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scikit-learn (formerly scikits.learn and also known as sklearn) is a free and open-source machine learning library for the Python programming language. [3] It features various classification, regression and clustering algorithms including support-vector machines, random forests, gradient boosting, k-means and DBSCAN, and is designed to interoperate with the Python numerical and scientific ...
A 3.1 TB dataset consisting of permissively licensed source code in 30 programming languages. Filtered through license detection and deduplication. 6 TB, 51.76B files (prior to deduplication); 3 TB, 5.28B files (after). 358 programming languages. Parquet Language modeling, autocompletion, program synthesis. 2022 [402] [403]
Keras is an open-source library that provides a Python interface for artificial neural networks. Keras was first independent software, then integrated into the TensorFlow library, and later supporting more. "Keras 3 is a full rewrite of Keras [and can be used] as a low-level cross-framework language to develop custom components such as layers ...
Linnaeus 5 dataset Images of 5 classes of objects. Classes labelled, training set splits created. 8000 Images Classification 2017 [40] Chaladze & Kalatozishvili 11K Hands 11,076 hand images (1600 x 1200 pixels) of 190 subjects, of varying ages between 18 – 75 years old, for gender recognition and biometric identification. None 11,076 hand images
DBSCAN optimizes the following loss function: [10] For any possible clustering = {, …,} out of the set of all clusterings , it minimizes the number of clusters under the condition that every pair of points in a cluster is density-reachable, which corresponds to the original two properties "maximality" and "connectivity" of a cluster: [1]
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]
The basic search algorithm is to propose a candidate model, evaluate it against a dataset, and use the results as feedback to teach the NAS network. [164] Available systems include AutoML and AutoKeras. [165] scikit-learn library provides functions to help with building a deep network
The scikit-learn project started as scikits.learn, a Google Summer of Code project by David Cournapeau. After having worked for Silveregg, a SaaS Japanese company delivering recommendation systems for Japanese online retailers, [3] he worked for 6 years at Enthought, a scientific consulting company.