Learning rateIn machine learning and statistics, the learning rate is a tuning parameter in an optimization algorithm that determines the step size at each iteration while moving toward a minimum of a loss function. Since it influences to what extent newly acquired information overrides old information, it metaphorically represents the speed at which a machine learning model "learns". In the adaptive control literature, the learning rate is commonly referred to as gain. In setting a learning rate, there is a trade-off between the rate of convergence and overshooting.
Active learning (machine learning)Active learning is a special case of machine learning in which a learning algorithm can interactively query a user (or some other information source) to label new data points with the desired outputs. In statistics literature, it is sometimes also called optimal experimental design. The information source is also called teacher or oracle. There are situations in which unlabeled data is abundant but manual labeling is expensive. In such a scenario, learning algorithms can actively query the user/teacher for labels.
Client–server modelThe client–server model is a distributed application structure that partitions tasks or workloads between the providers of a resource or service, called servers, and service requesters, called clients. Often clients and servers communicate over a computer network on separate hardware, but both client and server may reside in the same system. A server host runs one or more server programs, which share their resources with clients. A client usually does not share any of its resources, but it requests content or service from a server.
Server (computing)In computing, a server is a piece of computer hardware or software (computer program) that provides functionality for other programs or devices, called "clients". This architecture is called the client–server model. Servers can provide various functionalities, often called "services", such as sharing data or resources among multiple clients or performing computations for a client. A single server can serve multiple clients, and a single client can use multiple servers.
Proxy serverIn computer networking, a proxy server is a server application that acts as an intermediary between a client requesting a resource and the server providing that resource. It improves privacy, security, and performance in the process. Instead of connecting directly to a server that can fulfill a request for a resource, such as a file or web page, the client directs the request to the proxy server, which evaluates the request and performs the required network transactions.
Web serverA web server is computer software and underlying hardware that accepts requests via HTTP (the network protocol created to distribute web content) or its secure variant HTTPS. A user agent, commonly a web browser or web crawler, initiates communication by making a request for a web page or other resource using HTTP, and the server responds with the content of that resource or an error message. A web server can also accept and store resources sent from the user agent if configured to do so.
Byzantine EmpireThe Byzantine Empire, also referred to as the Eastern Roman Empire, was the continuation of the Roman Empire primarily in its eastern provinces during Late Antiquity and the Middle Ages, when its capital city was Constantinople. It survived the fall of the Western Roman Empire in the 5th century AD and continued to exist until the fall of Constantinople to the Ottoman Empire in 1453. During most of its existence, the empire remained the most powerful economic, cultural, and military force in the Mediterranean world.
Byzantine architectureByzantine architecture is the architecture of the Byzantine Empire, or Eastern Roman Empire. The Byzantine era is usually dated from 330 AD, when Constantine the Great established a new Roman capital in Byzantium, which became Constantinople, until the fall of the Byzantine Empire in 1453. However, there was initially no hard line between the Byzantine and Roman Empires, and early Byzantine architecture is stylistically and structurally distinguishable from earlier Roman architecture.
Byzantine IconoclasmThe Byzantine Iconoclasm (Eikonomachía) were two periods in the history of the Byzantine Empire when the use of s or icons was opposed by religious and imperial authorities within the Ecumenical Patriarchate (at the time still comprising the Roman-Latin and the Eastern-Orthodox traditions) and the temporal imperial hierarchy. The First Iconoclasm, as it is sometimes called, occurred between about 726 and 787, while the Second Iconoclasm occurred between 814 and 842.
Byzantine armyThe Byzantine army was the primary military body of the Byzantine armed forces, serving alongside the Byzantine navy. A direct continuation of the Eastern Roman army, shaping and developing itself on the legacy of the late Hellenistic armies, it maintained a similar level of discipline, strategic prowess and organization. It was among the most effective armies of western Eurasia for much of the Middle Ages. Over time the cavalry arm became more prominent in the Byzantine army as the legion system disappeared in the early 7th century.
DataIn common usage and statistics, data (USˈdætə; UKˈdeɪtə) is a collection of discrete or continuous values that convey information, describing the quantity, quality, fact, statistics, other basic units of meaning, or simply sequences of symbols that may be further interpreted formally. A datum is an individual value in a collection of data. Data is usually organized into structures such as tables that provide additional context and meaning, and which may themselves be used as data in larger structures.
Artificial neural networkArtificial neural networks (ANNs, also shortened to neural networks (NNs) or neural nets) are a branch of machine learning models that are built using principles of neuronal organization discovered by connectionism in the biological neural networks constituting animal brains. An ANN is based on a collection of connected units or nodes called artificial neurons, which loosely model the neurons in a biological brain. Each connection, like the synapses in a biological brain, can transmit a signal to other neurons.
