wepe/MachineLearning
MachineLearning
This project is a machine learning library providing a collection of implementations for supervised and unsupervised learning algorithms. It serves as a deep learning framework, a statistical classifier collection, and a suite of tools for unsupervised learning and dimensionality reduction.
The library enables the construction of neural networks, including multi-layer perceptrons and convolutional networks for pattern recognition. It also provides tools for performing principal component analysis and manifold learning to visualize high-dimensional datasets, alongside a suite of clustering algorithms that group unlabeled data through iterative partitioning.
The project covers a broad range of predictive modeling capabilities, including classification and regression tasks using decision trees, k-nearest neighbors, Bayes classifiers, support vector machines, and ridge regression. It also includes tools for image classification workflows and the analysis of unlabeled data.
Features
- General Deep Learning Frameworks - Serves as a deep learning framework for building multi-layer perceptrons and convolutional networks.
- Supervised Learning - Provides a comprehensive collection of supervised learning algorithms for classification and regression tasks.
- Bayesian Inference - Predicts class membership using probabilistic Bayesian inference and conditional probability theorems.
- Centroid-Based Clustering - Groups unlabeled data by iteratively calculating and updating cluster centers.
- Clustering Suites - Offers a collection of centroid and density-based clustering implementations for unlabeled data.
- Convolutional Neural Networks - Implements convolutional processing to extract spatial features from images for pattern recognition.
- Decision Trees - Builds classification trees using iterative feature splitting to make data predictions.
- Deep Learning Architectures - Provides frameworks for constructing multi-layered neural networks to identify complex patterns.
- Deep Learning Development - Provides tools for the design, construction, and training of multi-layered artificial neural networks.
- Clustering Algorithms - Provides clustering algorithms including KMeans and Gaussian Mixture Models for grouping unlabeled data.
- K-Nearest Neighbor Classifiers - Provides k-nearest neighbor classifiers that assign classes based on proximity to training samples.
- Logistic Regression Models - Implements logistic regression models to predict binary outcomes using the sigmoid function.
- Machine Learning Libraries - Provides a comprehensive collection of supervised and unsupervised machine learning algorithmic implementations.
- Multi-Layer Architectures - Constructs deep learning models using multiple hidden layers to extract hierarchical patterns.
- Multilayer Perceptrons - Implements multi-layer perceptrons with fully connected layers for learning non-linear mappings.
- Naive Bayes Classifiers - Implements naive bayes classifiers based on statistical feature distributions and conditional probability.
- Statistical Classifier Collections - Implements a diverse set of statistical classifiers including Bayes, support vector machines, and k-nearest neighbors.
- Dimensionality Reduction - Provides techniques for projecting high-dimensional data into lower-dimensional spaces to improve model efficiency.
- Support Vector Machines - Implements support vector machines to find optimal decision boundaries through boundary-based vector optimization.
- Unsupervised Learning - Provides algorithms for discovering patterns and structures in unlabeled datasets through clustering.
- Principal Component Analysis - Implements principal component analysis to isolate primary patterns by reducing dataset dimensionality.
- Image Classification - Implements convolutional neural networks to categorize images based on visual content.
- Manifold Visualizations - Projects high-dimensional data into low-dimensional space via manifold learning for visual analysis.
- Manifold Learning - Implements manifold learning to visualize complex, high-dimensional datasets in lower dimensions.
- Dimensionality Reduction - Implements principal component analysis and other dimensionality reduction techniques to simplify complex datasets.
- Ridge Regression - Implements ridge regression with L2 regularization to prevent overfitting in linear models.
Star history
How this analysis was created: This summary and feature list were written by an AI model that read the project's README and public documentation pages. Each feature links to the documentation it came from; stars, license and language come straight from the GitHub API. The model does not read the source code, and the analysis is refreshed when the project is re-analysed. Learn more on our About page.
