In machine learning (ML), a learning curve (or training curve) is a graphical representation that shows how a model's performance on a training set (and usually a validation set) changes with the number of training iterations (epochs) or the amount of training data. Typically, the number of training epochs or training set size is plotted on the x-axis, and the value of the loss function (and possibly some other metric such as the cross-validation score) on the y-axis. Synonyms include error curve, experience curve, improvement curve and generalization curve. More abstractly, learning curves plot the difference between learning effort and predictive performance, where "learning effort" usually means the number of training samples, and "predictive performance" means accuracy on testing samples. Learning curves have many useful purposes in ML, including: choosing model parameters during design, adjusting optimization to improve convergence, and diagnosing problems such as overfitting (or underfitting). Learning curves can also be tools for determining how much a model benefits from adding more training data, and whether the model suffers more from a variance error or a bias error. If both the validation score and the training score converge to a certain value, then the model will no longer significantly benefit from more training data. == Formal definition == When creating a function to approximate the distribution of some data, it is necessary to define a loss function L ( f θ ( X ) , Y ) {\displaystyle L(f_{\theta }(X),Y)} to measure how good the model output is (e.g., accuracy for classification tasks or mean squared error for regression). We then define an optimization process which finds model parameters θ {\displaystyle \theta } such that L ( f θ ( X ) , Y ) {\displaystyle L(f_{\theta }(X),Y)} is minimized, referred to as θ ∗ {\displaystyle \theta ^{}} . === Training curve for amount of data === If the training data is { x 1 , x 2 , … , x n } , { y 1 , y 2 , … y n } {\displaystyle \{x_{1},x_{2},\dots ,x_{n}\},\{y_{1},y_{2},\dots y_{n}\}} and the validation data is { x 1 ′ , x 2 ′ , … x m ′ } , { y 1 ′ , y 2 ′ , … y m ′ } {\displaystyle \{x_{1}',x_{2}',\dots x_{m}'\},\{y_{1}',y_{2}',\dots y_{m}'\}} , a learning curve is the plot of the two curves i ↦ L ( f θ ∗ ( X i , Y i ) ( X i ) , Y i ) {\displaystyle i\mapsto L(f_{\theta ^{}(X_{i},Y_{i})}(X_{i}),Y_{i})} i ↦ L ( f θ ∗ ( X i , Y i ) ( X i ′ ) , Y i ′ ) {\displaystyle i\mapsto L(f_{\theta ^{}(X_{i},Y_{i})}(X_{i}'),Y_{i}')} where X i = { x 1 , x 2 , … x i } {\displaystyle X_{i}=\{x_{1},x_{2},\dots x_{i}\}} === Training curve for number of iterations === Many optimization algorithms are iterative, repeating the same step (such as backpropagation) until the process converges to an optimal value. Gradient descent is one such algorithm. If θ i ∗ {\displaystyle \theta _{i}^{}} is the approximation of the optimal θ {\displaystyle \theta } after i {\displaystyle i} steps, a learning curve is the plot of i ↦ L ( f θ i ∗ ( X , Y ) ( X ) , Y ) {\displaystyle i\mapsto L(f_{\theta _{i}^{}(X,Y)}(X),Y)} i ↦ L ( f θ i ∗ ( X , Y ) ( X ′ ) , Y ′ ) {\displaystyle i\mapsto L(f_{\theta _{i}^{}(X,Y)}(X'),Y')}
Managed private cloud
Managed private cloud (also known as "hosted private cloud" or "single-tenant SaaS") refers to a principle in software architecture where a single instance of the software runs on a server, serves a single client organization (tenant), and is managed by a third party. The third-party provider is responsible for providing the hardware for the server and also for preliminary maintenance. This is in contrast to multitenancy, where multiple client organizations share a single server, or an on-premises deployment, where the client organization hosts its software instance. Managed private clouds also fall under the larger umbrella of cloud computing. == Adoption == The need for private clouds arose due to enterprises requiring a dedicated service and infrastructure for their cloud computing needs, such as for business-critical operations, improved security, and better control over their resources. Managed private cloud adoption is a popular choice among organizations. It has been on the rise due to enterprises requiring a dedicated cloud environment and preferring to avoid having to deal with management, maintenance, or future upgrade costs for the associated infrastructure and services. Such operational costs are unavoidable in on-premises private cloud data centers. == Advantages and challenges of managed private cloud == A managed private cloud cuts down on upkeep costs by outsourcing infrastructure management and maintenance to the managed cloud provider. It is easier to integrate an organization's existing software, services, and applications into a dedicated cloud hosting infrastructure which can be customized to the client's needs instead of a public cloud platform, whose hardware or infrastructure/software platform cannot be individualized to each client. Customers who choose a managed private cloud deployment usually choose them because of their desire for efficient cloud deployment, but also have the need for service customization or integration only available in a single-tenant environment. This chart shows the key benefits of the different types of deployments, and shows the overlap between these cloud solutions. This chart shows key drawbacks. Since deployments are done in a single-tenant environment, it is usually cost-prohibitive for small and medium-sized businesses. While server upkeep and maintenance are handled by the service provider, including network management and security, the client is charged for all such services. It is up to the potential client to determine if a managed private cloud solution aligns with their business objectives and budget. While the service provider maintains the upkeep of servers, network, and platform infrastructure, sensitive data is typically not stored on managed private clouds as it may leave business-critical information prone to breaches via third-party attacks on the cloud service provider. Common customizations and integrations include: Active Directory Single Sign-on Learning Management Systems Video Teleconferencing == Deployment strategies and service providers == Software companies have taken a variety of strategies in the Managed Private Cloud realm. Some software organizations have provided managed private cloud options internally, such as Microsoft. Companies that offer an on-premises deployment option, by definition, enable third-party companies to market Managed Private Cloud solutions. A few managed private cloud service providers are: Adobe Connect: Adobe Connect may be purchased for on-premises deployment, multi-tenant hosted deployment, managed private cloud as ACMS, or managed by third-party managed private cloud provider ConnectSolutions. Rackspace CenturyLink Microsoft licenses for Lync, SharePoint and Exchange may be purchased for on-premises deployment, a multi-tenant hosted deployment via Office 365, or managed by third-party cloud hosting from Azaleos, ConnectSolutions and others.
Data hub
A data hub is a center of data exchange that is supported by data science, data engineering, and data warehouse technologies to interact with endpoints such as applications and algorithms. == Features == A data hub differs from a data warehouse in that it is generally unintegrated and often at different grains. It differs from an operational data store because a data hub does not need to be limited to operational data. A data hub differs from a data lake by homogenizing data and possibly serving data in multiple desired formats, rather than simply storing it in one place, and by adding other value to the data such as de-duplication, quality, security, and a standardized set of query services. A data lake tends to store data in one place for availability, and allow/require the consumer to process or add value to the data. Data hubs are ideally the "go-to" place for data within an enterprise, so that many point-to-point connections between callers and data suppliers do not need to be made, and so that the data hub organization can negotiate deliverables and schedules with various data enclave teams, rather than being an organizational free-for-all as different teams try to get new services and features from many other teams.
Talim (textiles)
Talim (Kashmiri: تعليم, Kashmiri pronunciation: [t̪əːliːm], Urdu: تَعْلِیم, Arabic: تعليم, pronounced [taʕ.liːm] ) in textiles is a symbolic code and system of notation that facilitates the creation of intricate patterns in fabrics, such as shawls and carpets, and the written coded plans that include colour schemes and weaving instructions. The term is used in traditional hand-weaving in the Indian subcontinent. Talim was initially used to create certain types of patterns in Kashmiri shawls, and later came to be applied in the production of carpets. == Etymology and origin == The term talim, which refers to a symbolic code and system of notation used by shawl and carpet artisans in their weaving processes, came to the Urdu language from the Arabic noun taʻlim (تعليم), which means "authoritative instruction", "teaching", or "edification". It means the same in Urdu and Kashmiri. The Arabic noun originated from the second form of the Arabic root verb ʻalima (علم), which means "to know". According to a local belief in Kashmir, talim was introduced to them by Persian scholar and Sufi Muslim saint Mir Sayyid Ali Hamadani. The belief notwithstanding, talim might have originated from Kashmir; Amritsar was the only place outside of Srinagar where talim was used, by migrated Kashmiri artisans. == Technique == Whereas carpets are generally woven horizontally, providing weavers with a clear view of the progress they are making in creating designs, in Kashmir, carpets are woven vertically, so the weaver is reliant on the talim. The talim technique forms fabrics by passing the weft thread as per a given script that has design codes. Weavers use talim to weave the desired pattern with planned colours. Talim involves teamwork when applying the technique, as the process of creating intricate fabric designs in weaving begins with the Naqash (designer, who designs using pencils on graphs) meticulously crafting the design on a blank sheet of paper called a naska, and the master, Talim guru, making the colour codes and symbols for weft yarns that would interlace the warp to construct the desired design. He writes on a long strip of paper, in specific symbols, the colour codes, and the number of knots to be woven with each colour. Taraha guru collaborates with talim guru and is known as the artisan responsible for determining the colours. Talim uthana is a process or the act of "picking the codes" from the graph. A clerk called the Talim Navis would record the step-by-step instructions for these numbers and colours, and thousands of low-paid and interchangeable weavers would read or recite the record to carry out the design. Afterward, a talim copyist makes copies, which are needed when multiple looms weave the same product. The script, which has been encoded, is deciphered and translated according to the specific guidelines of weavers in order to incorporate the design that is included within it. Talim has been compared to "hieroglyphics" or as a "notational-cum-cryptographic system", as it is challenging to decipher and is unique to the shawls of Kashmir, which requires expertise to comprehend. According to researcher Gagan Deep Kaur, "The talim is widely held to be a trade secret of the community and has always been fiercely guarded by the owners." Those who use talim for shawl-making do not assign important tasks to women, because of the fear that the technique and knowledge may be divulged to other communities when the women are sent there to be married. The coded cards known as talim in the Kashmiri language were used for creating certain types of patterns in shawl weaving. The talim technique is employed in the creation of kani shawls, which originated from the Kanihama region of the Kashmir valley. Carpet weaving adapted the technique from shawl making. When Kashmiri artisans started to create carpets, they chose to continue using the talim rather than switching to a different method. The resurgence of the carpet industry in Amritsar during the last century resulted in the prevalent use of the talim technique among the local weavers, a majority of whom hailed from the region of Kashmir. === Recitation of codes === Talim was also communicated through recitation accompanied by a melodic chant or song. In traditional weaving practices, the use of chanting was common. The movement of the shuttles was synchronised with the song of the weaver, adding a musical rhythm to the instructions represented through hieroglyphics. The weaver's chant, "Two blue, one red, three yellow, two blue," served as a guide as they wove and replicated the designated pattern. == Usage == The first factories established in Amritsar around 1860 utilised Bokhara designs. However, Kashmiri weavers maintained their traditional techniques and employed the talim, instead of a cartoon, for tying knots. As a result, Amritsar became the second location in the Indian subcontinent to use the talim. The traditional weaving practices are still carried out in some parts of the Indian subcontinent. The exact date when talim was last used in the subcontinent varies depending on the region and the specific weaving community. Indian textile historian Jasleen Dhamija wrote in her 1989 book Handwoven Fabrics of India that there were still some weavers in the Kashmiri village of Kanihama who applied talim in weaving shawls. As of 2022, the carpet weavers in Kashmir were the only remaining users of talim in carpets, according to Zubair Ahmed, director of the Indian Institute of Carpet Technology. The institute aims to preserve traditional Kashmiri carpet designs by digitising talim and training weavers in the technique. == Gallery ==
Campus network
A campus network, campus area network, corporate area network or CAN is a computer network made up of an interconnection of local area networks (LANs) within a limited geographical area. The networking equipments (switches, routers) and transmission media (optical fiber, copper plant, Cat5 cabling etc.) are almost entirely owned by the campus tenant / owner: an enterprise, university, government etc. A campus area network is larger than a local area network but smaller than a metropolitan area network (MAN) or wide area network (WAN). == University campuses == College or university campus area networks often interconnect a variety of buildings, including administrative buildings, academic buildings, laboratories, university libraries, or student centers, residence halls, gymnasiums, and other outlying structures, like conference centers, technology centers, and training institutes. Early examples include the Stanford University Network at Stanford University, Project Athena at MIT, and the Andrew Project at Carnegie Mellon University. == Corporate campuses == Much like a university campus network, a corporate campus network serves to connect buildings. Examples of such are the networks at Googleplex and Microsoft's campus. Campus networks are normally interconnected with high speed Ethernet links operating over optical fiber such as gigabit Ethernet and 10 Gigabit Ethernet. == Area range == The range of CAN is 1 to 5 km (1 to 3 mi). If two buildings have the same domain and they are connected with a network, then it will be considered as CAN only. Though the CAN is mainly used for corporate campuses so the link will be high speed.
AstroPay
AstroPay is a global digital wallet that provides users with a way to pay, send, and receive money. The app provides online payments, virtual and physical debit cards, peer-to-peer money transfers, and more. == History == AstroPay was founded in Uruguay in 2009 as a payment processing company. Over time, it expanded its services across Latin America, EMEA, and APAC. A significant milestone occurred in 2016, when AstroPay spun off dLocal, focusing on cross-border payments for emerging markets. dLocal became Uruguay's first unicorn and eventually went public through a successful IPO. In 2020, AstroPay spun off its payment processing services into a new entity, D24, to focus on mobile wallet for cross border. Between 2023 and 2024 the Company brought new leadership to guide its transition towards becoming a fully focused global digital multicurrency wallet where users save, send, and spend globally. This shift introduced enhanced features, including loyalty prepaid cards and multicurrency accounts. == Services == AstroPay offers three main products: AstroPay Wallet, AstroPay check-out, and AstroPay Platform. AstroPay Wallet is a digital wallet for consumers, where they have multicurrency accounts, prepaid card and marketplace. With AstroPay check-out, businesses can tap into AstroPay's wallet user base by accepting AstroPay as a payment method in their check-out options. Lastly, AstroPay Platform enables other businesses to use the AstroPay network to launch their own global wallet. == Brand endorsements, partnerships == AstroPay's marketing strategy has included the development of co-branded products with sports teams and other brand. The company sponsored Burnley Football Club during the 2018–19 Premier League season, renewing the partnership for the 2021–22 Premier League season when it became the club's official payment service partner. In August 2021, AstroPay entered into a partnership with the Wolverhampton Wanderers for the 2021-22 Premier League season, and the following year, became the team's shirt sponsor. Later, in September 2021, AstroPay expanded its partnership with Wolverhampton Wanderers, which included becoming the team's official payment partner and later, in 2023, co-launching a co-branded card. Other partnerships include Newcastle United in 2021 in the English Premier League. AstroPay made arrangements to ensure that branding and logo would be visible on the pitch-side LED advertising during Premier League matches. Furthermore, in June 2022, the company renewed it's partnership with Wolverhampton Wanderers for the 2022-23 Premier League season and launched its Wolves debit card in February 2023. Some other notable partnerships include: Universidad de Chile in 2024, Tottenham Hotspurs in 2023-25, and even a collaboration with Lionel Messi across all of Latin America. == Recent developments == AstroPay has refocused its strategy since 2023, pivoting from payment processing to concentrate on its global digital wallet. This move reflects a broader effort to redefine the company's market positioning by emphasizing global user-friendly financial services, while separating its identity from previous operations managed by dLocal and D24.
Data proliferation
Data proliferation refers to the prodigious amount of data, structured and unstructured, that businesses and governments continue to generate at an unprecedented rate and the usability problems that result from attempting to store and manage that data. While originally pertaining to problems associated with paper documentation, data proliferation has become a major problem in primary and secondary data storage on computers. While digital storage has become cheaper, the associated costs, from raw power to maintenance and from metadata to search engines, have not kept up with the proliferation of data. Although the power required to maintain a unit of data has fallen, the cost of facilities which house the digital storage has tended to rise. Data proliferation has been documented as a problem for the U.S. military since August 1971, in particular regarding the excessive documentation submitted during the acquisition of major weapon systems. Efforts to mitigate data proliferation and the problems associated with it are ongoing. == Problems caused == The problem of data proliferation is affecting all areas of commerce as a result of the availability of relatively inexpensive data storage devices. This has made it very easy to dump data into secondary storage immediately after its window of usability has passed. This masks problem that could gravely affect the profitability of businesses and the efficient functioning of health services, police and security forces, local and national governments, and many other types of organizations. Data proliferation is problematic for several reasons: Difficulty when trying to find and retrieve information. At Xerox, on average it takes employees more than one hour per week to find hard-copy documents, costing $2,152 a year to manage and store them. For businesses with more than 10 employees, this increases to almost two hours per week at $5,760 per year. In large networks of primary and secondary data storage, problems finding electronic data are analogous to problems finding hard copy data. Data loss and legal liability when data is disorganized, not properly replicated, or cannot be found promptly. In April 2005, the Ameritrade Holding Corporation told 200,000 current and past customers that a tape containing confidential information had been lost or destroyed in transit. In May of the same year, Time Warner Incorporated reported that 40 tapes containing personal data on 600,000 current and former employees had been lost en route to a storage facility. In March 2005, a Florida judge hearing a $2.7 billion lawsuit against Morgan Stanley issued an "adverse inference order" against the company for "willful and gross abuse of its discovery obligations." The judge cited Morgan Stanley for repeatedly finding misplaced tapes of e-mail messages long after the company had claimed that it had turned over all such tapes to the court. Increased manpower requirements to manage increasingly chaotic data storage resources. Slower networks and application performance due to excess traffic as users search and search again for the material they need. High cost in terms of the energy resources required to operate storage hardware. A 100 terabyte system will cost up to $35,040 a year to run—not counting cooling costs. == Proposed solutions == Applications that better utilize modern technology Reductions in duplicate data (especially as caused by data movement) Improvement of metadata structures Improvement of file and storage transfer structures User education and discipline The implementation of Information Lifecycle Management solutions to eliminate low-value information as early as possible before putting the rest into actively managed long-term storage in which it can be quickly and cheaply accessed.