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  2. Training, validation, and test data sets - Wikipedia

    en.wikipedia.org/wiki/Training,_validation,_and...

    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]

  3. File:Non-Programmer's Tutorial for Python 3.pdf - Wikipedia

    en.wikipedia.org/wiki/File:Non-Programmer's...

    This file contains additional information, probably added from the digital camera or scanner used to create or digitize it. If the file has been modified from its original state, some details may not fully reflect the modified file.

  4. List of datasets for machine-learning research - Wikipedia

    en.wikipedia.org/wiki/List_of_datasets_for...

    A dataset for NLP and climate change media researchers The dataset is made up of a number of data artifacts (JSON, JSONL & CSV text files & SQLite database) Climate news DB, Project's GitHub repository [394] ADGEfficiency Climatext Climatext is a dataset for sentence-based climate change topic detection. HF dataset [395] University of Zurich ...

  5. List of datasets in computer vision and image processing

    en.wikipedia.org/wiki/List_of_datasets_in...

    THz and thermal video data set This multispectral data set includes terahertz, thermal, visual, near infrared, and three-dimensional videos of objects hidden under people's clothes. 3D lookup tables are provided that allow you to project images onto 3D point clouds. More than 20 videos.

  6. Online machine learning - Wikipedia

    en.wikipedia.org/wiki/Online_machine_learning

    Online learning is a common technique used in areas of machine learning where it is computationally infeasible to train over the entire dataset, requiring the need of out-of-core algorithms. It is also used in situations where it is necessary for the algorithm to dynamically adapt to new patterns in the data, or when the data itself is ...

  7. Feature hashing - Wikipedia

    en.wikipedia.org/wiki/Feature_hashing

    In a typical document classification task, the input to the machine learning algorithm (both during learning and classification) is free text. From this, a bag of words (BOW) representation is constructed: the individual tokens are extracted and counted, and each distinct token in the training set defines a feature (independent variable) of each of the documents in both the training and test sets.

  8. Pattern recognition - Wikipedia

    en.wikipedia.org/wiki/Pattern_recognition

    Pattern recognition is the task of assigning a class to an observation based on patterns extracted from data. While similar, pattern recognition (PR) is not to be confused with pattern machines (PM) which may possess (PR) capabilities but their primary function is to distinguish and create emergent patterns.

  9. Torch (machine learning) - Wikipedia

    en.wikipedia.org/wiki/Torch_(machine_learning)

    The torch package also simplifies object-oriented programming and serialization by providing various convenience functions which are used throughout its packages. The torch.class(classname, parentclass) function can be used to create object factories ().