wepe/MachineLearning
MachineLearning
Dieses Projekt ist eine Machine-Learning-Bibliothek, die eine Sammlung von Implementierungen für überwachte und unüberwachte Lernalgorithmen bereitstellt. Sie dient als Deep-Learning-Framework, als Sammlung statistischer Klassifikatoren und als Suite von Werkzeugen für unüberwachtes Lernen und Dimensionsreduktion.
Die Bibliothek ermöglicht die Konstruktion neuronaler Netze, einschließlich Multi-Layer-Perzeptrons und Convolutional Networks für die Mustererkennung. Sie bietet zudem Werkzeuge zur Durchführung von Hauptkomponentenanalysen (PCA) und Manifold-Learning zur Visualisierung hochdimensionaler Datensätze sowie eine Suite von Clustering-Algorithmen, die unbeschriftete Daten durch iterative Partitionierung gruppieren.
Das Projekt deckt ein breites Spektrum an prädiktiven Modellierungsfunktionen ab, einschließlich Klassifizierungs- und Regressionsaufgaben unter Verwendung von Entscheidungsbäumen, k-nächste-Nachbarn, Bayes-Klassifikatoren, Support Vector Machines und Ridge-Regression. Es enthält zudem Werkzeuge für Bildklassifizierungs-Workflows und die Analyse unbeschrifteter Daten.
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.
