SVM stands for Support Vector Machine. SVM is a well-known family of supervised learning non-parametric algorithms which are used in regression and classification machine learning problems, by separating data values using a hyperplane. SVM algorithms are ideal when there is presence of outliers in the model training data.
The tanh function, also known as the hyperbolic tangent function, is an activation function in artificial neural networks whose output values are constrained between the values of −1 and 1. The following screenshot provides a graph of function f(x)=tanh(x), as output from the Geogebra free online calculator.
In machine learning, the target function is a mathematical representation of the relationship between an ML model's input variables and output variables, which best approximates some desired outcome from a machine learning model.
Tensor flow TensorFlow is an open-source machine learning (ML) software library. Tensorflow was originally created by the Google Brain Team in 2015. The main purpose of Tensorflow is building and training neural networks, by using a variety of supported languages, the primary one being Python. To discover the Tensorflow customizable pre-built models, visit its official ... Read more
The threshold is a value which a classification model uses, in order to classify anything higher than the threshold as positive, and anything lower than the threshold as negative.
In machine learning, time series is a series of data, where values of certain features are presented in a sequence of time. There are univariate and multivariate time series in forecasting problems in machine learning. Univariate forecasting models make use of algorithms such as ARIMA and multivariate forecasting models make use of algorithms such as ... Read more
TLU stands for threshold logic unit. TLU is an output neuron which calculates the weighted sum of input neurons and then implements a step function. This is used in the perceptron artificial neural network model.
Tokenization in Natural Language Processing (NLP) is the process of partitioning natural language text into smaller units, which are then manipulated by RNN or other artificial neural network models.
Tensor Processing Units (TPUs) are electronic circuits which are optimized for AI operations and ML tasks. TPUs utilize the TensorFlow protocol logic to align with AI/ML task performance requirements.
Transformer machine learning model A transformer is a deep learning model. Transformer models are mainly used for natural language processing (NLP) and computer vision (CV). Transformers are the evolution of RNN models. A recent example of tranformer-type models in artificial intelligence (AI) are the dp-tranformer models developed by Microsoft research. More details can be found ... Read more
Underfitting is a problem in machine learning in which a model cannot make effective target feature value estimations due to an inability to identify the underlying patterns in the data. An underfitting ML model has low variance and high bias. Underfitting is one pole away from a good fit, the other pole being overfitting.
Unified communications Unified communications (UC) is a term used to describe the convergence of various communication services such as instant messaging (chat), presence, PSTN and VoIP voice calls as well as audio and video conferencing. Modern UC systems include Microsoft Teams, Cisco Webex, Zoom and BigBlueButton. The WebRTC protocol is increasingly being used inside the ... Read more
In machine learning, univariate generally means that a property of a dataset has only a single variable which is being studied. Univariate is used in forecasting models, where only a single variable is being considered in a single time series. A common univariate ML algorithm is ARIMA.
VAR stands for vector autoregression. It is a regression algorithm commonly used in multivariate time series forecasting problems. See the ARIMA algorithm for univariate time series forecasting.
In machine learning (ML), variance is a concept which is related to errors in the model's predictions, as a results of over-sensitivity and high correlation of the machine learning algorithm to the training data. Due to this over-sensitivity, the ML model becomes complex to explain (explainability) and it captures the complexity inside the training data ... Read more
A virtual machine is a cloud-based or on-premises based virtual computer which runs under the control of a hypervisor host cluster. A hypervisor is computer software, firmware or hardware which is able to create and run virtual machines. There are two basic types of server virtualization, i.e. type 1 and type 2. Type 1 hypervisors ... Read more
WCF Windows Communication Foundation (WCF) is a framework for building service-oriented applications by utilizing asynchronous messages and service endpoints. Citrix Virtual Apps and Desktops is a great example of a VDI platform which is developed under Microsoft .NET and the WCF framework. For more details about WCF, refer to the official page at https://docs.microsoft.com/en-us/dotnet/framework/wcf/whats-wcf.
XaaS Anything As A Service describes any cloud computing service which can be offered as a managed service.
Yaml YAML is a human-readable data-serialization language, primarily used for configuration files and in applications where data is being stored or transmitted. JSON is extensively used in public cloud computing Infrastructure As Code (IaC) templates. Details about the YAML specification can be found at https://yaml.org/.
Zero day exploit A zero day exploit (also called a zero-day threat) is an attack that takes advantage of a security vulnerability that does not have a fix in place. It is referred to as a "zero-day" threat because once the flaw is eventually discovered, the developer or organization has "zero days" to then come ... Read more
