve changes direction abruptly.
- Cybersecurity: The practice of protecting computers, networks, and digital information from unauthorized access, use, disclosure, disruption, modification, or destruction. Cybersecurity involves implementing a combination of technologies, processes, and policies to safeguard against cyberattacks, data breaches, and other security threats.
- Data Analytics: The process of analyzing and interpreting data to gain insights, make informed decisions, and improve outcomes. Data analytics can be used for a wide range of applications, including business intelligence, marketing, healthcare, and sports.
- Database: A collection of data that is stored and organized in a specific way to enable efficient retrieval and management of information. Databases are widely used to store and manage large amounts of structured data, and can be used for applications such as customer relationship management (CRM), enterprise resource planning (ERP), and e-commerce.
- Decision Point: A point at which a decision must be made.
- Deep Belief Networks (DBNs): A type of generative probabilistic model that stacks restricted Boltzmann machines to form a deep architecture, trained using unsupervised pretraining followed by supervised fine-tuning.
- Deep Learning: A type of machine learning based on artificial neural networks, designed to process and analyze large amounts of complex data.
- Deep Reinforcement Learning: A combination of deep learning and reinforcement learning where deep neural networks are used to represent the policies and value functions in reinforcement learning algorithms.
- Deep Reinforcement Learning: A type of reinforcement learning that uses neural networks to represent the policy, value, or model of the environment.
- DevOps: A set of practices that combines software development and IT operations, aimed at improving collaboration and efficiency in the development and deployment of software.
- DNS (Domain Name System): A hierarchical and decentralized naming system used to translate domain names, such as “google.com,” into IP addresses, such as “192.168.0.1.” DNS is a critical component of the Internet, allowing users to access websites and other resources using human-readable names, rather than IP addresses.
- Docker: A popular open-source platform for containerization, providing a way to build, deploy and run applications in containers.
- Dropout: A regularization technique for neural networks that randomly drops out neurons during training, preventing overfitting and improving generalization.
- Dropout: A regularization technique in deep learning where units are randomly dropped out during training to prevent overfitting.
- Dropout: A technique used in deep learning to prevent overfitting by randomly dropping out neurons during training, forcing the model to learn more robust representations.
- Early Stopping: A regularization technique for reducing overfitting in machine learning models by stopping the training process when the performance on a validation set stops improving.
- Early Stopping: A technique used in training to stop the optimization when the performance on the validation set starts to deteriorate, indicating overfitting.
- Edge Computing: A method of processing data as close to the source of the data as possible, rather than in a centralized data center or cloud.
- Endpoint: Refers to the specific destination of a data transmission in a network.
- Endpoint: A point marking the end of a line segment or arc.
- Event: A change in state of a system that can be triggered by internal or external conditions.
- Exception: An anomalous or unexpected event that occurs during program execution that requires special handling.
- Firewall: A network security system that monitors incoming and outgoing network traffic based on predetermined security rules.
- Flow control: A technique used in computer networks to prevent data transmission from overloading the receiver.
- Focal Point: A point at which light rays converge or from which they appear to diverge after reflection or refraction.
- Function as a Service (FaaS): A type of serverless computing in which individual functions are executed in response to events, providing a way to run code in a fully managed environment.
- Generative Adversarial Network (GAN): A type of generative model where two networks are trained in an adversarial manner, with one network generating data and the other network trying to distinguish generated from real data.
- Generative Adversarial Networks (GANs): A type of deep learning architecture composed of two neural networks, a generator and a discriminator, that are trained to compete against each other.
- Generative Adversarial Networks (GANs): A type of deep learning architecture that involves two neural networks, a generator and a discriminator, competing against each other to generate new data samples that are indistinguishable from real data.
- Generative Adversarial Networks (GANs): A type of neural network that consists of two networks, a generator and a discriminator, that are trained together in a zero-sum game to generate realistic samples from a target distribution.
- Generative Model: A type of machine learning model used for generative tasks, such as image synthesis or text generation.
- Generative Models: A class of machine learning models that are trained to generate new data samples that are similar to the training data, used for tasks such as image synthesis and language generation.
- Generative Pre-trained Transformer 3 (GPT-3): The latest version of OpenAI’s large-scale language model, trained on a diverse range of internet text and capable of performing a wide range of natural language tasks.
- Gradient Descent: An optimization algorithm used in deep learning to update the parameters of a model by computing the gradient of the loss function with respect to the parameters and taking a step in the opposite direction.
- Gradient Descent: An optimization algorithm used in machine learning to update the parameters of a model in order to minimize a loss function.
- GraphQL API: An API that uses a query language for APIs and a runtime for executing these queries, designed for better performance and flexibility compared to REST APIs.
- Hash: A mathematical function that transforms an input into a fixed-size output that can be used for data indexing or comparison.
- Header: Information at the beginning of a data packet or message that provides context and routing information.
- Hybrid Cloud: A computing environment that uses a combination of public and private clouds, allowing organizations to use the best combination of resources and deployment models for their specific needs.
- Hyperparameter Optimization: The process of tuning the hyperparameters of a machine learning model to improve its performance on the task.
- Hyperparameter Optimization: The process of tuning the parameters of a machine learning model, such as learning rate or number of hidden units, to achieve the best performance on a specific task.
- Hyperparameter tuning: The process of selecting the best hyperparameters for a machine learning model, to optimize its performance.
- Inflection Point: A point on a curve where the concavity changes.
- Infrastructure as a Service (IaaS): A cloud computing service model in which the cloud provider provides virtualized computing resources over the internet.
- Internet Protocol (IP): A communication protocol used to transmit data over the internet.
- Intersection Point: The point where two or more lines, curves or surfaces intersect.
- IoT (Internet of Things): A system of interrelated physical devices, vehicles, home appliances and other items embedded with electronics, software, sensors and connectivity which enables these objects to connect and exchange data.
- Key Point: A significant or important point in a process or concept.
- Kubernetes: An open-source platform for automating deployment, scaling and management of containerized applications.
- Latency: The amount of time it takes for a data packet to travel from its source to its destination.
- Load Balancer: A device or service that distributes incoming traffic across multiple servers to ensure consistent and efficient performance.
- Long Short-Term Memory (LSTM): A type of recurrent neural network that uses memory cells and gates to control the flow of information and prevent the vanishing gradient problem in training.
- Long Short-Term Memory (LSTM): A type of RNN architecture designed to handle long-term dependencies in sequential data.
- Long Short-Term Memory (LSTM): A type of RNN specifically designed to handle the vanishing gradient problem, allowing for the processing of long sequences.
- Machine Learning: A subfield of AI that focuses on the development of algorithms that can learn from and make predictions based on data.
- Malware: Software designed to harm or exploit computer systems and steal data or cause damage.
- Marker Point: A point on a map or chart used to indicate a specific location or feature.
- Markov Decision Process (MDP): A mathematical framework for modeling decision-making in uncertain environments, used in reinforcement learning.
- Microservices: An approach to software development in which a large application is broken down into smaller, independent services that can be developed, deployed and managed independently.
- Midpoint: A point equidistant from the endpoints of a line segment or arc.
- Model Selection: The process of choosing the best model architecture and hyperparameters for a given task.
- Momentum: A technique used in optimization to increase the stability of gradient descent by adding a moving average of the previous gradients to the current update.
- Monte Carlo Method: A statistical method used in reinforcement learning to estimate expected rewards by simulating many sample trajectories.
- Monte Carlo Methods: A class of algorithms that use random sampling to solve problems, used in reinforcement learning and other areas of machine learning.
- Multi-Armed Bandit: A classic problem in reinforcement learning where an agent must balance exploration and exploitation in order to maximize rewards from a set of uncertain options.
- Multi-cloud: A computing environment that uses multiple cloud services from different providers to support various business needs.
- Natural Language Processing (NLP): A subfield of AI and computational linguistics that deals with the interactions between computers and human (natural) languages.
- Network Address Translation (NAT): A technique used to remap one IP address space into another by modifying network address information in the IP header of packets while they are in transit across a traffic routing device.
- Node: A point in a graph or network where two or more lines converge.
- Overfitting: A common issue in machine learning where a model is too complex and performs well on the training data but poorly on new unseen data.
- Overfitting: A problem in machine learning where a model fits the training data too well and fails to generalize to unseen data.
- Packet: A unit of data that is transmitted over a network, consisting of a header and payload.
- Partially Observable Markov Decision Process (POMDP): A reinforcement learning problem where the state of the system is partially observable, requiring the agent to maintain a belief over the state and update it based on observations.
- Peak Point: The highest or maximum point of a curve or graph.
- Platform as a Service (PaaS): A cloud computing service model in which the cloud provider provides a platform for developing, running and managing applications, without the need to manage underlying infrastructure.
- Point Accuracy: The precision or degree of agreement between a measured point and its true position.
- Point Charge: A type of electric charge that is localized at a single point in space.
- Point Clipping: A computer graphics technique for removing parts of a shape or image that lie outside a specified region.
- Point cloud segmentation: The process of dividing a point cloud into multiple parts or regions.
- Point cloud: A set of data points in 3D space used to represent the surface of an object in computer graphics or 3D scanning.
- Point Color: The color assigned to a point in a graphic representation.
- Point Density: The number of points in a given area or volume.
- Point estimate: A single value used to estimate a population parameter.
- Point Estimate: A statistic used to estimate an unknown population parameter, such as the mean or standard deviation of a population. Point estimates are typically single values, calculated from a sample of the population, and are used to make inferences about the population based on the sample.
- Point Group: In crystallography, a group of symmetry operations that describe the symmetry of a crystal structure.
- Point load: A type of load that is applied to a structure at a single point.
- Point mutation: A type of genetic mutation where a single nucleotide is altered.
- Point of Contact: A person or place that serves as the main source of information or communication.
- Point of Departure: The starting point or basis for an action, argument or decision.
- Point of Failure: A critical component or system that, if it fails, will cause the entire system to fail.
- Point of Inflection: A point on a curve at which the concavity of the curve changes, meaning that the curve changes from being concave up to concave down, or vice versa.
- Point of Interest: A place or object of particular significance or attraction.
- Point of Maximum: A point on a curve at which the value of the function is the greatest of all the points on the curve.
- Point of Minimum: A point on a curve at which the value of the function is the smallest of all the points on the curve.
- Point of No Return: The point beyond which there is no turning back.
- Point of Origin: The starting point or source of something.
- Point of Purchase (POP): The location where a product or service is purchased, typically a retail store or online shop.
- Point of Reference: A basis for comparison or a frame of reference.
- Point of Sale (POS): The location where a transaction is processed, typically a retail store or online checkout.
- Point of Symmetry: A point on a curve at which the curve is symmetrical, meaning that it is mirror-symmetrical about that point.
- Point of View (POV): The perspective or stance from which a story or situation is observed or described.
- Point Pattern Analysis: A statistical technique used to analyze the distribution of points in a spatial context.
- Point Process Model: A mathematical model used to describe the behavior of point processes.
- Point Process Simulation: The simulation of point processes using mathematical models.
- Point Process: A type of random process that involves discrete events occurring in time or space.
- Point set topology: A branch of mathematics concerned with the study of sets of points and the properties of space that can be defined using these sets.
- Point Set: A collection of points in a mathematical space, often represented as a set of ordered or unordered pairs or triplets of numbers.
- Point Source: A single, identifiable source of pollution or emissions.
- Point Spread Function (PSF): A mathematical representation of the response of an imaging system to a point source.
- Point Terminology Definition List
- Point Value: The numerical value assigned to a point in a scoring system.
- Point: A specific location or place having no dimension, marked by a position in space.
- Point-and-Click Interface: A type of graphical user interface where users interact with the computer using a pointing device, such as a mouse.
- Point-based Graphics: A type of computer graphics where objects are represented as a set of points rather than polyggonal shapes.
- Point-of-Care Testing: A type of medical testing that is performed at or near the site of patient care.
- Point-Slope Form: A mathematical formula used to represent the equation of a line when given a point on the line and its slope.
- Point-to-Multipoint Network: A type of communication network where one central node communicates with multiple other nodes.
- Point-to-Point Protocol (PPP): A data link protocol commonly used for establishing a direct connection between two nodes on a network.
- Point-to-Surface Distance: The distance between a point and the closest point on a surface.
- Pointwise a.e. Convergence in Measure: A shorthand notation for pointwise almost everywhere convergence in measure.
- Pointwise a.e. Convergence in Probability: A shorthand notation for pointwise almost everywhere convergence in probability.
- Pointwise a.e. Convergence: A shorthand notation for pointwise almost everywhere convergence.
- Pointwise Almost Everywhere Continuity: A property of a function, where the function is almost everywhere continuous in the sense that it is continuous at almost all points in its domain.
- Pointwise Almost Everywhere Convergence in Measure: A type of convergence where a sequence of functions converges to a limit function almost everywhere in measure, meaning that the sequence converges to the limit at all but a set of points in the sample space that have measure 0.
- Pointwise Almost Everywhere Convergence in Probability: A type of convergence where a sequence of random variables converges to a limit random variable almost everywhere in probability, meaning that the sequence converges to the limit at all but a set of points in the sample space with probability 0.
- Pointwise Almost Everywhere Convergence: A type of convergence where a sequence of functions converges to a limit function almost everywhere in their respective domains, meaning that the sequence converges to the limit at all points in the domain except for a set of measure 0.
- Pointwise Almost Everywhere Convergence: A type of convergence where a sequence of functions converges to a limit function almost everywhere, meaning that the sequence converges to the limit at all but a set of points in the sample space of measure 0.
- Pointwise Almost Everywhere Convergence: A type of convergence where a sequence of functions converges to a limit function almost everywhere, meaning that the set of points where the convergence does not occur has measure 0.
- Pointwise Almost Everywhere Differentiability: A property of a function, where the function is almost everywhere differentiable in the sense that it is differentiable at almost all points in its domain.
- Pointwise Almost Periodic Function: A function in the sense of Bohr’s definition, that for any given point in its domain, the function’s values repeat almost periodically at that point.
- Pointwise Almost Sure Convergence in Capacity: A type of convergence where a sequence of functions converges to a limit function in the sense of capacity almost surely, meaning that for almost all realizations of the functions, the functions converge to the limit function in the sense of capacity at each point in their domain.
- Pointwise Almost Sure Convergence in Distribution: A type of convergence where a sequence of random variables converges to a limit random variable in distribution almost surely, meaning that for almost all realizations of the random variables, the cumulative distribution functions of the random variables converge to the cumulative distribution function of the limit random variable.
- Pointwise Almost Sure Convergence in Energy: A type of convergence where a sequence of functions converges to a limit function in the sense of energy almost surely, meaning that for almost all realizations of the functions, the functions converge to the limit function in the sense of energy at each point in their domain.
- Pointwise Almost Sure Convergence in Mean: A type of convergence where a sequence of random variables converges to a limit random variable in mean almost surely, meaning that for almost all realizations of the random variables, the mean values of the random variables converge to the mean value of the limit random variable.
- Pointwise Almost Sure Convergence in Quadratic Mean: A type of convergence where a sequence of random variables converges to a limit random variable in quadratic mean almost surely, meaning that for almost all realizations of the random variables, the quadratic means of the random variables converge to the quadratic mean of the limit random variable.
- Pointwise Almost Sure Convergence: A type of convergence where a sequence of random variables converges to a limit random variable at each point in its sample space with probability 1, almost surely.
- Pointwise Almost Uniform Convergence in Probability: A type of convergence where a sequence of random variables converges uniformly in probability to a limit random variable, meaning that for any given $\epsilon > 0$, the probability that the difference between the random variable and the limit exceeds $\epsilon$ can be made arbitrarily small on any compact subset of the sample space, except for a set of points with arbitrarily small measure.
- Pointwise Almost Uniform Convergence: A type of convergence where a sequence of functions converges uniformly to a limit function on every compact subset of its domain, except for a set of points with arbitrarily small measure.
- Pointwise Approximation: A type of approximation where a function is approximated by a sequence of simpler functions at each point in its domain.
- Pointwise Compactness: A property of a set in a topological space, where every sequence of points in the set has a convergent subsequence, meaning that there exists a convergent subsequence for each sequence of points in the set at each individual point.
- Pointwise Continuity: A property of a function where the function is continuous at each point in its domain, meaning that the limit of the function as the input approaches a particular point in the domain is equal to the value of the function at that point.
- Pointwise Convergence a.e. in Measure: A shorthand notation for pointwise almost everywhere convergence in measure.
- Pointwise Convergence a.e. in Probability: A shorthand notation for pointwise almost everywhere convergence in probability.
- Pointwise Convergence a.e.: A shorthand notation for pointwise almost everywhere convergence.
- Pointwise Convergence in Capacity in Probability: A type of convergence where a sequence of random variables converges in the sense of capacity in probability to a limit random variable, meaning that the sequence of random variables converges to the limit random variable in the sense of capacity at each point in its sample space with probability 1.
- Pointwise Convergence in Capacity: A type of convergence where a sequence of functions converges to a limit function in the sense of capacity at each point in its domain.
- Pointwise Convergence in d-dimensional Space: A type of convergence where a sequence of functions or random variables converges to a limit function or random variable in a d-dimensional space, meaning that the sequence converges to the limit at each point in the d-dimensional sample space.
- Pointwise Convergence in Distribution: A type of convergence where a sequence of random variables converges in distribution to a limit random variable at each point in the sample space, meaning that the distribution of the sequence converges to the distribution of the limit at each individual point.
- Pointwise Convergence in Distribution: A type of convergence where the cumulative distribution functions of a sequence of random variables converge to the cumulative distribution function of a limit random variable.
- Pointwise Convergence in Energy in Probability: A type of convergence where a sequence of random variables converges in the sense of energy in probability to a limit random variable, meaning that the sequence of random variables converges to the limit random variable in the sense of energy at each point in its sample space with probability 1.
- Pointwise Convergence in Energy: A type of convergence where a sequence of functions converges to a limit function in the sense of energy at each point in its domain.
- Pointwise Convergence in Lp: A type of convergence where a sequence of random variables converges in the Lp space to a limit random variable at each point in the sample space, meaning that the sequence converges to the limit in a specific sense at each individual point.
- Pointwise Convergence in Mean Square: A type of convergence where a sequence of random variables converges in mean square to a limit random variable at each point in the sample space, meaning that the expected value of the square of the difference between the sequence and the limit converges to 0 at each individual point.
- Pointwise Convergence in Mean: A type of convergence where the mean value of a sequence of random variables converges to the mean value of the limit random variable at each point in its sample space.
- Pointwise Convergence in Norm: A type of convergence where a sequence of functions converges to a limit function in a specific norm, meaning that the sequence converges to the limit in a specific sense at each individual point.
- Pointwise Convergence in Probability: A type of convergence where a sequence of random variables converges in probability to a limit random variable at each point in the sample space, meaning that the sequence converges to the limit at each individual point with probability 1.
- Pointwise Convergence in Probability: A type of convergence where a sequence of random variables converges to a limit random variable at each point in its sample space with probability 1.
- Pointwise Convergence in Quadratic Mean: A type of convergence where a sequence of random variables converges to a limit random variable in the sense of quadratic mean at each point in its sample space.
- Pointwise Convergence in Spectral Norm: A type of convergence where a sequence of matrices converges to a limit matrix in the sense of spectral norm at each point in its domain.
- Pointwise Convergence in Total Variation: A type of convergence where a sequence of measures converges to a limit measure in the sense of total variation, meaning that the sum of the absolute differences between the measures converges to 0 at each point in the sample space.
- Pointwise Convergence in Wasserstein Metric: A type of convergence where a sequence of probability measures converges to a limit probability measure in the sense of Wasserstein metric at each point in its sample space.
- Pointwise Convergence of Fourier Series: A type of convergence where a sequence of Fourier series converges to a limit function at each point in its domain, meaning that the sequence converges to the limit at each individual point.
- Pointwise Convergence of Integrals: A type of convergence where a sequence of integrals of functions converges to the integral of a limit function at each point in their respective domains, meaning that the sequence converges to the limit at each individual point.
- Pointwise Convergence of Laplace Transforms: A type of convergence where a sequence of Laplace transforms converges to a limit function at each point in the complex plane, meaning that the sequence converges to the limit at each individual point.
- Pointwise Convergence of Taylor Series: A type of convergence where a sequence of Taylor series converges to a limit function at each point in its domain, meaning that the sequence converges to the limit at each individual point.
- Pointwise convergence: A type of convergence where a sequence of functions converges to a limit function at each point in its domain.
- Pointwise Convergence: A type of convergence where a sequence of functions converges to a limit function at each point in their respective domains, meaning that the sequence converges to the limit at each individual point.
- Pointwise Convergence: A type of convergence where a sequence of functions or random variables converges to a limit function or random variable at each point in the sample space, meaning that the sequence converges to the limit at each individual point.
- Pointwise Differentiability: A property of a function where the function is differentiable at each point in its domain, meaning that the derivative exists at each point in the domain.
- Pointwise Differentiation: A process of finding the derivative of a function at each individual point in its domain, resulting in a new function that gives the slope of the original function at each point.
- Pointwise Ergodic Theorem: A result in ergodic theory which states that the time average of a function along the trajectory of a system under a given transformation converges almost everywhere to the spatial average of the function with respect to an invariant measure.
- Pointwise Ergodic Theorem: A theorem in ergodic theory that states that the average of a sequence of functions converges pointwise to the space average of the functions.
- Pointwise Infimum: A term used in mathematics to refer to the greatest lower bound of a set of functions at a particular point in the sample space.
- Pointwise Infimum: The minimum value of a set of functions at each point in their respective domains, meaning that the infimum is the minimum of the functions at each individual point.
- Pointwise Integrable Function: A function that is integrable with respect to Lebesgue measure on each compact interval in its domain.
- Pointwise Invariant: A property of a function or set, where the function or set remains unchanged under a given transformation at each point in its domain or sample space.
- Pointwise Limit: A term used in mathematics to refer to the limit of a function at a particular point in its domain, meaning the value that the function approaches as the independent variable approaches that particular point.
- Pointwise Lipschitz Continuity: A type of continuity where a function is continuous at each point in its domain and has a finite Lipschitz constant.
- Pointwise Lipschitz Continuous: A property of a function, where the function has a Lipschitz constant at each point in its domain, meaning that the function is locally Lipschitz continuous.
- Pointwise Maximum: A function that takes two or more functions as inputs and outputs the maximum value of the functions at each point in their respective domains, meaning that the output function is the maximum of the input functions at each individual point.
- Pointwise Maximum: A term used in mathematics to refer to the maximum value of a function at a particular point in its domain.
- Pointwise Maximum: The maximum value of a function at each point in its domain.
- Pointwise Minimum: A function that takes two or more functions as inputs and outputs the minimum value of the functions at each point in their respective domains, meaning that the output function is the minimum of the input functions at each individual point.
- Pointwise Minimum: A term used in mathematics to refer to the minimum value of a function at a particular point in its domain.
- Pointwise Minimum: The minimum value of a function at each point in its domain.
- Pointwise Mutual Information (PMI): A measure of association between two variables in information theory, that quantifies how much the presence of one event affects the likelihood of the occurrence of another event.
- Pointwise Product: A product of functions evaluated at each point in their respective domains, meaning that the product of the functions is calculated at each individual point.
- Pointwise Product: A product of two functions that is calculated at each point in the domain of the functions.
- Pointwise Product: A term used in mathematics to refer to the product of two functions evaluated at a particular point in their respective domains.
- Pointwise Strong Convergence in Probability: A type of convergence where a sequence of random variables converges strongly to a limit random variable in probability, meaning that the sequence converges to the limit random variable in the sense of the topology induced by the norm at each point in the sample space with probability 1.
- Pointwise Strong Convergence: A type of convergence where a sequence of functions converges strongly to a limit function at each point in their respective domains, meaning that the sequence converges to the limit in a strong sense at each individual point.
- Pointwise Strong Convergence: A type of convergence where a sequence of functions converges strongly to a limit function, meaning that the sequence of functions converges to the limit function in the sense of the topology induced by the norm at each point in its domain.
- Pointwise Strong Law of Large Numbers in Quadratic Mean: A law in probability theory that states that for independent and identically distributed random variables, the average of the square of a large number of the random variables converges to the expected value of the square almost surely, meaning that for almost all realizations of the random variables, the average of the square converges to the expected value of the square at each point in the sample space.
- Pointwise Strong Law of Large Numbers: A law in probability theory that states that for independent and identically distributed random variables, the average of a large number of the random variables converges to the expected value almost surely, meaning that for almost all realizations of the random variables, the average converges to the expected value at each point in the sample space.
- Pointwise Sum: A term used in mathematics to refer to the sum of two functions evaluated at a particular point in their respective domains.
- Pointwise Supremum: A term used in mathematics to refer to the least upper bound of a set of functions at a particular point in the sample space.
- Pointwise Supremum: The maximum value of a set of functions at each point in their respective domains, meaning that the supremum is the maximum of the functions at each individual point.
- Pointwise Weak Convergence: A type of convergence where a sequence of functions converges weakly to a limit function at each point in their respective domains, meaning that the sequence converges to the limit in a weak sense at each individual point.
- Pointwise Weak Convergence: A type of convergence where a sequence of measures converges weakly to a limit measure, meaning that the sequence of measures converges in distribution at each point in its sample space.
- Pole Point: A point used in pole-and-pole surveying for finding the location of a hidden object.
- Pole: A point on a sphere equidistant from all points on the circumference of its great circle.
- Policy Gradients: A reinforcement learning algorithm that uses gradient descent to directly optimize the policy, allowing for continuous action spaces and end-to-end learning.
- Policy Gradients: A type of reinforcement learning algorithm that learns the policy directly by adjusting the parameters based on the gradient of the expected reward with respect to the policy parameters.
- Port: A logical communication endpoint on a computer, used to identify specific types of network traffic.
- Power Point: A popular presentation software developed by Microsoft that allows users to create slideshows, including text, graphics, and multimedia elements, and to deliver the presentations on a computer or a projector.
- Precision: The degree of exactness or the amount of detail in a measurement or calculation, often expressed as the number of significant figures or decimal places.
- Predictive Modeling: A method of building a statistical or machine learning model that uses historical data to make predictions about future events or outcomes. Predictive models are often used in fields such as finance, marketing, and healthcare to forecast trends and make decisions based on predicted outcomes.
- Preprocessor: A program that processes source code written in a high-level programming language and converts it into a form that can be processed by a compiler or interpreter, often by adding additional information or transforming the code into a more convenient format.
- Pre-training: A technique in deep learning where a model is first trained on a large dataset, and then fine-tuned on a smaller task-specific dataset.
- Primary Key: A unique identifier in a database table that distinguishes each row of data in the table and allows for fast and efficient access to the data. Primary keys are used to enforce referential integrity in relational databases and to ensure that data can be retrieved and updated accurately and efficiently.
- Principal Component Analysis (PCA): A statistical method used to reduce the dimensionality of data by transforming it into a new coordinate system in which the first coordinate is the direction of maximum variance, the second coordinate is the direction of second-maximum variance, and so on. PCA is often used in fields such as machine learning and data mining to reduce the complexity of data and to identify patterns or trends in the data.
- Private Cloud: A cloud computing environment in which the infrastructure is dedicated to a single organization, providing higher levels of security and control.
- Probabilistic Graphical Model (PGM): A type of mathematical model that represents the relationships between variables in a system or process as a graphical network, where the nodes of the network represent variables and the edges represent relationships or dependencies between the variables. PGMs are often used in fields such as machine learning and artificial intelligence to represent and reason about uncertain or probabilistic information.
- Probability Density Function (PDF): A mathematical function that describes the distribution of a continuous random variable, giving the probability that the random variable takes on a specific value or falls within a specified range of values.
- Probability Mass Function (PMF): A mathematical function that describes the distribution of a discrete random variable, giving the probability that the random variable takes on a specific value.
- Procedure: A set of instructions or steps that specify how to perform a specific task or solve a specific problem, often written in a high-level programming language or in a form that can be translated into machine-executable code. Procedures are used to encapsulate and modularize the logic of a program, making it easier to understand, test, and reuse.
- Program: A set of instructions written in a programming language that specifies the tasks a computer should perform and the sequence in which they should be performed.
- Programming Language: A formal language designed to communicate instructions to a computer, consisting of a syntax and a set of rules for constructing statements. Common programming languages include Python, Java, C++, and JavaScript.
- Project Management: The discipline of planning, organizing, and managing resources to bring about the successful completion of specific project goals and objectives.
- Project Manager: The person responsible for leading a project team and ensuring that the project is completed on time, within budget, and to the required quality standards.
- Project: A temporary endeavor designed to produce a unique product, service, or result with a defined start and end time, often involving a team of people working together to achieve a common goal.
- Property: A characteristic of an object or data structure that describes some aspect of the object or data structure. Properties can be thought of as attributes or variables that belong to an object or data structure and can be read or set to reflect the state of the object or data structure.
- Protocol: A set of rules and standards that define the format, flow and error handling of data transmission over a network.
- Prototype: A preliminary version of a product, service, or system that is created for the purpose of testing and evaluating its functionality and performance, often with the aim of identifying and fixing problems before the final version is produced.
- Public Cloud: A cloud computing environment in which the infrastructure is owned and operated by a third-party provider, offering resources and services over the internet to multiple organizations.
- Q-Learning: A reinforcement learning algorithm that learns a value function for state-action pairs and uses it to make decisions.
- Q-Learning: A reinforcement learning algorithm that uses a Q-table to approximate the optimal action-value function, mapping states and actions to expected rewards.
- Query: A request for information or data from a database, typically expressed in a formal language such as SQL.
- Queue: A data structure that implements a first-in, first-out (FIFO) strategy, meaning that elements are added to the end of the queue and removed from the front of the queue. Queues are often used in computer systems to manage the processing of tasks or requests in an orderly and efficient manner.
- Random Forest: A machine learning algorithm that builds multiple decision trees and combines the results to produce a final prediction. The individual decision trees in a random forest are trained on different samples of the data and with different subsets of the features, leading to greater diversity and robustness in the overall model.
- Recurrent Neural Network (RNN): A type of deep learning architecture commonly used in NLP and time-series analysis, that processes sequential data by maintaining a hidden state that is updated at each step.
- Recurrent Neural Network (RNN): A type of neural network architecture designed for sequence processing tasks, characterized by the ability to maintain state across time steps.
- Recurrent Neural Networks (RNNs): A type of neural network designed for sequential data processing, using feedback connections that allow the network to retain information from previous time steps.
- Recurrent Neural Networks (RNNs): A type of neural network that is designed to process sequential data, using hidden state to capture information from previous time steps.
- Recursion: A programming technique in which a function calls itself in order to solve a problem or perform a task. Recursion is often used to solve problems that can be broken down into smaller subproblems that are similar to the original problem, such as traversing a tree data structure or computing the factorial of a number.
- Reference Point: A point used as a basis for measurements or comparisons.
- Referential Integrity: The property of a relational database that ensures that data is consistent and accurate by enforcing constraints between tables, such as the requirement that a foreign key value must match the value of a corresponding primary key. Referential integrity helps to prevent data anomalies such as missing or inconsistent information, and helps to ensure the accuracy and reliability of data.
- Regression: A statistical method used to model the relationship between a dependent variable and one or more independent variables, with the goal of making predictions about the value of the dependent variable based on the values of the independent variables. Regression can be used for linear and non-linear relationships, and can be applied to various types of data including continuous, categorical, and time-series data.
- Regular Expression: A sequence of characters that define a search pattern, used for matching text or extracting data from text. Regular expressions are a powerful tool for pattern matching and string processing and are used in many programming languages and text editors.
- Regularization: A technique for preventing overfitting in machine learning models by adding a penalty term to the loss function that discourages large weights.
- Regularization: A technique used in machine learning to prevent overfitting by adding a penalty term to the loss function to discourage the model from learning overly complex representations.
- Regularization: A technique used to prevent overfitting in machine learning by adding a penalty term to the loss function to encourage simpler models.
- Reinforcement Learning: A type of machine learning in which agents learn to make a sequence of decisions by performing actions and receiving rewards.
- Reinforcement Learning: A type of machine learning where an agent interacts with an environment, taking actions and receiving rewards, with the goal of maximizing the cumulative reward over time.
- Reinforcement Learning: A type of machine learning where an agent learns by interacting with an environment, receiving rewards for actions and adjusting its behavior based on the rewards.
- Relational Database: A database management system that organizes data into one or more tables, with each table consisting of a set of rows and columns, and each row representing a single record. Relationships between tables can be established using keys and foreign keys, allowing data to be easily retrieved and updated. Common relational database management systems include MySQL, Oracle, and Microsoft SQL Server.
- Remote Procedure Call (RPC): A protocol for communication between computer programs running on different computers, allowing one program to request a service from another program located on a different computer on a network. The requested service is executed on the remote computer and the results are returned to the requesting program, as if the service had been executed locally.
- REST (Representational State Transfer): An architectural style for designing web services and APIs, based on the principles of statelessness, client-server architecture, and a uniform interface. REST APIs use HTTP methods (e.g. GET, POST, PUT, DELETE) to manipulate resources and return data in a standard format, such as JSON or XML.
- REST API: A type of API that uses Representational State Transfer (REST) architecture, allowing for the creation, retrieval, update and deletion of resources through HTTP methods.
- Restricted Boltzmann Machines (RBMs): A type of generative probabilistic model, trained using contrastive divergence, that can be used as building blocks for deep belief networks.
- Root: The top-level node in a tree data structure, or the highest-level directory in a file system. The root node is the parent of all other nodes in the tree, and has no parent node itself.
- Router: A networking device that forwards data packets between computer networks based on their IP addresses.
- Routing: The process of determining the path that network traffic should take from its source to its destination, based on routing information stored in a routing table. Routing algorithms are used to determine the most efficient path based on factors such as network congestion, distance, and reliability.
- SARSA: A reinforcement learning algorithm that uses a state-action-reward-state-action (SARSA) tuple to update the Q-table, allowing for online learning.
- Scalability: The ability of a system or process to handle an increased workload or number of users without a proportionate increase in complexity or cost. Scalability is an important consideration for systems and processes that are expected to grow or change over time, such as web applications, databases, and networks.
- Search Algorithm: An algorithm that searches a data structure, such as an array, list, or tree, to find an element with a specific value or matching some criteria. Search algorithms include linear search, binary search, and depth-first search, and can vary in their efficiency and suitability for different types of data structures and search scenarios.
- Search Engine: A software system that searches the Internet or other digital resources to find information that matches a user’s query, based on algorithms that rank and prioritize the relevance and quality of the results. Examples of search engines include Google, Bing, and Yahoo! Search.
- Security: The protection of information and systems from unauthorized access, use, disclosure, disruption, modification, or destruction. Security measures include encryption, firewalls, access controls, and security policies, among others, and are used to safeguard data, networks, and systems from potential threats such as hackers, viruses, and malware.
- Semantic Web: A vision for the future of the World Wide Web, where information is represented in a machine-readable format that allows for the automatic processing and interpretation of data. The Semantic Web uses technologies such as RDF, OWL, and SPARQL to represent data in a way that is meaningful to both humans and machines, enabling greater automation and integration of data from a wide range of sources.
- Server: A computer or device that provides services to other computers or devices on a network, such as file sharing, web hosting, and email. Servers can be dedicated, meaning they are used only for server-related tasks, or they can be multi-purpose, serving multiple functions and users.
- Serverless Architecture: A computing architecture in which functions or applications are run and managed by a cloud provider, without the need for a dedicated server. Serverless computing abstracts away the infrastructure and only charges for the resources used, allowing for increased scalability and cost savings.
- Serverless Computing: A computing model in which the cloud provider is responsible for managing the infrastructure and automatically allocates resources as needed, allowing developers to focus on writing code without worrying about managing servers.
- Service Level Agreement (SLA): A formal agreement between a service provider and a customer that outlines the quality and availability of the services to be provided and any penalties or compensation for service failures or outages. SLAs are commonly used in cloud computing and outsourcing agreements to ensure that customers receive the level of service they require.
- Session: A logical connection between two networked devices that enables communication between them.
- Session: A period of time during which a user interacts with a computer system, typically by logging in to a user account and accessing various services or applications. Sessions can be stateful, meaning that information about the user’s activity is maintained and stored, or stateless, where each request is treated as an isolated event.
- Shell: A command-line interface that allows users to interact with an operating system by typing commands and receiving output. Shells provide a powerful and flexible way to interact with a computer, and are commonly used in Unix-based operating systems such as Linux and macOS. Examples of shells include bash, zsh, and csh.
- Simple Mail Transfer Protocol (SMTP): A protocol for sending and receiving email messages over the Internet, used by most email systems. SMTP defines the format of email messages and the procedures for transmitting and receiving messages, and is typically used in conjunction with other protocols such as POP3 and IMAP.
- Single Sign-On (SSO): A mechanism that allows a user to log in once and gain access to multiple applications or services, without the need to log in again for each individual application. SSO is typically based on an authentication service, such as an LDAP directory, that centralizes the management of user identities and credentials.
- Socket: A programming interface that provides a endpoint for communication between two devices.
- Software as a Service (SaaS): A cloud computing service model in which the cloud provider provides a complete software application over the internet.
- Software Development Life Cycle (SDLC): The process of planning, designing, building, testing, and deploying software, from initial concept to final product. The SDLC typically includes stages such as requirements gathering, design, coding, testing, and maintenance, and can vary in methodology, such as Agile, Waterfall, or Spiral.
- Software Testing: The process of evaluating a software system or application to identify and resolve defects, and to ensure that the system meets the requirements and quality standards. Software testing can include various types of tests, such as unit tests, integration tests, system tests, and user acceptance tests, and is an important part of the software development life cycle.
- Software: Programs and other operating information used by a computer, typically installed on the computer’s hard drive or other storage device. Software includes both system software, such as the operating system, and application software, such as productivity tools, games, and multimedia applications.
- Source Code: The human-readable text that represents a computer program or application, written in a programming language such as Python, Java, or C++. Source code is the input to a compiler, interpreter, or assembler, which then converts the code into machine-readable instructions that a computer can execute.
- Spark: An open-source, distributed computing framework for large-scale data processing and analytics, designed for speed and ease of use. Spark uses in-memory processing to achieve fast performance and provides a variety of high-level APIs in Java, Scala, Python, and R, as well as support for SQL, machine learning, and graph processing.
- SQL (Structured Query Language): A programming language used to manage relational databases, including creating and altering tables, inserting, updating and deleting data, and retrieving data through queries. SQL is widely used for data analysis, business intelligence, and other applications that require accessing and manipulating large amounts of structured data.
- SSL (Secure Sockets Layer)/TLS (Transport Layer Security): A protocol for securing network communications, especially over the Internet, by encrypting data and establishing trust through the use of digital certificates. SSL/TLS is used to protect sensitive information such as passwords, credit card numbers, and other personal data, and is widely used for secure web browsing (https://), email (SMTPS, IMAPS), and other applications.
- SSL/TLS: Security protocols that encrypt data transmitted over the internet to protect it from eavesdropping and tampering.
- Station Point: A fixed point in surveying used as a reference for measurements.
- Stochastic Gradient Descent (SGD): A variant of gradient descent where a random batch of samples is used to estimate the gradient in each iteration, allowing for faster convergence.
- Storage Area Network (SAN): A high-speed network of storage devices, such as disk arrays and tape libraries, that provides block-level access to data for servers. SANs are designed to provide centralized storage management, improved performance and scalability, and enhanced data protection and disaster recovery capabilities.
- Supercomputer: A high-performance computer, typically consisting of tens of thousands of processors, used for demanding applications such as scientific simulations, weather forecasting, and cryptography. Supercomputers are among the fastest and most powerful computers in the world and are used to tackle problems that would be intractable on conventional computers.
- System administrator: A person responsible for the operation, maintenance, and security of a computer system, network, or data center, including hardware and software, security, backups, and performance. System administrators may be responsible for a single system or a large, complex network of systems, and may work in a variety of settings, such as corporations, universities, or government agencies.
- System software: Computer programs that provide the underlying control and management for a computer, including the operating system, device drivers, and utility programs. System software is typically pre-installed on a computer and provides essential services such as resource management, device management, and security.
- Tape library: A storage device that contains one or more tape drives and a large number of removable data tapes, used for backup, archiving, and disaster recovery. Tape libraries are designed for high-capacity, low-cost storage and provide automated tape management, including robotic tape handling and barcode-based tape labeling.
- TCP/IP: A set of communication protocols used to transmit data over the internet.
- TCP/IP (Transmission Control Protocol/Internet Protocol): A suite of communication protocols used to interconnect computer networks and provide the Internet. TCP/IP provides the fundamental rules for data transmission and routing across the Internet, including data segmentation, error detection and correction, and flow control.
- Template: A pre-designed document or file that can be used as a starting point for creating new documents or files with similar or repeated content. Templates can be used in a variety of contexts, such as word processing, spreadsheets, presentations, and web design, to save time and ensure consistency.
- Terminal Point: A point marking the end of a curve or a series of connected lines.
- Terminal: A computer program that provides a text-based interface to a computer system, allowing users to interact with the system and run programs. Terminals can be used locally, or over a network connection, and provide a simple and efficient way to access and control remote computers.
- Text editor: A computer program used to create, edit, and manipulate text files. Text editors are essential tools for software development, web design, and many other applications, and range from simple, lightweight editors with basic features, to complex, feature-rich editors with advanced capabilities such as syntax highlighting, code completion, and version control integration.
- Thompson Sampling: A probabilistic approach to the multi-armed bandit problem, where the agent selects actions based on a sample from the posterior distribution over expected rewards.
- Threat: A potential danger, vulnerability, or risk that could cause harm to a computer system, network, or data, such as a virus, malware, intrusion, or data theft. Threats can originate from a variety of sources, including attackers, insiders, and accidents, and can have serious consequences for the confidentiality, integrity, and availability of information.
- Tipping Point: A point at which a significant change or effect occurs.
- ToS (Terms of Service): A legal agreement between a user and an online service provider, specifying the terms and conditions under which the service can be used, such as restrictions, warranties, and liability. ToS agreements are typically presented to users when they sign up for an online service and must be accepted in order to use the service.
- Transfer Learning: A technique for adapting a pre-trained machine learning model to a new task by fine-tuning the weights on a smaller set of task-specific data.
- Transfer Learning: A technique for reusing a pre-trained neural network on a new task by fine-tuning the network on the new data, saving time and resources compared to training from scratch.
- Transfer Learning: A technique in deep learning where a pre-trained model is fine-tuned for a new task, leveraging the knowledge learned from the previous task.
- Transfer Learning: A technique in machine learning where a model pre-trained on one task is fine-tuned for another similar task, allowing for better performance and faster training times.
- Transformers: A type of neural network that uses self-attention mechanisms to process sequential data, and has been used for natural language processing and other sequential data tasks.
- Transistor: A tiny electronic device, consisting of a semiconductor material with at least three terminals, used to amplify or switch electronic signals. Transistors are the basic building blocks of modern electronics, including computers, and are used in a wide variety of applications, from digital logic circuits to amplifiers and power switches.
- Transmission Control Protocol (TCP): A communication protocol used to ensure reliable and ordered delivery of data over a network.
- Troubleshooting: The process of identifying and resolving problems or faults in a computer system, network, or application. Troubleshooting involves gathering information about the problem, identifying potential causes, and testing and implementing solutions to restore normal operation.
- Turning Point: A point in a curve or graph where the direction changes.
- UI (User Interface): The part of a computer program or system that is visible to the user and provides a way for the user to interact with the system. The UI typically includes elements such as windows, menus, buttons, and other graphical or text-based elements that allow the user to input commands and receive information from the system.
- Underfitting: A problem in machine learning where a model is too simple and fails to capture the complexity of the data.
- URL (Uniform Resource Locator): A string of text that provides the address or location of a specific resource on the World Wide Web, such as a webpage, image, or file. URLs typically start with “http://” or “https://” and include the domain name and path of the resource, as well as any parameters or query strings.
- USB (Universal Serial Bus): A standard for connecting peripherals, such as keyboards, mice, and external storage devices, to computers. USB provides a common, standardized interface that makes it easy to add and remove peripherals, and supports high-speed data transfer, power delivery, and configuration.
- User account: A specific set of privileges, settings, and personal information associated with a user who interacts with a computer system, network, or online service. User accounts typically include a username and password, as well as other information such as the user’s name, email address, and security settings.
- User Datagram Protocol (UDP): A connectionless communication protocol that transmits data without the guarantee of delivery or order.
- Valley Point: The lowest or minimum point of a curve or graph.
- Variational Autoencoder (VAE): A type of generative model where a probabilistic encoder and decoder are used to learn a compact representation of the data and generate new samples.
- Variational Autoencoders (VAEs): A type of generative model that uses a neural network to approximate the variational lower bound of the data likelihood, used for generative modeling and representation learning.
- Vertex: The point of intersection or junction of two or more lines or curves.
- Virtual machine (VM): An operating system or application environment that runs on a computer, but is isolated from the underlying physical hardware, and appears as a separate system to the user. Virtual machines are used for a variety of purposes, including software testing, development, and resource management, and can be managed and accessed remotely using virtualization software.
- Virtual memory: A type of computer memory that allows a computer to temporarily extend its physical memory by temporarily transferring data to disk storage. Virtual memory allows a computer to run larger applications or multiple applications simultaneously, even if the computer’s physical memory is full, and provides a way to manage memory resources dynamically.
- Virtual Private Network (VPN): A private network created over a public network, using encryption and secure protocols to protect data transmission.
- Virus: A type of malicious software (malware) that is designed to spread from computer to computer and cause harm to computer systems, networks, or data. Viruses can infect computer files and cause problems such as data loss, system crashes, and slow performance, and can be spread via email, instant messaging, or other means.
- VPN (Virtual Private Network): A type of secure network that uses public telecommunications infrastructure, such as the Internet, to provide remote users or offices with secure access to a private network or intranet. VPNs use encryption and other security mechanisms to protect data in transit, and provide a secure and cost-effective way to extend a private network to remote locations.
- VPN Tunnel: An encrypted connection between two devices or networks, used to securely transmit data over a public network.
- WAN: A Wide Area Network, a network that covers a large geographical area, typically connecting multiple LANs.
- Web browser: A software application used to access and view webpages on the World Wide Web. Web browsers provide a graphical interface for users to interact with webpages, and typically include features such as bookmarks, history, tabs, and support for extensions or plugins.
- Wi-Fi: A technology that uses radio waves to provide wireless high-speed Internet and network connections.
- Wi-Fi Direct: A technology that enables Wi-Fi-enabled devices to communicate directly with each other without the need for a wireless access point (AP) or router. Wi-Fi Direct allows devices to transfer files, media, and other data directly, without the need for a Wi-Fi network, and is used in applications such as peer-to-peer gaming, screen sharing, and file transfer.
- Wifi: A technology that provides wireless local area network (LAN) communications using radio waves, allowing computers, smartphones, and other devices to connect to the Internet or other networks without cables. Wifi provides a convenient and flexible way to connect devices, and is widely used in homes, businesses, and public spaces.
- Windows: An operating system developed by Microsoft that runs on personal computers, laptops, and other devices. Windows provides a graphical user interface, and supports a wide range of applications and hardware, including desktops, laptops, tablets, smartphones, and servers.
- Wireless Access Point (WAP): A hardware device that allows wireless devices to connect to a wired network using Wi-Fi.
- Word processing: The process of creating, editing, and formatting text-based documents using a computer program, such as Microsoft Word, Google Docs, or LibreOffice Writer. Word processing is a common task for many users, and provides a way to create professional-looking documents, such as letters, resumes, reports, and articles.
- WWW (World Wide Web): A global system of interconnected documents, images, and other resources, accessible through the Internet, that uses the Hypertext Transfer Protocol (HTTP) to transfer information. The World Wide Web was created in 1989 by Tim Berners-Lee, and has since become the primary means of accessing and sharing information on the Internet.
- XHTML (Extensible Hypertext Markup Language): A markup language used to create webpages, and an evolution of HTML (Hypertext Markup Language). XHTML is designed to be more flexible, extensible, and accessible than HTML, and provides a way to create webpages that comply with modern web standards.
- XML (Extensible Markup Language): A markup language used to describe and exchange structured data, such as metadata, between computer systems and applications. XML provides a way to describe data in a consistent, machine-readable format, and is widely used for data interchange, data storage, and data exchange on the Internet.
- Zigbee: A low-power, low-data-rate wireless communication protocol used for IoT devices and home automation systems.
- ZIP file: A compressed file format used to reduce the size of one or more files, making it easier to transfer or store them. ZIP files are widely used on the Internet to distribute software, music, images, and other types of files, and can be opened and extracted using a variety of software programs, such as WinZip, 7-Zip, or the built-in ZIP utility on most modern operating systems.
- Z-Wave: A wireless communication protocol used for IoT devices and home automation systems.