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Unlike regular hard keywords, soft keywords are reserved words only in the limited contexts where interpreting them as keywords would make syntactic sense. These words can be used as identifiers elsewhere, in other words, match and case are valid names for functions and variables. [6] [7] _ [note 4] case [note 4] match [note 4] Notes
NumPy (pronounced / ˈ n ʌ m p aɪ / NUM-py) is a library for the Python programming language, adding support for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays. [3]
NumPy, a BSD-licensed library that adds support for the manipulation of large, multi-dimensional arrays and matrices; it also includes a large collection of high-level mathematical functions. NumPy serves as the backbone for a number of other numerical libraries, notably SciPy. De facto standard for matrix/tensor operations in Python.
The eval() vs. exec() built-in functions (in Python 2, exec is a statement); the former is for expressions, the latter is for statements; Statements cannot be a part of an expression—so list and other comprehensions or lambda expressions, all being expressions, cannot contain statements.
fun or function declarations state functions and their types, these must be implemented by concrete modules (see below). one or more concrete modules, containing judgement forms lincat and lin . lincat or linearization type definitions , says what type of objects linearization produces for each category listed in cat .
Goldberg and Levy point out that the word2vec objective function causes words that occur in similar contexts to have similar embeddings (as measured by cosine similarity) and note that this is in line with J. R. Firth's distributional hypothesis. However, they note that this explanation is "very hand-wavy" and argue that a more formal ...
The phrase grammar of most programming languages can be specified using a Type-2 grammar, i.e., they are context-free grammars, [8] though the overall syntax is context-sensitive (due to variable declarations and nested scopes), hence Type-1. However, there are exceptions, and for some languages the phrase grammar is Type-0 (Turing-complete).
In natural language processing, a word embedding is a representation of a word. The embedding is used in text analysis.Typically, the representation is a real-valued vector that encodes the meaning of the word in such a way that the words that are closer in the vector space are expected to be similar in meaning. [1]