Deep learningDeep learning is part of a broader family of machine learning methods, which is based on artificial neural networks with representation learning. The adjective "deep" in deep learning refers to the use of multiple layers in the network. Methods used can be either supervised, semi-supervised or unsupervised.
Network topologyNetwork topology is the arrangement of the elements (links, nodes, etc.) of a communication network. Network topology can be used to define or describe the arrangement of various types of telecommunication networks, including command and control radio networks, industrial fieldbusses and computer networks. Network topology is the topological structure of a network and may be depicted physically or logically. It is an application of graph theory wherein communicating devices are modeled as nodes and the connections between the devices are modeled as links or lines between the nodes.
UEFIUnified Extensible Firmware Interface (UEFI, 'uːᵻfaɪ or as acronym) is a specification that defines the architecture of the platform firmware used for booting and its interface for interaction with the operating system. Examples of firmware that implement the specification are AMI Aptio, Phoenix SecureCore, TianoCore EDK II, InsydeH2O. UEFI replaces the BIOS which was present in the boot ROM of all personal computers that are IBM PC compatible, although it can provide backwards compatibility with the BIOS using CSM booting.
Radical of an idealIn ring theory, a branch of mathematics, the radical of an ideal of a commutative ring is another ideal defined by the property that an element is in the radical if and only if some power of is in . Taking the radical of an ideal is called radicalization. A radical ideal (or semiprime ideal) is an ideal that is equal to its radical. The radical of a primary ideal is a prime ideal. This concept is generalized to non-commutative rings in the Semiprime ring article.
Empirical risk minimizationEmpirical risk minimization (ERM) is a principle in statistical learning theory which defines a family of learning algorithms and is used to give theoretical bounds on their performance. The core idea is that we cannot know exactly how well an algorithm will work in practice (the true "risk") because we don't know the true distribution of data that the algorithm will work on, but we can instead measure its performance on a known set of training data (the "empirical" risk).
Radial basis function kernelIn machine learning, the radial basis function kernel, or RBF kernel, is a popular kernel function used in various kernelized learning algorithms. In particular, it is commonly used in support vector machine classification. The RBF kernel on two samples and x', represented as feature vectors in some input space, is defined as may be recognized as the squared Euclidean distance between the two feature vectors. is a free parameter.
Boosting (machine learning)In machine learning, boosting is an ensemble meta-algorithm for primarily reducing bias, and also variance in supervised learning, and a family of machine learning algorithms that convert weak learners to strong ones. Boosting is based on the question posed by Kearns and Valiant (1988, 1989): "Can a set of weak learners create a single strong learner?" A weak learner is defined to be a classifier that is only slightly correlated with the true classification (it can label examples better than random guessing).
Falcon 9 first-stage landing testsThe Falcon 9 first-stage landing tests were a series of controlled-descent flight tests conducted by SpaceX between 2013 and 2016. Since 2017, the first stage of Falcon 9 missions has been routinely landed if the rocket performance allowed it, and if SpaceX chose to recover the stage. The program's objective was to reliably execute controlled re-entry, descent and landing (EDL) of the Falcon 9 first stage into Earth's atmosphere after the stage completes the boost phase of an orbital spaceflight.
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.
Architecture of Windows NTThe architecture of Windows NT, a line of operating systems produced and sold by Microsoft, is a layered design that consists of two main components, user mode and kernel mode. It is a preemptive, reentrant multitasking operating system, which has been designed to work with uniprocessor and symmetrical multiprocessor (SMP)-based computers. To process input/output (I/O) requests, it uses packet-driven I/O, which utilizes I/O request packets (IRPs) and asynchronous I/O.
