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An adaptive neuro-fuzzy inference system or adaptive network-based fuzzy inference system (ANFIS) is a kind of artificial neural network that is based on Takagi–Sugeno fuzzy inference system.
The book includes examples in C++ and Smalltalk. It has been influential to the field of software engineering and is regarded as an important source for object-oriented design theory and practice. More than 500,000 copies have been sold in English and in 13 other languages. [1] The authors are often referred to as the Gang of Four (GoF). [2] [3 ...
Kaggle is a data science competition platform and online community for data scientists and machine learning practitioners under Google LLC.Kaggle enables users to find and publish datasets, explore and build models in a web-based data science environment, work with other data scientists and machine learning engineers, and enter competitions to solve data science challenges.
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Modern C++ Design: Generic Programming and Design Patterns Applied is a book written by Andrei Alexandrescu, published in 2001 by Addison-Wesley. It has been regarded as "one of the most important C++ books" by Scott Meyers. [1] The book makes use of and explores a C++ programming technique called template metaprogramming. While Alexandrescu ...
Computer programming or coding is the composition of sequences of instructions, called programs, that computers can follow to perform tasks. [1] [2] It involves designing and implementing algorithms, step-by-step specifications of procedures, by writing code in one or more programming languages.
This is an accepted version of this page This is the latest accepted revision, reviewed on 1 February 2025. General-purpose programming language "C programming language" redirects here. For the book, see The C Programming Language. Not to be confused with C++ or C#. C Logotype used on the cover of the first edition of The C Programming Language Paradigm Multi-paradigm: imperative (procedural ...
In computer science, locality-sensitive hashing (LSH) is a fuzzy hashing technique that hashes similar input items into the same "buckets" with high probability. [1] ( The number of buckets is much smaller than the universe of possible input items.) [1] Since similar items end up in the same buckets, this technique can be used for data clustering and nearest neighbor search.