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A training data set is a data set of examples used during the learning process and is used to fit the parameters (e.g., weights) of, for example, a classifier. [9] [10]For classification tasks, a supervised learning algorithm looks at the training data set to determine, or learn, the optimal combinations of variables that will generate a good predictive model. [11]
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]
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.
The Common Language Infrastructure (CLI) is an open specification and technical standard originally developed by Microsoft and standardized by ISO/IEC (ISO/IEC 23271) and Ecma International (ECMA 335) [1] [2] that describes executable code and a runtime environment that allows multiple high-level languages to be used on different computer platforms without being rewritten for specific ...
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.
Multi-task learning (MTL) is a subfield of machine learning in which multiple learning tasks are solved at the same time, while exploiting commonalities and differences across tasks. This can result in improved learning efficiency and prediction accuracy for the task-specific models, when compared to training the models separately.
The Stanford Institute for Human-Centered Artificial Intelligence's (HAI) Center for Research on Foundation Models (CRFM) coined the term "foundation model" in August 2021 [16] to mean "any model that is trained on broad data (generally using self-supervision at scale) that can be adapted (e.g., fine-tuned) to a wide range of downstream tasks". [17]
IronPython 2.6.1 versions is binary compatible only with .NET Framework 4.0. IronPython 2.6.1 must be compiled from sources to run on .NET Framework 3.5. IronPython 2.6.2, released on October 21, 2010, is binary compatible with both .NET Framework 4.0 and .NET Framework 3.5. Release 2.7 was released on March 12, 2011 and it targets CPython 2.7 ...