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Awesome GitHub RepositoriesRegression Implementations

Implementations of regression methods including linear basis function models and Gaussian processes from the PRML textbook.

Distinct from Linear Regression Implementations: Distinct from Linear Regression Implementations: covers a broader range of regression methods including Gaussian processes, not just linear regression.

Explore 2 awesome GitHub repositories matching artificial intelligence & ml · Regression Implementations. Refine with filters or upvote what's useful.

Awesome Regression Implementations GitHub Repositories

AI के साथ बेहतरीन रिपॉजिटरी खोजें।हम AI का उपयोग करके सबसे सटीक रिपॉजिटरी खोजेंगे।
  • prml/prmltPRML का अवतार

    PRML/PRMLT

    6,207GitHub पर देखें↗

    PRMLT provides self-contained MATLAB implementations of every algorithm from the Pattern Recognition and Machine Learning textbook by Christopher Bishop. The code reproduces the book's exact formulas and notation, making each implementation directly traceable to the source material for educational verification and study. The implementations cover the full range of core machine learning methods from the textbook, including classification, clustering, regression, density estimation, and neural network algorithms. Each module is self-contained with heavy comments, and the code uses compact, vect

    Implements regression methods including linear basis function models and Gaussian processes from the PRML textbook.

    MATLAB
    GitHub पर देखें↗6,207
  • binroot/tensorflow-bookBinRoot का अवतार

    BinRoot/TensorFlow-Book

    4,431GitHub पर देखें↗

    This project is a collection of TensorFlow machine learning examples providing reference implementations for various neural network paradigms. It covers supervised, unsupervised, reinforcement, and sequential learning models. The repository includes implementations for convolutional neural networks focused on image classification and ranking, as well as recurrent neural networks for time-series forecasting and sequence-to-sequence translation. It further provides examples of reinforcement learning agents trained via reward optimization and unsupervised learning techniques such as autoencoders

    Implements a range of regression methods to predict continuous values by fitting linear and non-linear relationships.

    Jupyter Notebookautoencoderbookclassification
    GitHub पर देखें↗4,431
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