2 dépôts
Awesome GitHub RepositoriesNonparametric Models
Predictive models that do not assume a fixed functional form for the underlying data distribution.
Distinct from Tabular Predictive Models: Distinct from Tabular Predictive Models: focuses on nonparametric methods like kernel regression rather than general tabular modeling.
Explore 2 awesome GitHub repositories matching data & databases · Nonparametric Models. Refine with filters or upvote what's useful.
Awesome Nonparametric Models GitHub Repositories
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- ddbourgin/numpy-ml
ddbourgin/numpy-ml
16,275Voir sur GitHubThis library is a collection of machine learning algorithms and neural network components implemented from scratch using only NumPy. It serves as an educational toolkit for constructing and experimenting with machine learning architectures, emphasizing a modular approach where algorithms are organized into self-contained, object-oriented classes. The project distinguishes itself by relying exclusively on array-oriented programming to perform mathematical operations, ensuring that all computations are vectorized for performance. By utilizing a standardized interface for forward and backward pa
Implements flexible nonparametric predictive models like kernel regression and Gaussian processes.
Pythonattentionbayesian-inferencegaussian-mixture-models - rasbt/python-machine-learning-book
rasbt/python-machine-learning-book
12,614Voir sur GitHubThis project is an educational resource providing practical code examples and implementations of machine learning algorithms using the Python language. It serves as a guide for constructing predictive pipelines, clustering models, and dimensionality reduction within the Scikit-Learn ecosystem. The repository includes comprehensive demonstrations for supervised and unsupervised learning, as well as detailed examples for implementing neural networks and deep architectures. It also provides practical guidance on exporting model parameters to JSON and wrapping trained models in web APIs for produ
Builds nonparametric models where complexity grows dynamically with the size of the training dataset.
Jupyter Notebook
