Podcast #128: We chat with Kent C Dodds about why he loves React and discuss what life was like in the dark days before Git. Listen now.

Tags

A tag is a keyword or label that categorizes your question with other, similar questions. Using the right tags makes it easier for others to find and answer your question.

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the process of using domain knowledge of the data to create features that improve machine learning algorithms
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Refers to general procedures that attempt to determine the generalizability of a statistical result. Cross-validation arises frequently in the context of assessing how a particular model fit predicts …
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a set of one or more computations that will produce a calculated result. All statistics methods are algorithms. Algorithms can be simple, such as calculating a percentage, or can be ve…
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Everything related to recommender systems
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a subfield of computer science which deals with analyzing and understanding images. This includes detection of objects like faces in images or segmenting images.
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In statistics this refers to selecting an estimator of a parameter by maximizing or minimizing some function of the data. One very common example is choosing an estimator which maximizes the joint den…
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Variables (used for prediction or explication) used in regression or regression-like models (like clustering, discrimination). Use this tag for questions about constructing such variables or selecting…
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a family of cluster analysis methods in which you specify the number of clusters you expect. This is as opposed to hierarchical cluster analysis methods.
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Finding hidden (statistical) structure in unlabelled data, including clustering and feature extraction for dimensionality reduction.
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quantify the difference between observed data and predicted values according to a model. Minimization of loss functions is a way to estimate the parameters of the model.
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an algorithm for finding the minimum of a function. It iteratively calculates partial derivatives (gradients) of the function and descends in steps proportional to those partial d…
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a two layer neural network to process text. It takes words as an input and outputs a vector correspondingly. It uses a combination of Continuous Bag of Word and skipgram model implementati…
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the collective name for a set of language modeling and feature learning techniques in NLP where words are mapped to vectors of real numbers in a low dimensional space, relative to th…
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a type of machine learning algorithm that learns a mapping function y = f(x) between input variables (x) and output variables (y). The two most common supervised learning tasks …
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Categorical data can take on a limited (usually fixed) number of possible values called categories. Categorical values "label", they do not "measure". Nominal and dichotomous/binary scale types are ca…
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an open source library for Tensors and Dynamic neural networks in Python with strong GPU acceleration. For details, see https://pytorch.org.
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an open source cluster computing system that aims to make data analytics fast — both fast to run and fast to write, originally developed in the AMPLab at UC Berkeley.
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Anomaly detection refers to the problem of finding patterns in data that do not conform to expected behaviour. This is also known as outlier detection.
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For use when discussing the commutative and linear, but not associative operator interpreted on functions and distributions.
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A measure of the degree of linear association among a pair of variables.
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Use for questions about Backpropagation, which is commonly used in training Neural Networks in conjunction with an optimization method such as gradient descent.
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Use for questions about Orange, the free, open-source, component-based, data mining and machine learning software suite.
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Principal component analysis, a technique for dimensionality reduction.
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a type of neural network that learns a useful encoding for data.
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A form of signal processing where the input is an image. Usually treating the digital image as a two-dimensional signal (or multidimensional). This processing may include image restoration and enhance…
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Multilabel classification assigns to each sample a set of target labels. This can be thought as predicting properties of a data-point that are not mutually exclusive, such as topics that are relevant …
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a scientific and numerical computing extension to the Python programming language.
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Dimensionality reduction refers to techniques for reducing many variables into a smaller number while keeping as much information as possible. One prominent method is [tag pca]