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Topic Review
Training, Validation, and Test Sets
In machine learning, a common task is the study and construction of algorithms that can learn from and make predictions on data. Such algorithms function by making data-driven predictions or decisions, through building a mathematical model from input data. These input data used to build the model are usually divided in multiple data sets. In particular, three data sets are commonly used in different stages of the creation of the model: training, validation and test sets. The model is initially fit on a training data set, which is a set of examples used to fit the parameters (e.g. weights of connections between neurons in artificial neural networks) of the model. The model (e.g. a naive Bayes classifier) is trained on the training data set using a supervised learning method, for example using optimization methods such as gradient descent or stochastic gradient descent. In practice, the training data set often consists of pairs of an input vector (or scalar) and the corresponding output vector (or scalar), where the answer key is commonly denoted as the target (or label). The current model is run with the training data set and produces a result, which is then compared with the target, for each input vector in the training data set. Based on the result of the comparison and the specific learning algorithm being used, the parameters of the model are adjusted. The model fitting can include both variable selection and parameter estimation. Successively, the fitted model is used to predict the responses for the observations in a second data set called the validation data set. The validation data set provides an unbiased evaluation of a model fit on the training data set while tuning the model's hyperparameters (e.g. the number of hidden units—layers and layer widths—in a neural network). Validation datasets can be used for regularization by early stopping (stopping training when the error on the validation data set increases, as this is a sign of over-fitting to the training data set). This simple procedure is complicated in practice by the fact that the validation dataset's error may fluctuate during training, producing multiple local minima. This complication has led to the creation of many ad-hoc rules for deciding when over-fitting has truly begun. Finally, the test data set is a data set used to provide an unbiased evaluation of a final model fit on the training data set. If the data in the test data set has never been used in training (for example in cross-validation), the test data set is also called a holdout data set. The term "validation set" is sometimes used instead of "test set" in some literature (e.g., if the original data set was partitioned into only two subsets, the test set might be referred to as the validation set). Deciding the sizes and strategies for data set division in training, test and validation sets is very dependent on the problem and data available.
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Topic Review
Uniform Distribution (Continuous)
In probability theory and statistics, the continuous uniform distribution or rectangular distribution is a family of symmetric probability distributions. The distribution describes an experiment where there is an arbitrary outcome that lies between certain bounds. The bounds are defined by the parameters, a and b, which are the minimum and maximum values. The interval can be either be closed (eg. [a, b]) or open (eg. (a, b)). Therefore, the distribution is often abbreviated U (a, b), where U stands for uniform distribution. The difference between the bounds defines the interval length; all intervals of the same length on the distribution's support are equally probable. It is the maximum entropy probability distribution for a random variable X under no constraint other than that it is contained in the distribution's support.
  • 2.4K
  • 17 Oct 2022
Topic Review
Google App Engine
Google App Engine (often referred to as GAE or simply App Engine) is a cloud computing platform as a service for developing and hosting web applications in Google-managed data centers. Applications are sandboxed and run across multiple servers. App Engine offers automatic scaling for web applications—as the number of requests increases for an application, App Engine automatically allocates more resources for the web application to handle the additional demand. Google App Engine primarily supports Go, PHP, Java, Python, Node.js, .NET, and Ruby applications, although it can also support other languages via "custom runtimes". The service is free up to a certain level of consumed resources and only in standard environment but not in flexible environment. Fees are charged for additional storage, bandwidth, or instance hours required by the application. It was first released as a preview version in April 2008 and came out of preview in September 2011.
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Topic Review
Lazarus
Lazarus is a free cross-platform visual integrated development environment (IDE) for rapid application development (RAD) using the Free Pascal compiler. Its goal is to provide an easy-to-use development environment for programmers developing with the Object Pascal language, which is as close as possible to Delphi. Software developers use Lazarus to create native-code console and graphical user interface (GUI) applications for the desktop, and also for mobile devices, web applications, web services, visual components and function libraries for a number of different platforms, including Mac, Linux and Windows. A project created by using Lazarus on one platform can be compiled on any other one which Free Pascal compiler supports. For desktop applications a single source can target Mac OS, Linux, and Windows, with little or no modification. An example is the Lazarus IDE itself, created from a single code base and available on all major platforms including the Raspberry Pi.
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Topic Review
Network Sovereignty
Network Sovereignty is the effort of a governing entity, such as a state, to create boundaries on a network and then exert a form of control, often in the form of law enforcement over such boundaries. Much like states invoke sole power over their physical territorial boundaries, state sovereignty, such governing bodies also invoke sole power within the network boundaries they set and claim network sovereignty. In the context of the Internet, the intention is to govern the web and control it within the borders of the state. Often, that is witnessed as states seeking to control all information flowing into and within their borders. The concept stems from questions of how states can maintain law over an entity such like the Internet, whose infrastructure exists in real space, but its entity itself exists in the intangible cyberspace. Some Internet Scholars, such as Joel R. Reidenberg, argue, "Networks have key attributes of sovereignty: participant/citizens via service provider membership agreements, 'constitutional' rights through contractual terms of service, and police powers through taxation (fees) and system operator sanctions." Indeed, many countries have pushed to ensure the protection of their citizens' privacy and of internal business longevity by data protection and information privacy legislation (see the EU's Data Protection Directive, the UK's Data Protection Act 1998). Network Sovereignty has implications for state security, Internet governance, and the users of the Internet's national and international networks.
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Topic Review
Functional Database Model
The functional database model is used to support analytics applications such as financial planning and performance management. The functional database model, or the functional model for short, is different from but complementary to the relational model. The functional model is also distinct from other similarly named concepts, including the DAPLEX functional database model and functional language databases. The functional model is part of the online analytical processing (OLAP) category since it comprises multidimensional hierarchical consolidation. But it goes beyond OLAP by requiring a spreadsheet-like cell orientation, where cells can be input or calculated as functions of other cells. Also as in spreadsheets, it supports interactive calculations where the values of all dependent cells are automatically up to date whenever the value of a cell is changed.
  • 326
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Topic Review
BASIC09
BASIC09 is a structured BASIC programming language dialect developed by Microware on behalf of Motorola for the then-new Motorola 6809 CPU and released in February 1980. It is primarily used with the OS-9 operating system, released in 1979. Microware also released a version for OS-9/68k on the 68000 as Microware BASIC. In contrast to typical BASICs of the era, BASIC09 includes a multi-pass compiler that produces compact bytecode known as I-code. I-code replaces a number of data structures found in other BASICs with direct pointers to code and values, speeding performance. Users can further compile code using the PACK command, at which point it can be called directly by OS-9 and operated as native code. In the case of PACKed code, a cut-down version of the BASIC09 runtime system is used, Runb, further improving memory footprint and load time. The language includes a number of structured programming additions, including local variables, the ability to ignore line numbers in favor of named routines, user-defined structures, and several distinct base data types including 16-bit and 8-bit (byte) integers, in addition to floating point and strings.
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Topic Review
List of Numerical Analysis Software
Listed here are end-user computer applications intended for use with numerical or data analysis:
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  • 17 Oct 2022
Topic Review
IEEE 802.11ax
IEEE 802.11ax, marketed as Wi-Fi 6 by Wi-Fi Alliance, is a draft Wi-Fi specification standard, and the proposed successor to Wi-Fi 5. The 802.11ax standard is expected to become an official IEEE specification in September 2020. It is designed to operate in licensed exempt bands between 1 and 6 GHz when they become available for 802.11 use. All Wi-Fi 6 devices work over the previously allocated 2.4 and 5 GHz bands. The Wi-Fi 6E designation is for products that also support the standard over 6 GHz. Devices presented at CES 2018 claimed a combined 11 Gbit/s of theoretical data rates. For dense deployments, throughput speeds are 4× higher than IEEE 802.11ac, even though the nominal data rate is just 37% faster at most. Latency is also down 75%. To improve spectrum efficient utilization, the new version introduces better power-control methods to avoid interference with neighboring networks, orthogonal frequency-division multiple access (OFDMA), higher order 1024-QAM, and up-link direction added with the down-link of MIMO and MU-MIMO to further increase throughput, as well as dependability improvements of power consumption and security protocols such as Target Wake Time and WPA3.
  • 551
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Topic Review
Bumble (App)
Bumble is a location-based social application that facilitates communication between interested users. In heterosexual matches, only female users can make the first contact with matched male users, while in same-sex matches either person can send a message first. Users can sign up using their phone number or Facebook profile, and have options of searching for romantic matches or, in "BFF mode", friends. Bumble Bizz facilitates business communications. Bumble was founded by Whitney Wolfe Herd shortly after she left Tinder, a dating app she co-founded, due to growing tensions with other company executives. Wolfe Herd has described Bumble as a "feminist dating app". As of September 2019, with a monthly user base of 5 million, Bumble is the second-most popular dating app in the U.S. after Tinder. According to a June 2016 survey, 46.2% of its users are female. According to Forbes , the company is valued at more than $1 billion, and has over 55 million users.
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