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The enclosed text becomes a string literal, which Python usually ignores (except when it is the first statement in the body of a module, class or function; see docstring). Elixir. The above trick used in Python also works in Elixir, but the compiler will throw a warning if it spots this.
1975-2013, R 0 RS, R 1 RS, R 2 RS, R 3 RS, R 4 RS, R 5 RS, R 6 RS, R 7 RS Small Edition [42] [43] Seed7: Application, general, scripting, web Yes Yes No No Yes Yes Multi-paradigm, extensible, structured No Simula: Education, general Yes Yes No No No No discrete event simulation, multi-threaded (quasi-parallel) program execution Yes 1968 Small Basic
There are no differences in scoping between ANSI CL interpreters and compilers. Common Lisp is sometimes termed a Lisp-2 and Scheme a Lisp-1, referring to CL's use of separate namespaces for functions and variables. (In fact, CL has many namespaces, such as those for go tags, block names, and loop keywords). There is a long-standing controversy ...
Extreme learning machines are feedforward neural networks for classification, regression, clustering, sparse approximation, compression and feature learning with a single layer or multiple layers of hidden nodes, where the parameters of hidden nodes (not just the weights connecting inputs to hidden nodes) need to be tuned.
CLPython is an implementation of the Python programming language written in Common Lisp. This project allow to call Lisp functions from Python and Python functions from Lisp. Licensed under LGPL. CLPython was started in 2006, but as of 2013, it was not actively developed and the mailing list was closed. [1]
For most systems the expectation function {() ()} must be approximated. This can be done with the following unbiased estimator ^ {() ()} = = () where indicates the number of samples we use for that estimate.
In computer science, online machine learning is a method of machine learning in which data becomes available in a sequential order and is used to update the best predictor for future data at each step, as opposed to batch learning techniques which generate the best predictor by learning on the entire training data set at once.
JAX is a machine learning framework for transforming numerical functions. [2] [3] [4] It is described as bringing together a modified version of autograd (automatic obtaining of the gradient function through differentiation of a function) and OpenXLA's XLA (Accelerated Linear Algebra).