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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. Learning curve (machine learning) - Wikipedia

    en.wikipedia.org/wiki/Learning_curve_(machine...

    In machine learning (ML), a learning curve (or training curve) is a graphical representation that shows how a model's performance on a training set (and usually a validation set) changes with the number of training iterations (epochs) or the amount of training data. [1]

  4. Generalization error - Wikipedia

    en.wikipedia.org/wiki/Generalization_error

    A seventh order polynomial function was fit to the training data. In the right column, the function is tested on data sampled from the underlying joint probability distribution of x and y. In the top row, the function is fit on a sample dataset of 10 datapoints. In the bottom row, the function is fit on a sample dataset of 100 datapoints.

  5. Keras - Wikipedia

    en.wikipedia.org/wiki/Keras

    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.

  6. Data validation - Wikipedia

    en.wikipedia.org/wiki/Data_validation

    Data type validation is customarily carried out on one or more simple data fields. The simplest kind of data type validation verifies that the individual characters provided through user input are consistent with the expected characters of one or more known primitive data types as defined in a programming language or data storage and retrieval ...

  7. Early stopping - Wikipedia

    en.wikipedia.org/wiki/Early_stopping

    Regularization, in the context of machine learning, refers to the process of modifying a learning algorithm so as to prevent overfitting. This generally involves imposing some sort of smoothness constraint on the learned model. [1]

  8. Could AMD Be the Nvidia of 2025?

    www.aol.com/could-amd-nvidia-2025-210500400.html

    In the table, I've broken down the annual revenue growth rates for each of AMD's and Nvidia's respective data center businesses: Category. Q3 2023. Q4 2023. Q1 2024. Q2 2024. Q3 2024.

  9. Statistical model validation - Wikipedia

    en.wikipedia.org/wiki/Statistical_model_validation

    One example of this method is in Figure 1, which shows a polynomial function fit to some data. We see that the polynomial function does not conform well to the data, which appears linear, and might invalidate this polynomial model. Commonly, statistical models on existing data are validated using a validation set, which may also be referred to ...