Machine learningMachine learning (ML) is an umbrella term for solving problems for which development of algorithms by human programmers would be cost-prohibitive, and instead the problems are solved by helping machines 'discover' their 'own' algorithms, without needing to be explicitly told what to do by any human-developed algorithms. Recently, generative artificial neural networks have been able to surpass results of many previous approaches.
Residual neural networkA Residual Neural Network (a.k.a. Residual Network, ResNet) is a deep learning model in which the weight layers learn residual functions with reference to the layer inputs. A Residual Network is a network with skip connections that perform identity mappings, merged with the layer outputs by addition. It behaves like a Highway Network whose gates are opened through strongly positive bias weights. This enables deep learning models with tens or hundreds of layers to train easily and approach better accuracy when going deeper.
BackpropagationAs a machine-learning algorithm, backpropagation performs a backward pass to adjust the model's parameters, aiming to minimize the mean squared error (MSE). In a single-layered network, backpropagation uses the following steps: Traverse through the network from the input to the output by computing the hidden layers' output and the output layer. (the feedforward step) In the output layer, calculate the derivative of the cost function with respect to the input and the hidden layers.
MediaWikiMediaWiki is free and open-source wiki software originally developed by Magnus Manske for use on Wikipedia on January 25, 2002 and further improved by Lee Daniel Crocker, after which it has since been coordinated by the Wikimedia Foundation. It powers most websites hosted by the Foundation including Wikipedia, Wiktionary, Wikimedia Commons, Wikiquote, Meta-Wiki and Wikidata, which define a large part of the set requirements for the software. MediaWiki is written in the PHP programming language and stores all text content into a database.
Students' unionA students' union, also known by many other names, is a student organization present in many colleges, universities, and high schools. In higher education, the students' union is often accorded its own building on the campus, dedicated to social, organizational activities, representation, and academic support of the membership. In the United States, student union often only refers to a physical building owned by the university with the purpose of providing services for students without a governing body.
Universal approximation theoremIn the mathematical theory of artificial neural networks, universal approximation theorems are results that put limits on what neural networks can theoretically learn, i.e. that establish the density of an algorithmically generated class of functions within a given function space of interest. Typically, these results concern the approximation capabilities of the feedforward architecture on the space of continuous functions between two Euclidean spaces, and the approximation is with respect to the compact convergence topology.
Wiki softwareWiki software (also known as a wiki engine or a wiki application), is collaborative software that runs a wiki, which allows the users to create and collaboratively edit pages or entries via a web browser. A wiki system is usually a web application that runs on one or more web servers. The content, including previous revisions, is usually stored in either a or a database. Wikis are a type of web content management system, and the most commonly supported off-the-shelf software that web hosting facilities offer.
Wikimedia FoundationThe Wikimedia Foundation, Inc. (WMF) is an American 501(c)(3) nonprofit organization headquartered in San Francisco, California, and registered as a charitable foundation under local laws. Best known as the hosting platform for Wikipedia, a crowdsourced online encyclopedia, it also hosts other related projects and MediaWiki, a wiki software. The Wikimedia Foundation was established in 2003 in St. Petersburg, Florida, by Jimmy Wales as a nonprofit way to fund Wikipedia, Wiktionary, and other crowdsourced wiki projects that had until then been hosted by Bomis, Wales's for-profit company.
Deep belief networkIn machine learning, a deep belief network (DBN) is a generative graphical model, or alternatively a class of deep neural network, composed of multiple layers of latent variables ("hidden units"), with connections between the layers but not between units within each layer. When trained on a set of examples without supervision, a DBN can learn to probabilistically reconstruct its inputs. The layers then act as feature detectors. After this learning step, a DBN can be further trained with supervision to perform classification.
Procedural knowledgeProcedural knowledge (also known as knowing-how, and sometimes referred to as practical knowledge, imperative knowledge, or performative knowledge) is the knowledge exercised in the performance of some task. Unlike descriptive knowledge (also known as declarative knowledge, propositional knowledge or "knowing-that"), which involves knowledge of specific facts or propositions (e.g. "I know that snow is white"), procedural knowledge involves one's ability to do something (e.g. "I know how to change a flat tire").
Data processingData processing is the collection and manipulation of digital data to produce meaningful information. Data processing is a form of information processing, which is the modification (processing) of information in any manner detectable by an observer. The term "Data Processing", or "DP" has also been used to refer to a department within an organization responsible for the operation of data processing programs. Data processing may involve various processes, including: Validation – Ensuring that supplied data is correct and relevant.
AutoencoderAn autoencoder is a type of artificial neural network used to learn efficient codings of unlabeled data (unsupervised learning). An autoencoder learns two functions: an encoding function that transforms the input data, and a decoding function that recreates the input data from the encoded representation. The autoencoder learns an efficient representation (encoding) for a set of data, typically for dimensionality reduction. Variants exist, aiming to force the learned representations to assume useful properties.
