2 repositorios
Classifiers that use kernel functions to map data into higher dimensions for non-linear separation.
Distinct from Supervised Classification: Focuses on kernel tricks for non-linear boundaries specifically, rather than general supervised classification workflows.
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This 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
Implements non-linear classification using kernel tricks to find separating hyperplanes for non-linearly separable data.
TensorFlow-World is a collection of tutorials, implementation guides, and model templates for building and training machine learning models using the TensorFlow framework. It serves as an educational resource for designing deep learning architectures and implementing predictive models. The project provides ready-to-use examples for constructing neural network architectures and linear classifiers. It includes guides on performing tensor operations, automatic differentiation, and gradient descent optimization. The materials cover a range of machine learning capabilities, including the use of h
Develops classifiers that use kernel functions to map data into higher dimensions for non-linear separation.