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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]
Shannon's diagram of a general communications system, showing the process by which a message sent becomes the message received (possibly corrupted by noise). seq2seq is an approach to machine translation (or more generally, sequence transduction) with roots in information theory, where communication is understood as an encode-transmit-decode process, and machine translation can be studied as a ...
Principles of Model Checking is a textbook on model checking, an area of computer science that automates the problem of determining if a machine meets specification requirements. It was written by Christel Baier and Joost-Pieter Katoen , and published in 2008 by MIT Press .
Prefix sums are trivial to compute in sequential models of computation, by using the formula y i = y i − 1 + x i to compute each output value in sequence order. However, despite their ease of computation, prefix sums are a useful primitive in certain algorithms such as counting sort, [1] [2] and they form the basis of the scan higher-order function in functional programming languages.
One measure of goodness of fit is the coefficient of determination, often denoted, R 2. In ordinary least squares with an intercept, it ranges between 0 and 1. However, an R 2 close to 1 does not guarantee that the model fits the data well. For example, if the functional form of the model does not match the data, R 2 can be high despite a poor ...
PyTorch Lightning is an open-source Python library that provides a high-level interface for PyTorch, a popular deep learning framework. [1] It is a lightweight and high-performance framework that organizes PyTorch code to decouple research from engineering, thus making deep learning experiments easier to read and reproduce.
OpenML: [493] Web platform with Python, R, Java, and other APIs for downloading hundreds of machine learning datasets, evaluating algorithms on datasets, and benchmarking algorithm performance against dozens of other algorithms.
In addition to model-checking, SPIN can also operate as a simulator, following one possible execution path through the system and presenting the resulting execution trace to the user. Unlike many model-checkers, SPIN does not actually perform model-checking itself, but instead generates C sources for a problem-specific model checker.