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Pandas (styled as pandas) is a software library written for the Python programming language for data manipulation and analysis. In particular, it offers data structures and operations for manipulating numerical tables and time series .
Profiling results can be used to guide the design and optimization of an individual algorithm; the Krauss matching wildcards algorithm is an example. [5] Profilers are built into some application performance management systems that aggregate profiling data to provide insight into transaction workloads in distributed applications. [6]
In computer programming, profile-guided optimization (PGO, sometimes pronounced as pogo [1]), also known as profile-directed feedback (PDF) [2] or feedback-directed optimization (FDO), [3] is the compiler optimization technique of using prior analyses of software artifacts or behaviors ("profiling") to improve the expected runtime performance of the program.
Profiling technologies make possible a far-reaching monitoring of an individual's behaviour and preferences. Profiles may reveal personal or private information about individuals that they might not even be aware of themselves (Hildebrandt & Gutwirth 2008). Profiling technologies are by their very nature discriminatory tools.
Data profiling is the process of examining the data available from an existing information source (e.g. a database or a file) and collecting statistics or informative summaries about that data. [1] The purpose of these statistics may be to: Find out whether existing data can be easily used for other purposes
Social profiling is the process of constructing a social media user's profile using his or her social data. In general, profiling refers to the data science process of generating a person's profile with computerized algorithms and technology. [ 1 ]
A partial human STR profile obtained using the Applied Biosystems Identifiler kit Short tandem repeat ( STR ) analysis is a common molecular biology method used to compare allele repeats at specific loci in DNA between two or more samples.
It is divided into modular objects that share a common Module interface. Modules have a forward() and backward() method that allow them to feedforward and backpropagate, respectively. Modules can be joined using module composites, like Sequential, Parallel and Concat to create complex task-tailored graphs.