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English: PDF version of the Think Python Wikibook. This file was created with MediaWiki to LaTeX . The LaTeX source code is attached to the PDF file (see imprint).
Python is a high-level, general-purpose programming language. Its design philosophy emphasizes code readability with the use of significant indentation. [33] Python is dynamically type-checked and garbage-collected. It supports multiple programming paradigms, including structured (particularly procedural), object-oriented and functional ...
Data mining is a particular data analysis technique that focuses on statistical modeling and knowledge discovery for predictive rather than purely descriptive purposes, while business intelligence covers data analysis that relies heavily on aggregation, focusing mainly on business information. [4]
Natural language processing (NLP) is a subfield of computer science and especially artificial intelligence.It is primarily concerned with providing computers with the ability to process data encoded in natural language and is thus closely related to information retrieval, knowledge representation and computational linguistics, a subfield of linguistics.
Machine learning and data mining often employ the same methods and overlap significantly, but while machine learning focuses on prediction, based on known properties learned from the training data, data mining focuses on the discovery of (previously) unknown properties in the data (this is the analysis step of knowledge discovery in databases).
An extension for Ansys Mechanical, Femap and Simcenter with out of the box predefined standards on fatigue, stiffener and plate buckling, beam member checks, joint checks and weld. Such as AISC 360-10, API 2A RP, ISO 19902, Norsok N004, DIN15018, Eurocode 3, FEM 1.001, ABS 2004, ABS 2014, DNV RP-C201 2010, DNV CN30/1995, FKM etc.
IDEF0 Diagram Example. IDEF0, a compound acronym ("Icam DEFinition for Function Modeling", where ICAM is an acronym for "Integrated Computer Aided Manufacturing"), is a function modeling methodology for describing manufacturing functions, which offers a functional modeling language for the analysis, development, reengineering and integration of information systems, business processes or ...
Neural networks are typically trained through empirical risk minimization.This method is based on the idea of optimizing the network's parameters to minimize the difference, or empirical risk, between the predicted output and the actual target values in a given dataset. [4]