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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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Methods and principles of building "computer systems that automatically improve with experience."
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a general-purpose, dynamic, strongly typed language with many 3rd-party libraries for data science applications.
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composed of 'neurons' - programming constructs that mimic the properties of biological neurons. A set of weighted connections between the neurons allows informati…
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a new area of Machine Learning research concerned with the technologies used for learning hierarchical representations of data, mainly done with deep neural networks (i.e. networks with two or more hi…
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An instance of supervised learning that identifies the category or categories which a new instance of dataset belongs.
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a free, open-source programming language and software environment for statistical computing, bioinformatics, and graphics.
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a minimalist, highly modular neural network library written in Python.
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a Python module comprising of simple and efficient tool for machine learning, data mining and data analysis. It is built on NumPy, SciPy, and matplotlib. It is distributed under the 3-…
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An activity that seeks patterns in large, complex data sets. It usually emphasizes algorithmic techniques, but may also involve any set of related skills, applications, or methodologies with that goal…
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a field of computer science, artificial intelligence, and linguistics concerned with the interactions between computers and human (natural) languages. As such, NLP…
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an open source library for machine learning and machine intelligence. TensorFlow uses data flow graphs with tensors flowing along edges. For details, see https://www.tensorflow.org. Te…
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the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than t…
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a collection of data, generally represented in tabular form, with columns signifying different variables and rows signify different members of the set.
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data observed over time (either in continuous time or at discrete time periods).
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Statistical techniques used for predicting outcomes.
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Techniques for analyzing the relationship between one (or more) "dependent" variables and "independent" variables.
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a scientific approach to inductive inference and prediction based on probabilistic models of the data. By extension, it covers the design of experiments and surveys to gather data for th…
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Refers to a subset of data mining concerned with extracting information from data in the form of text by recognizing patterns. The goal of text mining is often to classify a given document into one of…
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a python library for Panel Data manipulation and analysis, e.g. multidimensional time series and cross-sectional data sets commonly found in statistics, experimental science results, econome…
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Methods and principles of selecting a subset of attributes for use in further modelling
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Convolutional Neural Networks (CNN, also called ConvNets) are a tool used for classification tasks and image recognition. The name giving first step is the extraction of features from the input data.…
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the term for a collection of data sets so large and complex that it becomes difficult to process using on-hand database management tools or traditional data processing applications. The ch…
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LSTM stands for Long Short-Term Memory. When we use this term most of the time we refer to a recurrent neural network or a block (part) of a bigger network.
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For questions regarding "Convolutional Neural Networks" (CNN)
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Constructing meaningful and useful graphical representations of data. (If your question is only about how to get particular software to produce a specific effect, then it is likely not on topic here.)…
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a machine-learning classifier based on choosing random subsets of variables for each tree and using the most frequent tree output as the overall classification.
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a class of artificial neural network where connections between units form a directed cycle.
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Questions mostly concerned with managing data, without focus on pre-processing or modelling.
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a preliminary step to statistical analysis in which the data-set is edited to correct errors and to put it into a form suitable for processing by statistical software.
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Techniques for analyzing the relationship between one (or more) "dependent" variables and "independent" variables.
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a popular supervised machine learning algorithm that can be used for classification or regression.
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Refers generally to statistical procedures that utilize the logistic function, most commonly various forms of logistic regression
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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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Area of machine learning concerned with how software agents ought to take actions in an environment so as to maximize some notion of cumulative reward.